==21311== Memcheck, a memory error detector
==21311== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al.
==21311== Using Valgrind-3.15.0 and LibVEX; rerun with -h for copyright info
==21311== Command: /data/blackswan/ripley/R/R-devel-vg/bin/exec/R --vanilla
==21311== 

R Under development (unstable) (2019-12-03 r77513) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> pkgname <- "MEET"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('MEET')
Loading required package: seqinr
Loading required package: pcaMethods
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: ‘pcaMethods’

The following object is masked from ‘package:stats’:

    loadings

Loading required package: Matrix
Loading required package: ROCR
Loading required package: gplots

Attaching package: ‘gplots’

The following object is masked from ‘package:stats’:

    lowess

Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: ‘Hmisc’

The following object is masked from ‘package:Biobase’:

    contents

The following object is masked from ‘package:seqinr’:

    translate

The following objects are masked from ‘package:base’:

    format.pval, units

Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
Loading required package: seqLogo
Loading required package: grid
> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("Alignment")
> ### * Alignment
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Alignment
> ### Title: To line up Transcription Factor Binding sites through Multiple
> ###   Sequence Alignment (MSA)
> ### Aliases: Alignment
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> pathMEET <- system.file("sequences", package = "MEET")
> Alignment(TF=paste(pathMEET, "AP1.fa", sep = "/"),iicc)
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 
> 
> 
> 
> cleanEx()
> nameEx("CalculInformation")
> ### * CalculInformation
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: CalculInformation
> ### Title: Information content in each position of a set of aligned DNA
> ###   sequences
> ### Aliases: CalculInformation
> 
> ### ** Examples
> 
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> CalculInformation(matriu=TranscriptionFactor, Prob=Prob)
            [,1]       [,2]       [,3]       [,4]        [,5]
 [1,] 0.61964852 0.02950707 0.85939349 1.50486070  0.35736343
 [2,] 0.86344137 0.04111626 0.42658115 2.78219995  1.18323446
 [3,] 0.37935306 0.13096713 0.52612656 0.82092805  0.11842238
 [4,] 1.17879638 3.38903959 0.23154929 0.05613316  0.05613316
 [5,] 1.40244795 0.06678324 4.24073547 0.06678324  0.06678324
 [6,] 1.40244795 0.06678324 4.24073547 0.06678324  0.06678324
 [7,] 2.15060117 0.10240958 0.10240958 0.10240958 11.48125177
 [8,] 1.40244795 0.06678324 4.24073547 0.06678324  0.06678324
 [9,] 1.00655058 0.04793098 2.74404860 0.31421420  0.31421420
[10,] 0.41427646 0.14302401 0.38961714 1.11569691  0.23892134
[11,] 0.02781791 0.02202251 0.03444122 0.02340237  0.03076160
[12,] 0.10432910 0.06706871 0.08259387 0.14296951  0.17056980
> 
> 
> cleanEx()
> nameEx("CalculPSSM")
> ### * CalculPSSM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: CalculPSSM
> ### Title: Position Specific Scoring Matrices from a set of aligned
> ###   sequences
> ### Aliases: CalculPSSM
> 
> ### ** Examples
> 
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> CalculPSSM(matriu=TranscriptionFactor, Prob=Prob)
            [,1]        [,2]       [,3]       [,4]
 [1,] -4.3923174  0.47186872  1.2801079 -0.7940581
 [2,] -4.3923174 -1.01727799  1.6880560  0.4545659
 [3,] -1.5343364  0.47186872  1.1137146 -1.6795994
 [4,]  1.5235620 -2.34792330 -4.3923174 -4.3923174
 [5,] -4.3923174  1.59636726 -4.3923174 -4.3923174
 [6,] -4.3923174  1.59636726 -4.3923174 -4.3923174
 [7,] -4.3923174 -4.39231742 -4.3923174  2.4164680
 [8,] -4.3923174  1.59636726 -4.3923174 -4.3923174
 [9,] -4.3923174  1.44688637 -1.6795994 -1.6795994
[10,] -1.5343364 -0.08853667  1.4292794 -0.7940581
[11,] -0.3370350  0.30812230 -0.2493595  0.1451167
[12,] -0.6374299 -0.33703499  0.4545659  0.7092206
> 
> 
> 
> cleanEx()
> nameEx("CalculPWM")
> ### * CalculPWM
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: CalculPWM
> ### Title: CalculPWM: To calculate Position Weight Matrix
> ### Aliases: CalculPWM
> 
> ### ** Examples
> 
> data(TranscriptionFactor)
> matriu<-TranscriptionFactor
> resul<-CalculPWM(matriu)
      [,1] [,2] [,3] [,4]
 [1,]    0    9    2    9
 [2,]    0   12    5    3
 [3,]    2    8    1    9
 [4,]   19    0    0    1
 [5,]    0    0    0   20
 [6,]    0    0    0   20
 [7,]    0    0   20    0
 [8,]    0    0    0   20
 [9,]    0    1    1   18
[10,]    2   10    2    6
[11,]    5    3    4    8
[12,]    4    5    6    5
> 
> 
> 
> cleanEx()
> nameEx("CalculScores")
> ### * CalculScores
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: CalculScores
> ### Title: Calcul Score of a Sequence, using a loggods matrix
> ### Aliases: CalculScores
> 
> ### ** Examples
> 
> require("MEET")
> #First example, calculating logodds of a Sequence 
> data(TFlogodds)
> data(Sequence)
> CalculScores(sequencia=Sequence, logodds=TFlogodds)
[1] 39.37294
> #given a Transfac matrix, calculate first loggods and then scores
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> data(Sequence)
> Factortrans<-TranscriptionFactor
> suma<-apply(Factortrans,2,function(y){sum(y=="-")})
> Factortrans<-Factortrans[, suma==0]
> logodds<-CalculInformation(matriu=Factortrans, Prob=Prob)
> logodds <- logodds[,2:dim(logodds)[2]]
> CalculScores(sequencia=Sequence,logodds=logodds)
[1] 35.97108
>   
> 
> 
> 
> cleanEx()
> nameEx("CalculSimilarity")
> ### * CalculSimilarity
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: CalculSimilarity
> ### Title: Similarity Score between a Sequence and a PSSM model
> ### Aliases: CalculSimilarity
> 
> ### ** Examples
> 
> require("MEET")
> data(TFlogodds)
> data(BackgroundOrganism)
> data(Sequence)
> current<-CalculScores(Sequence, TFlogodds)
> 
> maxim<-0
> minim<-0
> 
> for(j in 1:dim(TFlogodds)[1]){
+ 	  minim <- min(TFlogodds[j,])+minim
+ 	  maxim <- max(TFlogodds[j,])+maxim
+ 	  }
> Similarity<-CalculSimilarity(current, minim, maxim)
>   
> 
> 
> cleanEx()
> nameEx("CreateConsensus")
> ### * CreateConsensus
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: CreateConsensus
> ### Title: Consensus Sequence for a DNA motif
> ### Aliases: CreateConsensus
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> data(iicc)
> pathMEET <- system.file("sequences", package = "MEET")
> SeqCons<-CreateConsensus(TranscriptionFactor,iicc,filein=NULL)
> 
> 
> 
> cleanEx()
> nameEx("DivergenceDROSOPHILA")
> ### * DivergenceDROSOPHILA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: DivergenceDROSOPHILA
> ### Title: DivergenceDROSOPHILA: Given a Transcription factor chooses the
> ###   model for a specific organism and method.
> ### Aliases: DivergenceDROSOPHILA
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> DivergenceDROSOPHILA(nameTF=iicc$nameTF)
[1] "Transcription Factor not included"
NULL
> 
> 
> cleanEx()
> nameEx("DivergenceHOMO")
> ### * DivergenceHOMO
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: DivergenceHOMO
> ### Title: DivergenceHOMO: Given a Transcription factor chooses the model
> ###   for a specific organism and method.
> ### Aliases: DivergenceHOMO
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> DivergenceHOMO(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
$model[[1]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4854608 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.2974722 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.5907239 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.5907239

$model[[2]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.0000000 0.3404667 0.2352035 0.3801976
[2,] 0.1922091 0.0000000 0.1922091 0.1922091
[3,] 0.2064929 0.3404667 0.3404667 0.4854608
[4,] 0.2064929 0.3404667 0.3404667 0.4854608

$model[[3]]
         [,1]      [,2]     [,3]      [,4]
[1,] 1.058277 0.0000000 0.000000 0.0000000
[2,] 0.000000 0.9530139 0.000000 0.0000000
[3,] 0.000000 0.0000000 0.913283 0.0000000
[4,] 0.000000 0.0000000 0.000000 0.7742433

$model[[4]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2952432 0.4292170 0.4292170 0.5742111
[2,] 0.1899801 0.3239539 0.4292170 0.4689480
[3,] 0.2555123 0.3894861 0.2842230 0.4292170
[4,] 0.2847491 0.2504465 0.2504465 0.2901774

$model[[5]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3764550 0.3506537 0.3506537 0.2670035
[2,] 0.2711919 0.2453905 0.3506537 0.1617403
[3,] 0.2314609 0.3109228 0.2056596 0.2272726
[4,] 0.2374154 0.2116140 0.2116140 0.2298095

$model[[6]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4660386 0.6110327 0.3269990 0.5057695
[2,] 0.3607755 0.5057695 0.2217358 0.5057695
[3,] 0.3210445 0.4660386 0.3269990 0.3607755
[4,] 0.3269990 0.3269990 0.3165048 0.2217358

$model[[7]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.4952754 0.4952754 0.3502813
[2,] 0.0000000 0.3900122 0.3900122 0.3502813
[3,] 0.2064929 0.3502813 0.3502813 0.2052872
[4,] 0.2014270 0.2112417 0.2112417 0.1715107

$model[[8]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.0000000 0.1922091 0.2064929 0.2064929
[2,] 0.3404667 0.0000000 0.3404667 0.3404667
[3,] 0.2352035 0.1922091 0.3404667 0.3404667
[4,] 0.3801976 0.1922091 0.4854608 0.4854608

$model[[9]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2952432 0.1899801 0.2555123 0.2847491
[2,] 0.4292170 0.3239539 0.3894861 0.2504465
[3,] 0.4292170 0.4292170 0.2842230 0.2504465
[4,] 0.5742111 0.4689480 0.4292170 0.2901774

$model[[10]]
          [,1]     [,2]     [,3]     [,4]
[1,] 0.9530139 0.000000 0.000000 0.000000
[2,] 0.0000000 1.086988 0.000000 0.000000
[3,] 0.0000000 0.000000 1.086988 0.000000
[4,] 0.0000000 0.000000 0.000000 1.231982

$model[[11]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2380766 0.2380766 0.2217358 0.0000000
[2,] 0.2270563 0.3720504 0.2330107 0.2667872
[3,] 0.2270563 0.3720504 0.1932798 0.3720504
[4,] 0.3720504 0.5170445 0.2330107 0.4117813

$model[[12]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3764550 0.2711919 0.2314609 0.2374154
[2,] 0.3506537 0.2453905 0.3109228 0.2116140
[3,] 0.3506537 0.3506537 0.2056596 0.2116140
[4,] 0.2670035 0.1617403 0.2272726 0.2298095

$model[[13]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.748773 0.000000 0.000000 0.000000
[2,] 0.000000 1.722971 0.000000 0.000000
[3,] 0.000000 0.000000 1.722971 0.000000
[4,] 0.000000 0.000000 0.000000 1.639321

$model[[14]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2314609 0.3764550 0.2374154 0.2711919
[2,] 0.3109228 0.3506537 0.2116140 0.2453905
[3,] 0.2056596 0.3506537 0.2116140 0.3506537
[4,] 0.2272726 0.2670035 0.2298095 0.1617403

$model[[15]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3404667 0.4842551 0.4842551 0.3392611
[2,] 0.3404667 0.4584538 0.4584538 0.3134598
[3,] 0.2749344 0.4584538 0.4584538 0.4584538
[4,] 0.3404667 0.3748036 0.3748036 0.2298095

$model[[16]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5907239 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.2974722 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.5907239 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.4854608

$model[[17]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.1966782 0.4854608 0.4854608 0.3404667
[2,] 0.1922091 0.1922091 0.1922091 0.0000000
[3,] 0.1966782 0.4854608 0.4854608 0.3404667
[4,] 0.0000000 0.3801976 0.3801976 0.3404667

$model[[18]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4660386 0.3607755 0.3210445 0.3269990
[2,] 0.6110327 0.5057695 0.4660386 0.3269990
[3,] 0.3269990 0.2217358 0.3269990 0.3165048
[4,] 0.5057695 0.5057695 0.3607755 0.2217358

$model[[19]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2380766 0.2270563 0.2270563 0.3720504
[2,] 0.2380766 0.3720504 0.3720504 0.5170445
[3,] 0.2217358 0.2330107 0.1932798 0.2330107
[4,] 0.0000000 0.2667872 0.3720504 0.4117813

$model[[20]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2314609 0.3109228 0.2056596 0.2272726
[2,] 0.3764550 0.3506537 0.3506537 0.2670035
[3,] 0.2374154 0.2116140 0.2116140 0.2298095
[4,] 0.2711919 0.2453905 0.3506537 0.1617403

$model[[21]]
         [,1]     [,2]      [,3]      [,4]
[1,] 0.913283 0.000000 0.0000000 0.0000000
[2,] 0.000000 1.058277 0.0000000 0.0000000
[3,] 0.000000 0.000000 0.7742433 0.0000000
[4,] 0.000000 0.000000 0.0000000 0.9530139

$model[[22]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.3502813 0.3502813 0.2052872
[2,] 0.2064929 0.4952754 0.4952754 0.3502813
[3,] 0.2014270 0.2112417 0.2112417 0.1715107
[4,] 0.0000000 0.3900122 0.3900122 0.3502813

$model[[23]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.0000000 0.2064929 0.2014270
[2,] 0.4952754 0.3900122 0.3502813 0.2112417
[3,] 0.4952754 0.3900122 0.3502813 0.2112417
[4,] 0.3502813 0.3502813 0.2052872 0.1715107

$model[[24]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3404667 0.3404667 0.2749344 0.3404667
[2,] 0.4842551 0.4584538 0.4584538 0.3748036
[3,] 0.4842551 0.4584538 0.4584538 0.3748036
[4,] 0.3392611 0.3134598 0.4584538 0.2298095

$model[[25]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.1966782 0.1922091 0.1966782 0.0000000
[2,] 0.4854608 0.1922091 0.4854608 0.3801976
[3,] 0.4854608 0.1922091 0.4854608 0.3801976
[4,] 0.3404667 0.0000000 0.3404667 0.3404667

$model[[26]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.2064929 0.2014270 0.0000000
[2,] 0.3502813 0.4952754 0.2112417 0.3900122
[3,] 0.3502813 0.4952754 0.2112417 0.3900122
[4,] 0.2052872 0.3502813 0.1715107 0.3502813

$model[[27]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4854608 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.7742433 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.7742433 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.6292492


$parameterModel
$parameterModel$Order
[1] 1

$parameterModel$HXmax
[1] 1.848238

$parameterModel$correction_1rOrdre
An object of class "correction"
Slot "Herror":
 [1] -3.203427e-16  7.419000e-01  1.097224e+00  1.306505e+00  1.442855e+00
 [6]  1.537475e+00  1.606148e+00  1.657735e+00  1.697580e+00  1.729079e+00
[11]  1.754477e+00  1.775312e+00  1.792661e+00  1.807302e+00  1.819803e+00
[16]  1.830589e+00  1.839985e+00  1.848238e+00

Slot "sderror":
 [1] 4.679240e-24 4.375893e-01 4.325258e-01 3.964260e-01 3.570110e-01
 [6] 3.204883e-01 2.882586e-01 2.603281e-01 2.362809e-01 2.156032e-01
[11] 1.977978e-01 1.824212e-01 1.690924e-01 1.574893e-01 1.473426e-01
[16] 1.384275e-01 1.305573e-01 1.235761e-01


$parameterModel$Entropy
[1] 0.3095434 0.8030717 0.9864267 1.6849774 0.3095434 0.8030717 0.5032583

$parameterModel$D
          [,1]      [,2]      [,3]     [,4]      [,5]      [,6]      [,7]
[1,] 0.3095434 0.0000000 0.1984323 0.000000 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.8030717 0.2921133 0.236420 0.0000000 0.3309805 0.2087922
[3,] 0.1984323 0.2921133 0.9864267 0.000000 0.0000000 0.2317707 0.0000000
[4,] 0.0000000 0.2364200 0.0000000 1.848238 0.0000000 0.2364200 0.3502090
[5,] 0.0000000 0.0000000 0.0000000 0.000000 0.3095434 0.0000000 0.1984323
[6,] 0.0000000 0.3309805 0.2317707 0.236420 0.0000000 0.8030717 0.2087922
[7,] 0.0000000 0.2087922 0.0000000 0.350209 0.1984323 0.2087922 0.5032583

$parameterModel$Mperfil
          [,1]      [,2]      [,3]       [,4]      [,5]      [,6]      [,7]
[1,] 0.8325197 0.6861373 0.6230522 0.27118105 0.8325197 0.6861373 0.7783523
[2,] 0.6861373 0.5654934 0.5135006 0.22349914 0.6861373 0.5654934 0.6414942
[3,] 0.6230522 0.5135006 0.4662881 0.20295009 0.6230522 0.5135006 0.5825137
[4,] 0.2711811 0.2234991 0.2029501 0.08833324 0.2711811 0.2234991 0.2535368
[5,] 0.8325197 0.6861373 0.6230522 0.27118105 0.8325197 0.6861373 0.7783523
[6,] 0.6861373 0.5654934 0.5135006 0.22349914 0.6861373 0.5654934 0.6414942
[7,] 0.7783523 0.6414942 0.5825137 0.25353680 0.7783523 0.6414942 0.7277092

$parameterModel$interA
 [1] 1 1 2 2 2 2 2 3 3 3 3 4 4 4 4 5 5 6 6 6 6 6 7 7 7 7 7

$parameterModel$interB
 [1] 1 3 2 3 4 6 7 1 2 3 6 2 4 6 7 5 7 2 3 4 6 7 2 4 5 6 7


$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 


> 
> 
> cleanEx()
> nameEx("DivergenceMUS")
> ### * DivergenceMUS
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: DivergenceMUS
> ### Title: DivergenceMUS: Given a Transcription factor chooses the model
> ###   for a specific organism and method.
> ### Aliases: DivergenceMUS
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> DivergenceMUS(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
$model[[1]]
         [,1]      [,2]     [,3]     [,4]
[1,] 4.662322 3.8803841 4.880384 4.880384
[2,] 4.683224 3.6868109 4.797922 4.797922
[3,] 4.549303 2.7035574 4.467738 4.751541
[4,] 3.711628 0.7086106 4.725165 3.730807

$model[[2]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5046303 0.5693557 0.7721472 0.7721472
[2,] 0.6271304 0.5798238 0.7113100 0.7113100
[3,] 0.6731506 0.4556132 0.7474086 0.7731531
[4,] 0.7086106 0.6830986 0.7925811 0.7828377

$model[[3]]
         [,1]      [,2]     [,3]     [,4]
[1,] 2.735552 1.9308882 2.909374 2.909374
[2,] 2.922154 2.4684314 2.926559 2.926559
[3,] 2.796565 1.7227733 2.769692 2.890993
[4,] 2.536047 0.8181496 2.812799 2.539251

$model[[4]]
          [,1]      [,2]      [,3]      [,4]
[1,]  6.382884 3.7515412 50.080478 49.776506
[2,] 50.090397 4.6752987 50.103269 50.080478
[3,]  4.722710 0.9277651  4.738392  7.967847
[4,]  8.967847 4.6400029 49.776506  7.967847

$model[[5]]
         [,1]      [,2]     [,3]     [,4]
[1,] 3.769273 0.6404286 3.967847 3.967847
[2,] 4.880384 3.6894864 4.967847 4.967847
[3,] 4.880384 4.8032775 4.840818 4.967847
[4,] 4.662322 3.1198287 4.967847 4.646901

$model[[6]]
         [,1]     [,2]     [,3]      [,4]
[1,] 4.662322 4.683224 4.549303 3.7116284
[2,] 3.880384 3.686811 2.703557 0.7086106
[3,] 4.880384 4.797922 4.467738 4.7251652
[4,] 4.880384 4.797922 4.751541 3.7308068

$model[[7]]
          [,1]      [,2]    [,3]      [,4]
[1,] 0.6673019 0.0000000 0.00000 0.0000000
[2,] 0.0000000 0.6517222 0.00000 0.0000000
[3,] 0.0000000 0.0000000 0.63747 0.0000000
[4,] 0.0000000 0.0000000 0.00000 0.4847732

$model[[8]]
         [,1]     [,2]     [,3]     [,4]
[1,] 4.277232 4.206246 3.574347 1.111872
[2,] 5.856736 4.794123 5.491253 3.409934
[3,] 5.875858 5.406183 4.634538 3.416292
[4,] 6.279989 5.875858 6.050860 4.138678

$model[[9]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5800637 0.5744203 0.4911833 0.3290148
[2,] 0.5929568 0.5255348 0.5013103 0.2540349
[3,] 0.5972401 0.5772243 0.4388481 0.4711720
[4,] 0.6023987 0.5865539 0.5089731 0.4827144

$model[[10]]
         [,1]     [,2]     [,3]      [,4]
[1,] 3.711628 3.731311 3.707301 3.1976071
[2,] 3.146657 3.021493 2.309625 0.5046303
[3,] 3.773173 3.731311 3.553109 3.1976071
[4,] 3.730807 3.689486 3.560650 3.0752925

$model[[11]]
         [,1]     [,2]     [,3]      [,4]
[1,] 3.567092 3.067713 3.451996 2.6735385
[2,] 3.789225 3.111613 3.704763 3.6620405
[3,] 3.204262 2.422980 3.060576 0.7318909
[4,] 3.789225 3.119181 3.703685 2.6965512

$model[[12]]
         [,1]     [,2]     [,3]     [,4]
[1,] 3.197607 2.784277 2.779815 0.707249
[2,] 4.060956 3.284380 4.060956 3.475994
[3,] 4.060956 3.373099 3.870880 3.475994
[4,] 3.967847 3.196148 3.738392 3.271773

$model[[13]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5046303 0.6271304 0.6731506 0.7086106
[2,] 0.5693557 0.5798238 0.4556132 0.6830986
[3,] 0.7721472 0.7113100 0.7474086 0.7925811
[4,] 0.7721472 0.7113100 0.7731531 0.7828377

$model[[14]]
         [,1]     [,2]     [,3]     [,4]
[1,] 4.277232 5.856736 5.875858 6.279989
[2,] 4.206246 4.794123 5.406183 5.875858
[3,] 3.574347 5.491253 4.634538 6.050860
[4,] 1.111872 3.409934 3.416292 4.138678

$model[[15]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.6517222 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.8091688 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.8091688 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.8424087

$model[[16]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.540986 2.975794 2.975794 3.039488
[2,] 1.841833 2.842433 2.717819 2.944131
[3,] 1.841833 2.717819 2.842433 2.944131
[4,] 0.980742 2.306864 2.306864 2.534556

$model[[17]]
          [,1]     [,2]     [,3]     [,4]
[1,] 3.0761941 3.686811 3.648742 3.769273
[2,] 0.6609383 3.036853 2.309524 3.184311
[3,] 3.6250178 3.686811 3.499832 3.769273
[4,] 3.0636046 3.579542 3.543911 3.656537

$model[[18]]
          [,1]     [,2]     [,3]     [,4]
[1,] 2.3493446 2.568187 2.696551 2.739191
[2,] 2.4381835 2.633772 2.742512 2.785193
[3,] 0.5233489 1.886005 2.329796 2.440044
[4,] 1.9245301 2.335921 2.336917 2.387948

$model[[19]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5504541 0.3647809 0.3709384 0.6497964
[2,] 0.6618692 0.6195316 0.6501740 0.7493321
[3,] 0.6618692 0.6432875 0.6263166 0.7493321
[4,] 0.3901608 0.5100120 0.5081429 0.6142648

$model[[20]]
         [,1]     [,2]     [,3]      [,4]
[1,] 2.735552 2.922154 2.796565 2.5360469
[2,] 1.930888 2.468431 1.722773 0.8181496
[3,] 2.909374 2.926559 2.769692 2.8127990
[4,] 2.909374 2.926559 2.890993 2.5392506

$model[[21]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5800637 0.5929568 0.5972401 0.6023987
[2,] 0.5744203 0.5255348 0.5772243 0.5865539
[3,] 0.4911833 0.5013103 0.4388481 0.5089731
[4,] 0.3290148 0.2540349 0.4711720 0.4827144

$model[[22]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.540986 1.841833 1.841833 0.980742
[2,] 2.975794 2.842433 2.717819 2.306864
[3,] 2.975794 2.717819 2.842433 2.306864
[4,] 3.039488 2.944131 2.944131 2.534556

$model[[23]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.528725 0.000000 0.000000 0.000000
[2,] 0.000000 1.505173 0.000000 0.000000
[3,] 0.000000 0.000000 1.505173 0.000000
[4,] 0.000000 0.000000 0.000000 1.360891

$model[[24]]
         [,1]     [,2]     [,3]      [,4]
[1,] 2.783523 2.908054 2.614871 2.8753768
[2,] 1.908156 2.450415 1.581787 0.8356782
[3,] 2.877164 2.908054 2.512163 2.8753768
[4,] 2.811577 2.906869 2.514532 2.3812450

$model[[25]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.9918563 0.9809124 1.0137320 0.8801039
[2,] 1.0834739 1.0162202 1.0254903 1.0584931
[3,] 0.7241957 0.6161602 0.8666594 0.8459705
[4,] 0.9893413 0.9357371 0.9304673 0.8002356

$model[[26]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.875377 2.008113 1.845549 1.042474
[2,] 3.515999 3.305697 3.094571 3.485789
[3,] 3.515999 3.477830 2.957527 3.485789
[4,] 3.513757 3.235383 2.873178 2.578619

$model[[27]]
         [,1]      [,2]     [,3]     [,4]
[1,] 3.711628 3.1466575 3.773173 3.730807
[2,] 3.731311 3.0214934 3.731311 3.689486
[3,] 3.707301 2.3096246 3.553109 3.560650
[4,] 3.197607 0.5046303 3.197607 3.075292

$model[[28]]
         [,1]      [,2]     [,3]     [,4]
[1,] 3.076194 0.6609383 3.625018 3.063605
[2,] 3.686811 3.0368525 3.686811 3.579542
[3,] 3.648742 2.3095238 3.499832 3.543911
[4,] 3.769273 3.1843105 3.769273 3.656537

$model[[29]]
         [,1]      [,2]     [,3]     [,4]
[1,] 2.783523 1.9081564 2.877164 2.811577
[2,] 2.908054 2.4504149 2.908054 2.906869
[3,] 2.614871 1.5817866 2.512163 2.514532
[4,] 2.875377 0.8356782 2.875377 2.381245

$model[[30]]
          [,1]      [,2]      [,3]      [,4]
[1,]  7.382884 3.7471045 49.776506  7.967847
[2,] 50.090397 4.6832237 50.103269 50.080478
[3,]  8.967847 0.9377683  4.840818  4.818766
[4,] 49.776506 4.6623218 50.080478  6.967847

$model[[31]]
         [,1]      [,2]     [,3]     [,4]
[1,] 4.662322 0.8377471 7.967847 4.646901
[2,] 7.967847 4.4889323 7.967847 7.382884
[3,] 7.967847 7.2799894 7.279989 7.382884
[4,] 6.967847 3.5566272 6.967847 6.271773

$model[[32]]
          [,1]      [,2]      [,3]      [,4]
[1,]  6.382884 50.090397 4.7227097  8.967847
[2,]  3.751541  4.675299 0.9277651  4.640003
[3,] 50.080478 50.103269 4.7383920 49.776506
[4,] 49.776506 50.080478 7.9678470  7.967847

$model[[33]]
         [,1]     [,2]      [,3]     [,4]
[1,] 3.567092 3.789225 3.2042621 3.789225
[2,] 3.067713 3.111613 2.4229797 3.119181
[3,] 3.451996 3.704763 3.0605764 3.703685
[4,] 2.673539 3.662040 0.7318909 2.696551

$model[[34]]
         [,1]     [,2]      [,3]     [,4]
[1,] 2.349345 2.438183 0.5233489 1.924530
[2,] 2.568187 2.633772 1.8860052 2.335921
[3,] 2.696551 2.742512 2.3297957 2.336917
[4,] 2.739191 2.785193 2.4400436 2.387948

$model[[35]]
          [,1]     [,2]      [,3]      [,4]
[1,] 0.9918563 1.083474 0.7241957 0.9893413
[2,] 0.9809124 1.016220 0.6161602 0.9357371
[3,] 1.0137320 1.025490 0.8666594 0.9304673
[4,] 0.8801039 1.058493 0.8459705 0.8002356

$model[[36]]
          [,1]      [,2]      [,3]      [,4]
[1,]  7.382884 50.090397 8.9678470 49.776506
[2,]  3.747105  4.683224 0.9377683  4.662322
[3,] 49.776506 50.103269 4.8408180 50.080478
[4,]  7.967847 50.080478 4.8187665  6.967847

$model[[37]]
        [,1]      [,2]      [,3]      [,4]
[1,] 0.63747 0.0000000 0.0000000 0.0000000
[2,] 0.00000 0.6673019 0.0000000 0.0000000
[3,] 0.00000 0.0000000 0.4847732 0.0000000
[4,] 0.00000 0.0000000 0.0000000 0.6517222

$model[[38]]
         [,1]     [,2]      [,3]     [,4]
[1,] 3.043283 3.188350 0.6432654 2.705081
[2,] 3.656537 3.677676 3.5401275 3.688244
[3,] 3.656537 3.737906 2.9889166 3.688244
[4,] 3.383702 3.641662 2.5920413 3.535790

$model[[39]]
          [,1]     [,2]     [,3]     [,4]
[1,] 3.7692730 4.880384 4.880384 4.662322
[2,] 0.6404286 3.689486 4.803277 3.119829
[3,] 3.9678470 4.967847 4.840818 4.967847
[4,] 3.9678470 4.967847 4.967847 4.646901

$model[[40]]
         [,1]     [,2]     [,3]     [,4]
[1,] 3.197607 4.060956 4.060956 3.967847
[2,] 2.784277 3.284380 3.373099 3.196148
[3,] 2.779815 4.060956 3.870880 3.738392
[4,] 0.707249 3.475994 3.475994 3.271773

$model[[41]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5504541 0.6618692 0.6618692 0.3901608
[2,] 0.3647809 0.6195316 0.6432875 0.5100120
[3,] 0.3709384 0.6501740 0.6263166 0.5081429
[4,] 0.6497964 0.7493321 0.7493321 0.6142648

$model[[42]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.875377 3.515999 3.515999 3.513757
[2,] 2.008113 3.305697 3.477830 3.235383
[3,] 1.845549 3.094571 2.957527 2.873178
[4,] 1.042474 3.485789 3.485789 2.578619

$model[[43]]
          [,1]     [,2]     [,3]     [,4]
[1,] 4.6623218 7.967847 7.967847 6.967847
[2,] 0.8377471 4.488932 7.279989 3.556627
[3,] 7.9678470 7.967847 7.279989 6.967847
[4,] 4.6469012 7.382884 7.382884 6.271773

$model[[44]]
          [,1]     [,2]     [,3]     [,4]
[1,] 3.0432832 3.656537 3.656537 3.383702
[2,] 3.1883497 3.677676 3.737906 3.641662
[3,] 0.6432654 3.540128 2.988917 2.592041
[4,] 2.7050809 3.688244 3.688244 3.535790

$model[[45]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3115104 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.4847732 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.4847732 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.4581253


$parameterModel
$parameterModel$Order
[1] 0.1

$parameterModel$HXmax
[1] 1.977267

$parameterModel$correction_1rOrdre
An object of class "correction"
Slot "Herror":
 [1] 3.559363e-16 7.500000e-01 1.152092e+00 1.398675e+00 1.561932e+00
 [6] 1.674996e+00 1.755532e+00 1.813980e+00 1.856961e+00 1.888878e+00
[11] 1.912761e+00 1.930748e+00 1.944373e+00 1.954749e+00 1.962696e+00
[16] 1.968818e+00 1.973563e+00 1.977267e+00

Slot "sderror":
 [1] 0.00000000 0.43301270 0.40954403 0.37097346 0.33343147 0.29811596
 [7] 0.26530394 0.23514125 0.20766473 0.18283125 0.16054263 0.14066180
[13] 0.12302483 0.10745156 0.09375495 0.08174880 0.07125347 0.06209990


$parameterModel$Entropy
[1] 0.8908981 1.4902471 1.5197142 1.9699698 0.8908981 1.4902471 0.9342127

$parameterModel$D
          [,1]      [,2]      [,3]      [,4]     [,5]      [,6]      [,7]
[1,] 0.0000000 1.9772675 0.6279091 1.9772675 0.000000 1.9772675 1.9772675
[2,] 1.9772675 0.5116939 1.9772675 0.5142576 1.977267 1.9772675 1.9772675
[3,] 0.6279091 1.9772675 0.6878502 1.9772675 1.977267 1.9772675 0.5846601
[4,] 1.9772675 0.5142576 1.9772675 1.4324988 1.977267 0.9467860 1.9772675
[5,] 0.0000000 1.9772675 1.9772675 1.9772675 0.000000 1.9772675 1.9772675
[6,] 1.9772675 1.9772675 1.9772675 0.9467860 1.977267 0.5116939 1.9772675
[7,] 1.9772675 1.9772675 0.5846601 1.9772675 1.977267 1.9772675 0.3281872

$parameterModel$Mperfil
           [,1]       [,2]       [,3]        [,4]       [,5]       [,6]
[1,] 0.54942964 0.36787215 0.35656947 0.045031312 0.54942964 0.36787215
[2,] 0.36787215 0.24630983 0.23874208 0.030150841 0.36787215 0.24630983
[3,] 0.35656947 0.23874208 0.23140686 0.029224472 0.35656947 0.23874208
[4,] 0.04503131 0.03015084 0.02922447 0.003690771 0.04503131 0.03015084
[5,] 0.54942964 0.36787215 0.35656947 0.045031312 0.54942964 0.36787215
[6,] 0.36787215 0.24630983 0.23874208 0.030150841 0.36787215 0.24630983
[7,] 0.53836509 0.36046385 0.34938879 0.044124460 0.53836509 0.36046385
           [,7]
[1,] 0.53836509
[2,] 0.36046385
[3,] 0.34938879
[4,] 0.04412446
[5,] 0.53836509
[6,] 0.36046385
[7,] 0.52752337

$parameterModel$interA
 [1] 1 1 1 1 1 2 2 2 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 6 6
[39] 7 7 7 7 7 7 7

$parameterModel$interB
 [1] 2 3 4 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 2 3 4 6 7 1 2 3 4 5 6 7
[39] 1 2 3 4 5 6 7


$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 


> 
> 
> cleanEx()
> nameEx("DivergenceRATTUS")
> ### * DivergenceRATTUS
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: DivergenceRATTUS
> ### Title: DivergenceRATTUS: Given a Transcription factor chooses the model
> ###   for a specific organism and method.
> ### Aliases: DivergenceRATTUS
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> DivergenceRATTUS(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
$model[[1]]
         [,1]      [,2]     [,3]     [,4]
[1,] 4.662322 3.8803841 4.880384 4.880384
[2,] 4.683224 3.6868109 4.797922 4.797922
[3,] 4.549303 2.7035574 4.467738 4.751541
[4,] 3.711628 0.7086106 4.725165 3.730807

$model[[2]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5046303 0.5693557 0.7721472 0.7721472
[2,] 0.6271304 0.5798238 0.7113100 0.7113100
[3,] 0.6731506 0.4556132 0.7474086 0.7731531
[4,] 0.7086106 0.6830986 0.7925811 0.7828377

$model[[3]]
         [,1]      [,2]     [,3]     [,4]
[1,] 2.735552 1.9308882 2.909374 2.909374
[2,] 2.922154 2.4684314 2.926559 2.926559
[3,] 2.796565 1.7227733 2.769692 2.890993
[4,] 2.536047 0.8181496 2.812799 2.539251

$model[[4]]
          [,1]      [,2]      [,3]      [,4]
[1,]  6.382884 3.7515412 50.080478 49.776506
[2,] 50.090397 4.6752987 50.103269 50.080478
[3,]  4.722710 0.9277651  4.738392  7.967847
[4,]  8.967847 4.6400029 49.776506  7.967847

$model[[5]]
         [,1]      [,2]     [,3]     [,4]
[1,] 3.769273 0.6404286 3.967847 3.967847
[2,] 4.880384 3.6894864 4.967847 4.967847
[3,] 4.880384 4.8032775 4.840818 4.967847
[4,] 4.662322 3.1198287 4.967847 4.646901

$model[[6]]
         [,1]     [,2]     [,3]      [,4]
[1,] 4.662322 4.683224 4.549303 3.7116284
[2,] 3.880384 3.686811 2.703557 0.7086106
[3,] 4.880384 4.797922 4.467738 4.7251652
[4,] 4.880384 4.797922 4.751541 3.7308068

$model[[7]]
          [,1]      [,2]    [,3]      [,4]
[1,] 0.6673019 0.0000000 0.00000 0.0000000
[2,] 0.0000000 0.6517222 0.00000 0.0000000
[3,] 0.0000000 0.0000000 0.63747 0.0000000
[4,] 0.0000000 0.0000000 0.00000 0.4847732

$model[[8]]
         [,1]     [,2]     [,3]     [,4]
[1,] 4.277232 4.206246 3.574347 1.111872
[2,] 5.856736 4.794123 5.491253 3.409934
[3,] 5.875858 5.406183 4.634538 3.416292
[4,] 6.279989 5.875858 6.050860 4.138678

$model[[9]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5800637 0.5744203 0.4911833 0.3290148
[2,] 0.5929568 0.5255348 0.5013103 0.2540349
[3,] 0.5972401 0.5772243 0.4388481 0.4711720
[4,] 0.6023987 0.5865539 0.5089731 0.4827144

$model[[10]]
         [,1]     [,2]     [,3]      [,4]
[1,] 3.711628 3.731311 3.707301 3.1976071
[2,] 3.146657 3.021493 2.309625 0.5046303
[3,] 3.773173 3.731311 3.553109 3.1976071
[4,] 3.730807 3.689486 3.560650 3.0752925

$model[[11]]
         [,1]     [,2]     [,3]      [,4]
[1,] 3.567092 3.067713 3.451996 2.6735385
[2,] 3.789225 3.111613 3.704763 3.6620405
[3,] 3.204262 2.422980 3.060576 0.7318909
[4,] 3.789225 3.119181 3.703685 2.6965512

$model[[12]]
         [,1]     [,2]     [,3]     [,4]
[1,] 3.197607 2.784277 2.779815 0.707249
[2,] 4.060956 3.284380 4.060956 3.475994
[3,] 4.060956 3.373099 3.870880 3.475994
[4,] 3.967847 3.196148 3.738392 3.271773

$model[[13]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5046303 0.6271304 0.6731506 0.7086106
[2,] 0.5693557 0.5798238 0.4556132 0.6830986
[3,] 0.7721472 0.7113100 0.7474086 0.7925811
[4,] 0.7721472 0.7113100 0.7731531 0.7828377

$model[[14]]
         [,1]     [,2]     [,3]     [,4]
[1,] 4.277232 5.856736 5.875858 6.279989
[2,] 4.206246 4.794123 5.406183 5.875858
[3,] 3.574347 5.491253 4.634538 6.050860
[4,] 1.111872 3.409934 3.416292 4.138678

$model[[15]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.6517222 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.8091688 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.8091688 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.8424087

$model[[16]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.540986 2.975794 2.975794 3.039488
[2,] 1.841833 2.842433 2.717819 2.944131
[3,] 1.841833 2.717819 2.842433 2.944131
[4,] 0.980742 2.306864 2.306864 2.534556

$model[[17]]
          [,1]     [,2]     [,3]     [,4]
[1,] 3.0761941 3.686811 3.648742 3.769273
[2,] 0.6609383 3.036853 2.309524 3.184311
[3,] 3.6250178 3.686811 3.499832 3.769273
[4,] 3.0636046 3.579542 3.543911 3.656537

$model[[18]]
          [,1]     [,2]     [,3]     [,4]
[1,] 2.3493446 2.568187 2.696551 2.739191
[2,] 2.4381835 2.633772 2.742512 2.785193
[3,] 0.5233489 1.886005 2.329796 2.440044
[4,] 1.9245301 2.335921 2.336917 2.387948

$model[[19]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5504541 0.3647809 0.3709384 0.6497964
[2,] 0.6618692 0.6195316 0.6501740 0.7493321
[3,] 0.6618692 0.6432875 0.6263166 0.7493321
[4,] 0.3901608 0.5100120 0.5081429 0.6142648

$model[[20]]
         [,1]     [,2]     [,3]      [,4]
[1,] 2.735552 2.922154 2.796565 2.5360469
[2,] 1.930888 2.468431 1.722773 0.8181496
[3,] 2.909374 2.926559 2.769692 2.8127990
[4,] 2.909374 2.926559 2.890993 2.5392506

$model[[21]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5800637 0.5929568 0.5972401 0.6023987
[2,] 0.5744203 0.5255348 0.5772243 0.5865539
[3,] 0.4911833 0.5013103 0.4388481 0.5089731
[4,] 0.3290148 0.2540349 0.4711720 0.4827144

$model[[22]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.540986 1.841833 1.841833 0.980742
[2,] 2.975794 2.842433 2.717819 2.306864
[3,] 2.975794 2.717819 2.842433 2.306864
[4,] 3.039488 2.944131 2.944131 2.534556

$model[[23]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.528725 0.000000 0.000000 0.000000
[2,] 0.000000 1.505173 0.000000 0.000000
[3,] 0.000000 0.000000 1.505173 0.000000
[4,] 0.000000 0.000000 0.000000 1.360891

$model[[24]]
         [,1]     [,2]     [,3]      [,4]
[1,] 2.783523 2.908054 2.614871 2.8753768
[2,] 1.908156 2.450415 1.581787 0.8356782
[3,] 2.877164 2.908054 2.512163 2.8753768
[4,] 2.811577 2.906869 2.514532 2.3812450

$model[[25]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.9918563 0.9809124 1.0137320 0.8801039
[2,] 1.0834739 1.0162202 1.0254903 1.0584931
[3,] 0.7241957 0.6161602 0.8666594 0.8459705
[4,] 0.9893413 0.9357371 0.9304673 0.8002356

$model[[26]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.875377 2.008113 1.845549 1.042474
[2,] 3.515999 3.305697 3.094571 3.485789
[3,] 3.515999 3.477830 2.957527 3.485789
[4,] 3.513757 3.235383 2.873178 2.578619

$model[[27]]
         [,1]      [,2]     [,3]     [,4]
[1,] 3.711628 3.1466575 3.773173 3.730807
[2,] 3.731311 3.0214934 3.731311 3.689486
[3,] 3.707301 2.3096246 3.553109 3.560650
[4,] 3.197607 0.5046303 3.197607 3.075292

$model[[28]]
         [,1]      [,2]     [,3]     [,4]
[1,] 3.076194 0.6609383 3.625018 3.063605
[2,] 3.686811 3.0368525 3.686811 3.579542
[3,] 3.648742 2.3095238 3.499832 3.543911
[4,] 3.769273 3.1843105 3.769273 3.656537

$model[[29]]
         [,1]      [,2]     [,3]     [,4]
[1,] 2.783523 1.9081564 2.877164 2.811577
[2,] 2.908054 2.4504149 2.908054 2.906869
[3,] 2.614871 1.5817866 2.512163 2.514532
[4,] 2.875377 0.8356782 2.875377 2.381245

$model[[30]]
          [,1]      [,2]      [,3]      [,4]
[1,]  7.382884 3.7471045 49.776506  7.967847
[2,] 50.090397 4.6832237 50.103269 50.080478
[3,]  8.967847 0.9377683  4.840818  4.818766
[4,] 49.776506 4.6623218 50.080478  6.967847

$model[[31]]
         [,1]      [,2]     [,3]     [,4]
[1,] 4.662322 0.8377471 7.967847 4.646901
[2,] 7.967847 4.4889323 7.967847 7.382884
[3,] 7.967847 7.2799894 7.279989 7.382884
[4,] 6.967847 3.5566272 6.967847 6.271773

$model[[32]]
          [,1]      [,2]      [,3]      [,4]
[1,]  6.382884 50.090397 4.7227097  8.967847
[2,]  3.751541  4.675299 0.9277651  4.640003
[3,] 50.080478 50.103269 4.7383920 49.776506
[4,] 49.776506 50.080478 7.9678470  7.967847

$model[[33]]
         [,1]     [,2]      [,3]     [,4]
[1,] 3.567092 3.789225 3.2042621 3.789225
[2,] 3.067713 3.111613 2.4229797 3.119181
[3,] 3.451996 3.704763 3.0605764 3.703685
[4,] 2.673539 3.662040 0.7318909 2.696551

$model[[34]]
         [,1]     [,2]      [,3]     [,4]
[1,] 2.349345 2.438183 0.5233489 1.924530
[2,] 2.568187 2.633772 1.8860052 2.335921
[3,] 2.696551 2.742512 2.3297957 2.336917
[4,] 2.739191 2.785193 2.4400436 2.387948

$model[[35]]
          [,1]     [,2]      [,3]      [,4]
[1,] 0.9918563 1.083474 0.7241957 0.9893413
[2,] 0.9809124 1.016220 0.6161602 0.9357371
[3,] 1.0137320 1.025490 0.8666594 0.9304673
[4,] 0.8801039 1.058493 0.8459705 0.8002356

$model[[36]]
          [,1]      [,2]      [,3]      [,4]
[1,]  7.382884 50.090397 8.9678470 49.776506
[2,]  3.747105  4.683224 0.9377683  4.662322
[3,] 49.776506 50.103269 4.8408180 50.080478
[4,]  7.967847 50.080478 4.8187665  6.967847

$model[[37]]
        [,1]      [,2]      [,3]      [,4]
[1,] 0.63747 0.0000000 0.0000000 0.0000000
[2,] 0.00000 0.6673019 0.0000000 0.0000000
[3,] 0.00000 0.0000000 0.4847732 0.0000000
[4,] 0.00000 0.0000000 0.0000000 0.6517222

$model[[38]]
         [,1]     [,2]      [,3]     [,4]
[1,] 3.043283 3.188350 0.6432654 2.705081
[2,] 3.656537 3.677676 3.5401275 3.688244
[3,] 3.656537 3.737906 2.9889166 3.688244
[4,] 3.383702 3.641662 2.5920413 3.535790

$model[[39]]
          [,1]     [,2]     [,3]     [,4]
[1,] 3.7692730 4.880384 4.880384 4.662322
[2,] 0.6404286 3.689486 4.803277 3.119829
[3,] 3.9678470 4.967847 4.840818 4.967847
[4,] 3.9678470 4.967847 4.967847 4.646901

$model[[40]]
         [,1]     [,2]     [,3]     [,4]
[1,] 3.197607 4.060956 4.060956 3.967847
[2,] 2.784277 3.284380 3.373099 3.196148
[3,] 2.779815 4.060956 3.870880 3.738392
[4,] 0.707249 3.475994 3.475994 3.271773

$model[[41]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5504541 0.6618692 0.6618692 0.3901608
[2,] 0.3647809 0.6195316 0.6432875 0.5100120
[3,] 0.3709384 0.6501740 0.6263166 0.5081429
[4,] 0.6497964 0.7493321 0.7493321 0.6142648

$model[[42]]
         [,1]     [,2]     [,3]     [,4]
[1,] 2.875377 3.515999 3.515999 3.513757
[2,] 2.008113 3.305697 3.477830 3.235383
[3,] 1.845549 3.094571 2.957527 2.873178
[4,] 1.042474 3.485789 3.485789 2.578619

$model[[43]]
          [,1]     [,2]     [,3]     [,4]
[1,] 4.6623218 7.967847 7.967847 6.967847
[2,] 0.8377471 4.488932 7.279989 3.556627
[3,] 7.9678470 7.967847 7.279989 6.967847
[4,] 4.6469012 7.382884 7.382884 6.271773

$model[[44]]
          [,1]     [,2]     [,3]     [,4]
[1,] 3.0432832 3.656537 3.656537 3.383702
[2,] 3.1883497 3.677676 3.737906 3.641662
[3,] 0.6432654 3.540128 2.988917 2.592041
[4,] 2.7050809 3.688244 3.688244 3.535790

$model[[45]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3115104 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.4847732 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.4847732 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.4581253


$parameterModel
$parameterModel$Order
[1] 0.1

$parameterModel$HXmax
[1] 1.977267

$parameterModel$correction_1rOrdre
An object of class "correction"
Slot "Herror":
 [1] 3.559363e-16 7.500000e-01 1.152092e+00 1.398675e+00 1.561932e+00
 [6] 1.674996e+00 1.755532e+00 1.813980e+00 1.856961e+00 1.888878e+00
[11] 1.912761e+00 1.930748e+00 1.944373e+00 1.954749e+00 1.962696e+00
[16] 1.968818e+00 1.973563e+00 1.977267e+00

Slot "sderror":
 [1] 0.00000000 0.43301270 0.40954403 0.37097346 0.33343147 0.29811596
 [7] 0.26530394 0.23514125 0.20766473 0.18283125 0.16054263 0.14066180
[13] 0.12302483 0.10745156 0.09375495 0.08174880 0.07125347 0.06209990


$parameterModel$Entropy
[1] 0.8908981 1.4902471 1.5197142 1.9699698 0.8908981 1.4902471 0.9342127

$parameterModel$D
          [,1]      [,2]      [,3]      [,4]     [,5]      [,6]      [,7]
[1,] 0.0000000 1.9772675 0.6279091 1.9772675 0.000000 1.9772675 1.9772675
[2,] 1.9772675 0.5116939 1.9772675 0.5142576 1.977267 1.9772675 1.9772675
[3,] 0.6279091 1.9772675 0.6878502 1.9772675 1.977267 1.9772675 0.5846601
[4,] 1.9772675 0.5142576 1.9772675 1.4324988 1.977267 0.9467860 1.9772675
[5,] 0.0000000 1.9772675 1.9772675 1.9772675 0.000000 1.9772675 1.9772675
[6,] 1.9772675 1.9772675 1.9772675 0.9467860 1.977267 0.5116939 1.9772675
[7,] 1.9772675 1.9772675 0.5846601 1.9772675 1.977267 1.9772675 0.3281872

$parameterModel$Mperfil
           [,1]       [,2]       [,3]        [,4]       [,5]       [,6]
[1,] 0.54942964 0.36787215 0.35656947 0.045031312 0.54942964 0.36787215
[2,] 0.36787215 0.24630983 0.23874208 0.030150841 0.36787215 0.24630983
[3,] 0.35656947 0.23874208 0.23140686 0.029224472 0.35656947 0.23874208
[4,] 0.04503131 0.03015084 0.02922447 0.003690771 0.04503131 0.03015084
[5,] 0.54942964 0.36787215 0.35656947 0.045031312 0.54942964 0.36787215
[6,] 0.36787215 0.24630983 0.23874208 0.030150841 0.36787215 0.24630983
[7,] 0.53836509 0.36046385 0.34938879 0.044124460 0.53836509 0.36046385
           [,7]
[1,] 0.53836509
[2,] 0.36046385
[3,] 0.34938879
[4,] 0.04412446
[5,] 0.53836509
[6,] 0.36046385
[7,] 0.52752337

$parameterModel$interA
 [1] 1 1 1 1 1 2 2 2 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 6 6
[39] 7 7 7 7 7 7 7

$parameterModel$interB
 [1] 2 3 4 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 2 3 4 6 7 1 2 3 4 5 6 7
[39] 1 2 3 4 5 6 7


$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 


> 
> 
> cleanEx()
> nameEx("EntropyDROSOPHILA")
> ### * EntropyDROSOPHILA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: EntropyDROSOPHILA
> ### Title: EntropyDROSOPHILA: Given a Transcription factor chooses the
> ###   model for a specific organism and method.
> ### Aliases: EntropyDROSOPHILA
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> EntropyDROSOPHILA(nameTF=iicc$nameTF)
[1] "Transcription Factor not included"
NULL
> 
> 
> cleanEx()
> nameEx("EntropyHOMO")
> ### * EntropyHOMO
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: EntropyHOMO
> ### Title: EntropyHOMO: Given a Transcription factor chooses the model for
> ###   a specific organism and method.
> ### Aliases: EntropyHOMO
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> EntropyHOMO(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
$model[[1]]
[1] 0.4854608 1.0582770 0.9530139 1.7487726 0.5907239 0.9132830 0.4854608

$model[[2]]
[1] 0.2974722 0.9530139 1.0869877 1.7229713 0.2974722 1.0582770 0.7742433

$model[[3]]
[1] 0.5907239 0.9132830 1.0869877 1.7229713 0.5907239 0.7742433 0.7742433

$model[[4]]
[1] 0.5907239 0.7742433 1.2319818 1.6393210 0.4854608 0.9530139 0.6292492


$parameterModel
$parameterModel$Order
[1] 1

$parameterModel$HXmax
[1] 1.848238

$parameterModel$ErrorHX
[1] 0.1235761

$parameterModel$Redundancy
[1] 0.83251974 0.56549341 0.46628813 0.08833324 0.83251974 0.56549341 0.72770918


$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 


> 
> 
> cleanEx()
> nameEx("EntropyMUS")
> ### * EntropyMUS
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: EntropyMUS
> ### Title: EntropyMUS: Given a Transcription factor chooses the model for a
> ###   specific organism and method.
> ### Aliases: EntropyMUS
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> EntropyMUS(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
$model[[1]]
[1] 0.6904479 1.4828004 1.2410020 1.8785365 0.9805996 1.1992045 0.6904479

$model[[2]]
[1] 0.5326598 1.2410020 1.3202100 1.8612632 0.5326598 1.4828004 1.1146173

$model[[3]]
[1] 0.9805996 1.1992045 1.3202100 1.8612632 0.9805996 1.1146173 1.1146173

$model[[4]]
[1] 0.9805996 1.1146173 1.5926913 1.8215392 0.6904479 1.2410020 0.7901753


$parameterModel
$parameterModel$Order
[1] 0.5

$parameterModel$HXmax
[1] 1.927123

$parameterModel$ErrorHX
[1] 0.07767061

$parameterModel$Redundancy
[1] 0.71765571 0.41108653 0.34519041 0.04278118 0.71765571 0.41108653 0.63490734


$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 


> 
> 
> cleanEx()
> nameEx("EntropyRATTUS")
> ### * EntropyRATTUS
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: EntropyRATTUS
> ### Title: EntropyRATTUS: Given a Transcription factor chooses the model
> ###   for a specific organism and method.
> ### Aliases: EntropyRATTUS
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> EntropyRATTUS(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
$model[[1]]
[1] 0.3010982 0.6441060 0.6316689 1.5071703 0.3109797 0.6193381 0.3010982

$model[[2]]
[1] 0.1515591 0.6316689 0.7884959 1.4846278 0.1515591 0.6441060 0.4679490

$model[[3]]
[1] 0.3109797 0.6193381 0.7884959 1.4846278 0.3109797 0.4679490 0.4679490

$model[[4]]
[1] 0.3109797 0.4679490 0.8163749 1.3359837 0.3010982 0.6316689 0.4460065


$parameterModel
$parameterModel$Order
[1] 2

$parameterModel$HXmax
[1] 1.786071

$parameterModel$ErrorHX
[1] 0.1530527

$parameterModel$Redundancy
[1] 0.9104510 0.7232138 0.6263169 0.2110546 0.9104510 0.7232138 0.8222455


$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 


> 
> 
> cleanEx()
> nameEx("Hmemory")
> ### * Hmemory
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Hmemory
> ### Title: Library of entropy values
> ### Aliases: Hmemory
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> factor<-iicc$Transcriptionfactor
> Hmemory(iicc,factor)
$Entropy
$Entropy[[1]]
[1] 0.4854608 1.0582770 0.9530139 1.7487726 0.5907239 0.9132830 0.4854608

$Entropy[[2]]
[1] 0.2974722 0.9530139 1.0869877 1.7229713 0.2974722 1.0582770 0.7742433

$Entropy[[3]]
[1] 0.5907239 0.9132830 1.0869877 1.7229713 0.5907239 0.7742433 0.7742433

$Entropy[[4]]
[1] 0.5907239 0.7742433 1.2319818 1.6393210 0.4854608 0.9530139 0.6292492


> 
> 
> 
> cleanEx()
> nameEx("Hread")
> ### * Hread
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Hread
> ### Title: To read Entropy values
> ### Aliases: Hread
> 
> ### ** Examples
> 
> require("MEET")
> require("seqinr")
> write.fasta <- get("write.fasta",pos="package:seqinr")
> read.fasta <- get("read.fasta",pos="package:seqinr")
> data(iicc)
> training.set.mes.rand<-iicc$Transcriptionfactor
> val.set<-c("A","T","C","C","G","T","A","T","T","A","C","G")
> test<-Hread(training.set.mes.rand, val.set, iicc)
> 
> 
> 
> cleanEx()
> nameEx("JacksonParameters")
> ### * JacksonParameters
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: JacksonParameters
> ### Title: JacksonParameters: To calculates the parameters needed to
> ###   transform a Q-residual to a confidence interval
> ### Aliases: JacksonParameters
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> data(iicc)
> nPCs<-1
> TFBS<-iicc$Transcriptionfactor
> Prob<-iicc$background
> missing<-50
> NumericalMatrix<-numericalDNA(Prob)
> suma<-apply(TFBS,2,function(y){sum(y=="-")})
> threshold<-floor(nrow(TFBS)*missing/100)
> TFBS<-TFBS[, suma<=threshold]
> ncolTFBS<-ncol(TFBS)
> TFBSnum<-apply(TFBS,1,function(x){as.vector(t(NumericalMatrix[x,]))})
> TFBSnum<-t(TFBSnum)
> model<-pca(TFBSnum, nPcs=nPCs, method="svd", center=TRUE)
> JacksonPars<-JacksonParameters(nPCs,TFBSnum)
> 
> 
> 
> cleanEx()
> nameEx("MImemory")
> ### * MImemory
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: MImemory
> ### Title: Library of PredictDivergence values
> ### Aliases: MImemory
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> iicc <- detector_2nOrdre_init(training.set=iicc$Transcriptionfactor, val.set=val.set, iicc)
> MImemory(iicc,training.set=iicc$Transcriptionfactor)
$Divergence
$Divergence[[1]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4854608 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.2974722 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.5907239 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.5907239

$Divergence[[2]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.0000000 0.3404667 0.2352035 0.3801976
[2,] 0.1922091 0.0000000 0.1922091 0.1922091
[3,] 0.2064929 0.3404667 0.3404667 0.4854608
[4,] 0.2064929 0.3404667 0.3404667 0.4854608

$Divergence[[3]]
         [,1]      [,2]     [,3]      [,4]
[1,] 1.058277 0.0000000 0.000000 0.0000000
[2,] 0.000000 0.9530139 0.000000 0.0000000
[3,] 0.000000 0.0000000 0.913283 0.0000000
[4,] 0.000000 0.0000000 0.000000 0.7742433

$Divergence[[4]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2952432 0.4292170 0.4292170 0.5742111
[2,] 0.1899801 0.3239539 0.4292170 0.4689480
[3,] 0.2555123 0.3894861 0.2842230 0.4292170
[4,] 0.2847491 0.2504465 0.2504465 0.2901774

$Divergence[[5]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3764550 0.3506537 0.3506537 0.2670035
[2,] 0.2711919 0.2453905 0.3506537 0.1617403
[3,] 0.2314609 0.3109228 0.2056596 0.2272726
[4,] 0.2374154 0.2116140 0.2116140 0.2298095

$Divergence[[6]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4660386 0.6110327 0.3269990 0.5057695
[2,] 0.3607755 0.5057695 0.2217358 0.5057695
[3,] 0.3210445 0.4660386 0.3269990 0.3607755
[4,] 0.3269990 0.3269990 0.3165048 0.2217358

$Divergence[[7]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.4952754 0.4952754 0.3502813
[2,] 0.0000000 0.3900122 0.3900122 0.3502813
[3,] 0.2064929 0.3502813 0.3502813 0.2052872
[4,] 0.2014270 0.2112417 0.2112417 0.1715107

$Divergence[[8]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.0000000 0.1922091 0.2064929 0.2064929
[2,] 0.3404667 0.0000000 0.3404667 0.3404667
[3,] 0.2352035 0.1922091 0.3404667 0.3404667
[4,] 0.3801976 0.1922091 0.4854608 0.4854608

$Divergence[[9]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2952432 0.1899801 0.2555123 0.2847491
[2,] 0.4292170 0.3239539 0.3894861 0.2504465
[3,] 0.4292170 0.4292170 0.2842230 0.2504465
[4,] 0.5742111 0.4689480 0.4292170 0.2901774

$Divergence[[10]]
          [,1]     [,2]     [,3]     [,4]
[1,] 0.9530139 0.000000 0.000000 0.000000
[2,] 0.0000000 1.086988 0.000000 0.000000
[3,] 0.0000000 0.000000 1.086988 0.000000
[4,] 0.0000000 0.000000 0.000000 1.231982

$Divergence[[11]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2380766 0.2380766 0.2217358 0.0000000
[2,] 0.2270563 0.3720504 0.2330107 0.2667872
[3,] 0.2270563 0.3720504 0.1932798 0.3720504
[4,] 0.3720504 0.5170445 0.2330107 0.4117813

$Divergence[[12]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3764550 0.2711919 0.2314609 0.2374154
[2,] 0.3506537 0.2453905 0.3109228 0.2116140
[3,] 0.3506537 0.3506537 0.2056596 0.2116140
[4,] 0.2670035 0.1617403 0.2272726 0.2298095

$Divergence[[13]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.836044 0.000000 0.000000 0.000000
[2,] 0.000000 1.836044 0.000000 0.000000
[3,] 0.000000 0.000000 1.836044 0.000000
[4,] 0.000000 0.000000 0.000000 1.639321

$Divergence[[14]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2314609 0.3764550 0.2374154 0.2711919
[2,] 0.3109228 0.3506537 0.2116140 0.2453905
[3,] 0.2056596 0.3506537 0.2116140 0.3506537
[4,] 0.2272726 0.2670035 0.2298095 0.1617403

$Divergence[[15]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3404667 0.4842551 0.4842551 0.3392611
[2,] 0.3404667 0.4584538 0.4584538 0.3134598
[3,] 0.2749344 0.4584538 0.4584538 0.4584538
[4,] 0.3404667 0.3748036 0.3748036 0.2298095

$Divergence[[16]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.5907239 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.2974722 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.5907239 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.4854608

$Divergence[[17]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.1966782 0.4854608 0.4854608 0.3404667
[2,] 0.1922091 0.1922091 0.1922091 0.0000000
[3,] 0.1966782 0.4854608 0.4854608 0.3404667
[4,] 0.0000000 0.3801976 0.3801976 0.3404667

$Divergence[[18]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4660386 0.3607755 0.3210445 0.3269990
[2,] 0.6110327 0.5057695 0.4660386 0.3269990
[3,] 0.3269990 0.2217358 0.3269990 0.3165048
[4,] 0.5057695 0.5057695 0.3607755 0.2217358

$Divergence[[19]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2380766 0.2270563 0.2270563 0.3720504
[2,] 0.2380766 0.3720504 0.3720504 0.5170445
[3,] 0.2217358 0.2330107 0.1932798 0.2330107
[4,] 0.0000000 0.2667872 0.3720504 0.4117813

$Divergence[[20]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2314609 0.3109228 0.2056596 0.2272726
[2,] 0.3764550 0.3506537 0.3506537 0.2670035
[3,] 0.2374154 0.2116140 0.2116140 0.2298095
[4,] 0.2711919 0.2453905 0.3506537 0.1617403

$Divergence[[21]]
         [,1]     [,2]      [,3]      [,4]
[1,] 0.913283 0.000000 0.0000000 0.0000000
[2,] 0.000000 1.058277 0.0000000 0.0000000
[3,] 0.000000 0.000000 0.7742433 0.0000000
[4,] 0.000000 0.000000 0.0000000 0.9530139

$Divergence[[22]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.3502813 0.3502813 0.2052872
[2,] 0.2064929 0.4952754 0.4952754 0.3502813
[3,] 0.2014270 0.2112417 0.2112417 0.1715107
[4,] 0.0000000 0.3900122 0.3900122 0.3502813

$Divergence[[23]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.0000000 0.2064929 0.2014270
[2,] 0.4952754 0.3900122 0.3502813 0.2112417
[3,] 0.4952754 0.3900122 0.3502813 0.2112417
[4,] 0.3502813 0.3502813 0.2052872 0.1715107

$Divergence[[24]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3404667 0.3404667 0.2749344 0.3404667
[2,] 0.4842551 0.4584538 0.4584538 0.3748036
[3,] 0.4842551 0.4584538 0.4584538 0.3748036
[4,] 0.3392611 0.3134598 0.4584538 0.2298095

$Divergence[[25]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.1966782 0.1922091 0.1966782 0.0000000
[2,] 0.4854608 0.1922091 0.4854608 0.3801976
[3,] 0.4854608 0.1922091 0.4854608 0.3801976
[4,] 0.3404667 0.0000000 0.3404667 0.3404667

$Divergence[[26]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.2064929 0.2064929 0.2014270 0.0000000
[2,] 0.3502813 0.4952754 0.2112417 0.3900122
[3,] 0.3502813 0.4952754 0.2112417 0.3900122
[4,] 0.2052872 0.3502813 0.1715107 0.3502813

$Divergence[[27]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.4854608 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.7742433 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.7742433 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.6292492


$interA
 [1] 1 1 2 2 2 2 2 3 3 3 3 4 4 4 4 5 5 6 6 6 6 6 7 7 7 7 7

$interB
 [1] 1 3 2 3 4 6 7 1 2 3 6 2 4 6 7 5 7 2 3 4 6 7 2 4 5 6 7

> 
> 
> 
> cleanEx()
> nameEx("MIread")
> ### * MIread
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: MIread
> ### Title: To read PredictDivergence values
> ### Aliases: MIread
> 
> ### ** Examples
> 
> require("MEET")
> data(DrosophilaDivergence)
> model<-list()
> model$D<-iicc[["a1"]]$model$parameterModel$D
> model$HXmax<-iicc[["a1"]]$model$parameterModel$HXmax
> model$correctioc_1rOrdre<-iicc[["a1"]]$model$parameterModel$correction_1rOrdre
> model$Entropy<-iicc[["a1"]]$model$parameterModel$HX
> model$Mperfil<-iicc[["a1"]]$model$parameterModel$Mperfil
> model$interA<-iicc[["a1"]]$model$parameterModel$interA
> model$interB<-iicc[["a1"]]$model$parameterModel$interB
> model$Divergence<-iicc[["a1"]]$model$model
> 
> test<-MIread(training.set=iicc[["a1"]]$Transcriptionfactor, val.set=iicc[["a1"]]$Transcriptionfactor[1,],iicc=model)
==21311== Invalid write of size 1
==21311==    at 0x483BD5E: strcpy (/builddir/build/BUILD/valgrind-3.15.0/memcheck/../shared/vg_replace_strmem.c:513)
==21311==    by 0x1FE0E39F: MI (packages/tests-vg/MEET/src/MI.c:30)
==21311==    by 0x49BC01: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==21311==    by 0x49C184: do_dotcall (svn/R-devel/src/main/dotcode.c:1280)
==21311==    by 0x4D88CB: bcEval (svn/R-devel/src/main/eval.c:7610)
==21311==    by 0x4E8347: Rf_eval (svn/R-devel/src/main/eval.c:688)
==21311==    by 0x4E8C5F: forcePromise (svn/R-devel/src/main/eval.c:516)
==21311==    by 0x4E906F: FORCE_PROMISE (svn/R-devel/src/main/eval.c:5087)
==21311==    by 0x4E906F: getvar (svn/R-devel/src/main/eval.c:5128)
==21311==    by 0x4D78B9: bcEval (svn/R-devel/src/main/eval.c:6806)
==21311==    by 0x4E8347: Rf_eval (svn/R-devel/src/main/eval.c:688)
==21311==    by 0x4E9F06: R_execClosure (svn/R-devel/src/main/eval.c:1853)
==21311==    by 0x4EACE3: Rf_applyClosure (svn/R-devel/src/main/eval.c:1779)
==21311==  Address 0x23825d52 is 338 bytes inside a block of size 7,960 alloc'd
==21311==    at 0x483880B: malloc (/builddir/build/BUILD/valgrind-3.15.0/coregrind/m_replacemalloc/vg_replace_malloc.c:309)
==21311==    by 0x5224E0: GetNewPage (svn/R-devel/src/main/memory.c:946)
==21311==    by 0x52428B: Rf_allocVector3 (svn/R-devel/src/main/memory.c:2784)
==21311==    by 0x57C990: Rf_allocVector (svn/R-devel/src/include/Rinlinedfuns.h:593)
==21311==    by 0x57C990: ReadItem (svn/R-devel/src/main/serialize.c:1925)
==21311==    by 0x57C025: ReadItem (svn/R-devel/src/main/serialize.c:1865)
==21311==    by 0x57CDB2: ReadItem (svn/R-devel/src/main/serialize.c:2010)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C025: ReadItem (svn/R-devel/src/main/serialize.c:1865)
==21311==    by 0x57DD98: R_Unserialize (svn/R-devel/src/main/serialize.c:2177)
==21311== 
==21311== Invalid read of size 1
==21311==    at 0x483CB90: strcmp (/builddir/build/BUILD/valgrind-3.15.0/memcheck/../shared/vg_replace_strmem.c:849)
==21311==    by 0x1FE0F5CC: getListElement (packages/tests-vg/MEET/src/getListElement.c:12)
==21311==    by 0x1FE0E3AC: MI (packages/tests-vg/MEET/src/MI.c:31)
==21311==    by 0x49BC01: R_doDotCall (svn/R-devel/src/main/dotcode.c:624)
==21311==    by 0x49C184: do_dotcall (svn/R-devel/src/main/dotcode.c:1280)
==21311==    by 0x4D88CB: bcEval (svn/R-devel/src/main/eval.c:7610)
==21311==    by 0x4E8347: Rf_eval (svn/R-devel/src/main/eval.c:688)
==21311==    by 0x4E8C5F: forcePromise (svn/R-devel/src/main/eval.c:516)
==21311==    by 0x4E906F: FORCE_PROMISE (svn/R-devel/src/main/eval.c:5087)
==21311==    by 0x4E906F: getvar (svn/R-devel/src/main/eval.c:5128)
==21311==    by 0x4D78B9: bcEval (svn/R-devel/src/main/eval.c:6806)
==21311==    by 0x4E8347: Rf_eval (svn/R-devel/src/main/eval.c:688)
==21311==    by 0x4E9F06: R_execClosure (svn/R-devel/src/main/eval.c:1853)
==21311==  Address 0x23825d52 is 338 bytes inside a block of size 7,960 alloc'd
==21311==    at 0x483880B: malloc (/builddir/build/BUILD/valgrind-3.15.0/coregrind/m_replacemalloc/vg_replace_malloc.c:309)
==21311==    by 0x5224E0: GetNewPage (svn/R-devel/src/main/memory.c:946)
==21311==    by 0x52428B: Rf_allocVector3 (svn/R-devel/src/main/memory.c:2784)
==21311==    by 0x57C990: Rf_allocVector (svn/R-devel/src/include/Rinlinedfuns.h:593)
==21311==    by 0x57C990: ReadItem (svn/R-devel/src/main/serialize.c:1925)
==21311==    by 0x57C025: ReadItem (svn/R-devel/src/main/serialize.c:1865)
==21311==    by 0x57CDB2: ReadItem (svn/R-devel/src/main/serialize.c:2010)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C672: ReadItem (svn/R-devel/src/main/serialize.c:1954)
==21311==    by 0x57C025: ReadItem (svn/R-devel/src/main/serialize.c:1865)
==21311==    by 0x57DD98: R_Unserialize (svn/R-devel/src/main/serialize.c:2177)
==21311== 
> 
> 
> 
> cleanEx()
> nameEx("ModelDivergence")
> ### * ModelDivergence
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ModelDivergence
> ### Title: To create Model Divergence
> ### Aliases: ModelDivergence
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> iicc$parametersIdeal<-0.5
> ModelDivergence(iicc)
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
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$model[[2]]
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[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[3]]
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[1,] 1.893929 1.893929 1.893929 1.893929
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$model[[4]]
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$model[[5]]
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$model[[6]]
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[2,] 1.893929 1.893929 1.893929 1.893929
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$model[[7]]
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[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[8]]
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$model[[9]]
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$model[[10]]
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$model[[11]]
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$model[[12]]
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[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[13]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[14]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[15]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[16]]
         [,1]      [,2]      [,3]      [,4]
[1,] 0.755003 0.0000000 0.0000000 0.0000000
[2,] 0.000000 0.9104575 0.0000000 0.0000000
[3,] 0.000000 0.0000000 0.9104575 0.0000000
[4,] 0.000000 0.0000000 0.0000000 0.9765381

$model[[17]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[18]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[19]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[20]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.9420741 0.6119900 0.6819276 1.1364522
[2,] 1.1485250 0.8829463 1.0425532 1.2359878
[3,] 1.1485250 0.9660791 0.9572203 1.2359878
[4,] 0.6973453 0.6877736 0.7121894 0.9447061

$model[[21]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[22]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.7153858 0.8823722 0.9107956 0.9370422
[2,] 0.7126594 0.7345099 0.8548975 0.8710817
[3,] 0.6459750 0.7695978 0.6914874 0.8069132
[4,] 0.4476736 0.4803738 0.6738970 0.7264429

$model[[23]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[24]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.621666 0.000000 0.000000 0.000000
[2,] 0.000000 1.595134 0.000000 0.000000
[3,] 0.000000 0.000000 1.595134 0.000000
[4,] 0.000000 0.000000 0.000000 1.473225

$model[[25]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[26]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[27]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[28]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3429732 0.0000000 0.3990033 0.0000000
[2,] 0.4863947 0.3164697 0.4863947 0.0000000
[3,] 0.5688568 0.4863947 0.5267336 0.3990033
[4,] 0.5688568 0.4863947 0.5688568 0.3429732

$model[[29]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[30]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[31]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[32]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[33]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[34]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[35]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[36]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[37]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[38]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[39]]
          [,1]      [,2]      [,3]     [,4]
[1,] 0.7306971 0.0000000 0.0000000 0.000000
[2,] 0.0000000 0.7927433 0.0000000 0.000000
[3,] 0.0000000 0.0000000 0.5761463 0.000000
[4,] 0.0000000 0.0000000 0.0000000 0.755003

$model[[40]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[41]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[42]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[43]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.9420741 1.1485250 1.1485250 0.6973453
[2,] 0.6119900 0.8829463 0.9660791 0.6877736
[3,] 0.6819276 1.0425532 0.9572203 0.7121894
[4,] 1.1364522 1.2359878 1.2359878 0.9447061

$model[[44]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[45]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[46]]
         [,1]     [,2]     [,3]     [,4]
[1,] 1.893929 1.893929 1.893929 1.893929
[2,] 1.893929 1.893929 1.893929 1.893929
[3,] 1.893929 1.893929 1.893929 1.893929
[4,] 1.893929 1.893929 1.893929 1.893929

$model[[47]]
          [,1]      [,2]      [,3]      [,4]
[1,] 0.3672488 0.0000000 0.0000000 0.0000000
[2,] 0.0000000 0.5761463 0.0000000 0.0000000
[3,] 0.0000000 0.0000000 0.5761463 0.0000000
[4,] 0.0000000 0.0000000 0.0000000 0.5185427


$parameterModel
$parameterModel$Order
[1] 0.5

$parameterModel$HXmax
[1] 1.887915

$parameterModel$correction_1rOrdre
An object of class "correction"
Slot "Herror":
 [1] 0.000000 0.730400 1.100804 1.323801 1.470454 1.572370 1.645997 1.700783
 [9] 1.742528 1.774972 1.800620 1.821200 1.837934 1.851703 1.863157 1.872780
[17] 1.880938 1.887915

Slot "sderror":
 [1] 0.0000000 0.4437520 0.4316336 0.3961344 0.3591114 0.3245797 0.2933830
 [8] 0.2655406 0.2408201 0.2189175 0.1995227 0.1823443 0.1671185 0.1536111
[15] 0.1416158 0.1309521 0.1214622 0.1130079


$parameterModel$Entropy
[1] 0.5441123 1.1349089 1.2618989 1.8446788 0.5441123 1.1349089 0.7035786

$parameterModel$D
          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]     [,7]
[1,] 0.0000000 1.8879153 1.8879153 1.8879153 0.3348493 1.8879153 1.887915
[2,] 1.8879153 0.6049598 1.8879153 0.7725604 1.8879153 1.8879153 1.887915
[3,] 1.8879153 1.8879153 0.7918209 1.8879153 1.8879153 1.8879153 1.001980
[4,] 1.8879153 0.7725604 1.8879153 1.5361205 1.8879153 1.8879153 1.887915
[5,] 0.3348493 1.8879153 1.8879153 1.8879153 0.0000000 1.8879153 1.887915
[6,] 1.8879153 1.8879153 1.8879153 1.8879153 1.8879153 0.6049598 1.887915
[7,] 1.8879153 1.8879153 1.0019803 1.8879153 1.8879153 1.8879153 0.384995

$parameterModel$Mperfil
          [,1]      [,2]      [,3]       [,4]      [,5]      [,6]      [,7]
[1,] 0.7117920 0.5328251 0.4858231 0.12767649 0.7117920 0.5328251 0.6682253
[2,] 0.5328251 0.3988560 0.3636719 0.09557460 0.5328251 0.3988560 0.5002124
[3,] 0.4858231 0.3636719 0.3315914 0.08714370 0.4858231 0.3636719 0.4560873
[4,] 0.1276765 0.0955746 0.0871437 0.02290175 0.1276765 0.0955746 0.1198618
[5,] 0.7117920 0.5328251 0.4858231 0.12767649 0.7117920 0.5328251 0.6682253
[6,] 0.5328251 0.3988560 0.3636719 0.09557460 0.5328251 0.3988560 0.5002124
[7,] 0.6682253 0.5002124 0.4560873 0.11986178 0.6682253 0.5002124 0.6273251

$parameterModel$interA
 [1] 1 1 1 1 1 1 2 2 2 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 6 6 6 6 6
[39] 6 6 7 7 7 7 7 7 7

$parameterModel$interB
 [1] 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 6 7 1 2 3 4 5
[39] 6 7 1 2 3 4 5 6 7


> 
> 
> 
> cleanEx()
> nameEx("ModelEntropy")
> ### * ModelEntropy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ModelEntropy
> ### Title: To create Model Entropy
> ### Aliases: ModelEntropy
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> iicc$parametersIdeal<-1
> ModelEntropy(iicc)
$model
$model[[1]]
[1] 0.4854608 1.0582770 0.9530139 1.7487726 0.5907239 0.9132830 0.4854608

$model[[2]]
[1] 0.2974722 0.9530139 1.0869877 1.7229713 0.2974722 1.0582770 0.7742433

$model[[3]]
[1] 0.5907239 0.9132830 1.0869877 1.7229713 0.5907239 0.7742433 0.7742433

$model[[4]]
[1] 0.5907239 0.7742433 1.2319818 1.6393210 0.4854608 0.9530139 0.6292492


$parameterModel
$parameterModel$Order
[1] 1

$parameterModel$HXmax
[1] 1.832272

$parameterModel$ErrorHX
[1] 0.1183434

$parameterModel$Redundancy
[1] 0.83106037 0.56170725 0.46163752 0.08038924 0.83106037 0.56170725 0.72533651


> 
> 
> 
> cleanEx()
> nameEx("ModelMATCH")
> ### * ModelMATCH
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ModelMATCH
> ### Title: Match algorithm to detect TFBS in a sequence
> ### Aliases: ModelMATCH
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> data(TranscriptionFactor)
> iicc$method<-"MATCH"
> 
> iicc$pvalor<-0.8
> iicc$parameter<-60
> ModelMATCH(iicc)
           [,1]       [,2]       [,3]       [,4]
[1,] 0.24819021 3.25655635 0.06016732 0.06016732
[2,] 0.07466564 0.30799575 0.90428381 6.29680195
[3,] 1.52377350 0.24686833 0.41239308 0.03405080
[4,] 0.19206447 0.27485089 0.46801918 1.49825008
[5,] 0.06155903 3.33188262 0.06155903 0.40355366
[6,] 0.52704110 0.07269532 6.13063903 0.47655824
[7,] 2.78417111 0.05459159 0.05459159 0.66116482
[1] 4
[1] 5 4
> 
> 
> 
> cleanEx()
> nameEx("ModelPCA")
> ### * ModelPCA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ModelPCA
> ### Title: PCA model for a set of TFBS
> ### Aliases: ModelPCA
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> data(TranscriptionFactor)
> iicc$parameterIdeal<-2
> iicc$pvalor<-0.1
> iicc$parameter<-2
> TFBSmodel<-ModelPCA(iicc)
> 
> 
> 
> cleanEx()
> nameEx("Models")
> ### * Models
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Models
> ### Title: To create Detection Model
> ### Aliases: Models
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> iicc$parametersIdeal<-1
> Models(iicc)
$model
$model[[1]]
[1] 0.4854608 1.0582770 0.9530139 1.7487726 0.5907239 0.9132830 0.4854608

$model[[2]]
[1] 0.2974722 0.9530139 1.0869877 1.7229713 0.2974722 1.0582770 0.7742433

$model[[3]]
[1] 0.5907239 0.9132830 1.0869877 1.7229713 0.5907239 0.7742433 0.7742433

$model[[4]]
[1] 0.5907239 0.7742433 1.2319818 1.6393210 0.4854608 0.9530139 0.6292492


$parameterModel
$parameterModel$Order
[1] 1

$parameterModel$HXmax
[1] 1.832272

$parameterModel$ErrorHX
[1] 0.1183434

$parameterModel$Redundancy
[1] 0.83106037 0.56170725 0.46163752 0.08038924 0.83106037 0.56170725 0.72533651


> 
> 
> 
> cleanEx()
> nameEx("PredictDivergence")
> ### * PredictDivergence
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PredictDivergence
> ### Title: A set of functions for detection of Transcription Factor Binding
> ###   Sites by means of Divergence
> ### Aliases: PredictDivergence
> 
> ### ** Examples
> 
> require("MEET")
> data(HomoDivergence)
> iicc[["AP1"]]$threshold<-0.01
> PredictDivergence(iicc[["AP1"]])
==21311== Invalid write of size 1
==21311==    at 0x483BD5E: strcpy (/builddir/build/BUILD/valgrind-3.15.0/memcheck/../shared/vg_replace_strmem.c:513)
==21311==    by 0x1FE103FE: loopDIVERGENCE (packages/tests-vg/MEET/src/loopDIVERGENCE.c:60)
==21311==    by 0x49BB65: R_doDotCall (svn/R-devel/src/main/dotcode.c:645)
==21311==    by 0x49C184: do_dotcall (svn/R-devel/src/main/dotcode.c:1280)
==21311==    by 0x4D19CC: bcEval (svn/R-devel/src/main/eval.c:7054)
==21311==    by 0x4E8347: Rf_eval (svn/R-devel/src/main/eval.c:688)
==21311==    by 0x4E9F06: R_execClosure (svn/R-devel/src/main/eval.c:1853)
==21311==    by 0x4EACE3: Rf_applyClosure (svn/R-devel/src/main/eval.c:1779)
==21311==    by 0x4E8513: Rf_eval (svn/R-devel/src/main/eval.c:811)
==21311==    by 0x517E3C: Rf_ReplIteration (svn/R-devel/src/main/main.c:264)
==21311==    by 0x517E3C: Rf_ReplIteration (svn/R-devel/src/main/main.c:200)
==21311==    by 0x518177: R_ReplConsole (svn/R-devel/src/main/main.c:314)
==21311==    by 0x518204: run_Rmainloop (svn/R-devel/src/main/main.c:1113)
==21311==  Address 0x263f8292 is 2,354 bytes inside a block of size 7,960 alloc'd
==21311==    at 0x483880B: malloc (/builddir/build/BUILD/valgrind-3.15.0/coregrind/m_replacemalloc/vg_replace_malloc.c:309)
==21311==    by 0x5224E0: GetNewPage (svn/R-devel/src/main/memory.c:946)
==21311==    by 0x5242A9: Rf_allocVector3 (svn/R-devel/src/main/memory.c:2633)
==21311==    by 0x59E957: Rf_allocVector (svn/R-devel/src/include/Rinlinedfuns.h:593)
==21311==    by 0x59E957: do_subset2_dflt (svn/R-devel/src/main/subset.c:1080)
==21311==    by 0x59F14F: do_subset2 (svn/R-devel/src/main/subset.c:913)
==21311==    by 0x4E8794: Rf_eval (svn/R-devel/src/main/eval.c:763)
==21311==    by 0x4E8C5F: forcePromise (svn/R-devel/src/main/eval.c:516)
==21311==    by 0x4E8807: Rf_eval (svn/R-devel/src/main/eval.c:724)
==21311==    by 0x4ED390: R_forceAndCall (svn/R-devel/src/main/eval.c:1914)
==21311==    by 0x4298B5: do_lapply (svn/R-devel/src/main/apply.c:70)
==21311==    by 0x52733E: do_internal (svn/R-devel/src/main/names.c:1378)
==21311==    by 0x4D181F: bcEval (svn/R-devel/src/main/eval.c:7074)
==21311== 
==21311== Invalid read of size 1
==21311==    at 0x483CB90: strcmp (/builddir/build/BUILD/valgrind-3.15.0/memcheck/../shared/vg_replace_strmem.c:849)
==21311==    by 0x1FE0F5CC: getListElement (packages/tests-vg/MEET/src/getListElement.c:12)
==21311==    by 0x1FE1040A: loopDIVERGENCE (packages/tests-vg/MEET/src/loopDIVERGENCE.c:61)
==21311==    by 0x49BB65: R_doDotCall (svn/R-devel/src/main/dotcode.c:645)
==21311==    by 0x49C184: do_dotcall (svn/R-devel/src/main/dotcode.c:1280)
==21311==    by 0x4D19CC: bcEval (svn/R-devel/src/main/eval.c:7054)
==21311==    by 0x4E8347: Rf_eval (svn/R-devel/src/main/eval.c:688)
==21311==    by 0x4E9F06: R_execClosure (svn/R-devel/src/main/eval.c:1853)
==21311==    by 0x4EACE3: Rf_applyClosure (svn/R-devel/src/main/eval.c:1779)
==21311==    by 0x4E8513: Rf_eval (svn/R-devel/src/main/eval.c:811)
==21311==    by 0x517E3C: Rf_ReplIteration (svn/R-devel/src/main/main.c:264)
==21311==    by 0x517E3C: Rf_ReplIteration (svn/R-devel/src/main/main.c:200)
==21311==    by 0x518177: R_ReplConsole (svn/R-devel/src/main/main.c:314)
==21311==  Address 0x263f8292 is 2,354 bytes inside a block of size 7,960 alloc'd
==21311==    at 0x483880B: malloc (/builddir/build/BUILD/valgrind-3.15.0/coregrind/m_replacemalloc/vg_replace_malloc.c:309)
==21311==    by 0x5224E0: GetNewPage (svn/R-devel/src/main/memory.c:946)
==21311==    by 0x5242A9: Rf_allocVector3 (svn/R-devel/src/main/memory.c:2633)
==21311==    by 0x59E957: Rf_allocVector (svn/R-devel/src/include/Rinlinedfuns.h:593)
==21311==    by 0x59E957: do_subset2_dflt (svn/R-devel/src/main/subset.c:1080)
==21311==    by 0x59F14F: do_subset2 (svn/R-devel/src/main/subset.c:913)
==21311==    by 0x4E8794: Rf_eval (svn/R-devel/src/main/eval.c:763)
==21311==    by 0x4E8C5F: forcePromise (svn/R-devel/src/main/eval.c:516)
==21311==    by 0x4E8807: Rf_eval (svn/R-devel/src/main/eval.c:724)
==21311==    by 0x4ED390: R_forceAndCall (svn/R-devel/src/main/eval.c:1914)
==21311==    by 0x4298B5: do_lapply (svn/R-devel/src/main/apply.c:70)
==21311==    by 0x52733E: do_internal (svn/R-devel/src/main/names.c:1378)
==21311==    by 0x4D181F: bcEval (svn/R-devel/src/main/eval.c:7074)
==21311== 
Run time Divergence Detection 1.585 
   Position Value       Direction Sequence  
1  "282"    "0"         "f"       "TAAAAAAA"
2  "838"    "0.0006689" "f"       "AACGGGAG"
3  "463"    "0.0013378" "f"       "AACCACCG"
4  "1372"   "0.0020067" "f"       "ACCCTGGC"
5  "1194"   "0.0026756" "f"       "ACTCTATC"
6  "1346"   "0.0033445" "f"       "AACGTGAG"
7  "20"     "0.0040134" "f"       "ACCATGGG"
8  "517"    "0.0046823" "f"       "TCCCTGGC"
9  "244"    "0.0053512" "f"       "CAAGACCA"
10 "647"    "0.0060201" "f"       "CACGAGAC"
11 "300"    "0.006689"  "f"       "ACCCGGGC"
12 "432"    "0.0073579" "f"       "AGCGAGAC"
13 "1099"   "0.0080268" "f"       "ACCAAGAG"
14 "296"    "0.0086957" "f"       "TGCTACCC"
15 "450"    "0.0093645" "f"       "AAAGAAAA"
16 "456"    "0.0093645" "f"       "AAAGAAAA"
> 
> 
> 
> cleanEx()
> nameEx("PredictEntropy")
> ### * PredictEntropy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PredictEntropy
> ### Title: PredictEntropy: Detection of Transcription Factor Binding Sites
> ###   by means of Renyi entropy
> ### Aliases: PredictEntropy
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> PredictEntropy(iicc)
run tine correction entropy 0.079 
Run time Entropy Detection 0.276 
Run time Entropy Detection 0.271 
   Position Value       Direction Sequence  
1  "501"    "0"         "f"       "TGACTCAG"
2  "442"    "0.0006689" "f"       "TGTCTCAG"
3  "76"     "0.0013378" "f"       "TGAGTAAA"
4  "797"    "0.0013378" "f"       "TGAGTAAG"
5  "176"    "0.0026756" "f"       "TGGCTCAC"
6  "699"    "0.0033445" "f"       "TTTCTCAG"
7  "634"    "0.0040134" "f"       "TGCGTGAA"
8  "387"    "0.0046823" "f"       "TCAGTGAG"
9  "703"    "0.0046823" "f"       "TCAGTGAC"
10 "54"     "0.0060201" "f"       "TCTGTAAG"
11 "144"    "0.006689"  "f"       "TTAGTGAC"
12 "41"     "0.0073579" "f"       "TCTCTGAG"
13 "612"    "0.0080268" "f"       "TGATTGGC"
14 "1121"   "0.0086957" "f"       "TGAGTGGC"
15 "793"    "0.0093645" "f"       "TCCATGAG"
16 "720"    "0.0100334" "f"       "TGCTTAGG"
17 "225"    "0.0107023" "f"       "TCTCTCGA"
18 "832"    "0.0113712" "f"       "TGGTTGAA"
19 "1047"   "0.0120401" "f"       "TGTCGCAA"
20 "569"    "0.012709"  "f"       "AGCCTCAC"
21 "109"    "0.0133779" "f"       "TGTGGCAC"
22 "1246"   "0.0140468" "f"       "TTTGTGAT"
23 "1175"   "0.0147157" "f"       "TGGATGAA"
24 "846"    "0.0153846" "f"       "TGCAGCAC"
25 "1145"   "0.0160535" "f"       "TGGTTCGG"
26 "627"    "0.0167224" "f"       "TGCGTGGT"
27 "1045"   "0.0167224" "f"       "TTTGTCGC"
28 "1250"   "0.0180602" "f"       "TGATTCCA"
29 "750"    "0.0187291" "f"       "TCTCTAGG"
30 "72"     "0.019398"  "f"       "AGATTGAG"
31 "1117"   "0.0200669" "f"       "TCGGTGAG"
32 "585"    "0.0207358" "f"       "TGCTTCTC"
33 "6"      "0.0214047" "f"       "TTGATAAT"
34 "357"    "0.0220736" "f"       "TCGCTCGA"
35 "95"     "0.0227425" "f"       "TTCTTAGA"
36 "517"    "0.0227425" "f"       "TCCCTGGC"
37 "1232"   "0.0240803" "f"       "TGGTTTAG"
38 "1059"   "0.0247492" "f"       "TATGTGAC"
39 "872"    "0.0254181" "f"       "TGACTATG"
40 "1494"   "0.026087"  "f"       "AGGTTCAT"
41 "1360"   "0.0267559" "f"       "AGGGTCAA"
42 "940"    "0.0274247" "f"       "TGTGGGAC"
43 "1415"   "0.0280936" "f"       "TTGATGAG"
44 "64"     "0.0287625" "f"       "TGTATTGA"
45 "24"     "0.0294314" "f"       "TGGGTGGG"
46 "375"    "0.0294314" "f"       "TTGGTCGG"
47 "733"    "0.0307692" "f"       "TGCGGCGG"
48 "1228"   "0.0314381" "f"       "TTCGTGGT"
49 "766"    "0.032107"  "f"       "TGGATGGT"
50 "814"    "0.0327759" "f"       "TTAGGAAA"
51 "1236"   "0.0327759" "f"       "TTAGGAAG"
52 "893"    "0.0341137" "f"       "TTATTCTC"
53 "9"      "0.0347826" "f"       "ATAATAAT"
54 "383"    "0.0354515" "f"       "GGCCTCAG"
55 "131"    "0.0361204" "f"       "TGCCACAT"
56 "1277"   "0.0367893" "f"       "TGTGCCAG"
57 "1337"   "0.0374582" "f"       "TCCGGAAC"
58 "1416"   "0.0381271" "f"       "TGATGAGA"
59 "697"    "0.038796"  "f"       "TCTTTCTC"
60 "984"    "0.038796"  "f"       "TCTCTCCC"
61 "664"    "0.0401338" "f"       "ACTATAAG"
62 "124"    "0.0408027" "f"       "TCCAGAAT"
63 "1449"   "0.0414716" "f"       "TGCGTATG"
64 "1205"   "0.0421405" "f"       "TCCGTCCC"
65 "1196"   "0.0428094" "f"       "TCTATCTA"
66 "591"    "0.0434783" "f"       "TCAGCCAG"
67 "868"    "0.0441472" "f"       "ATACTGAC"
68 "973"    "0.0448161" "f"       "ACGCTCAC"
69 "1136"   "0.0454849" "f"       "TCCGGGAA"
70 "1155"   "0.0454849" "f"       "TCCGGGAG"
71 "616"    "0.0468227" "f"       "TGGCTCTG"
72 "319"    "0.0474916" "f"       "TGCCTGTA"
73 "746"    "0.0474916" "f"       "TTTTTCTC"
74 "748"    "0.0474916" "f"       "TTTCTCTA"
75 "1198"   "0.0494983" "f"       "TATCTAGT"
1  "995"    "0"         "r"       "CAGGCGCA"
2  "699"    "0.0006689" "r"       "TTTCTCAG"
3  "1420"   "0.0006689" "r"       "GAGAAGGA"
4  "1348"   "0.0020067" "r"       "CGTGAGGT"
5  "1352"   "0.0026756" "r"       "AGGTCTGG"
6  "793"    "0.0033445" "r"       "TCCATGAG"
7  "1109"   "0.0033445" "r"       "GAAGCTGA"
8  "260"    "0.0046823" "r"       "AACATGGC"
9  "682"    "0.0046823" "r"       "TGGCGCTT"
10 "1215"   "0.0046823" "r"       "AAATTTGG"
11 "1070"   "0.006689"  "r"       "GCGGAGAC"
12 "905"    "0.0073579" "r"       "GATCGGAT"
13 "650"    "0.0080268" "r"       "GAGACGCC"
14 "388"    "0.0086957" "r"       "CAGTGAGC"
15 "1050"   "0.0093645" "r"       "CGCAAAAC"
16 "1365"   "0.0100334" "r"       "CAAATTTA"
17 "87"     "0.0107023" "r"       "ATTTAAAA"
18 "1493"   "0.0113712" "r"       "GAGGTTCA"
19 "414"    "0.0120401" "r"       "CTCCAGCC"
20 "862"    "0.012709"  "r"       "GGGCAGAT"
21 "603"    "0.0133779" "r"       "CGCCTCTT"
22 "691"    "0.0140468" "r"       "CGCGCCTC"
23 "624"    "0.0147157" "r"       "ACGTGCGT"
24 "317"    "0.0153846" "r"       "TGTGCCTG"
25 "1451"   "0.0153846" "r"       "CGTATGGG"
26 "525"    "0.0167224" "r"       "CGGAAAAA"
27 "679"    "0.0173913" "r"       "CCGTGGCG"
28 "1054"   "0.0180602" "r"       "AAACTTAT"
29 "1320"   "0.0180602" "r"       "ACCAGGCG"
30 "246"    "0.019398"  "r"       "AGACCAGC"
31 "1214"   "0.0200669" "r"       "TAAATTTG"
32 "1358"   "0.0207358" "r"       "GGAGGGTC"
33 "1424"   "0.0214047" "r"       "AGGACCCA"
34 "1427"   "0.0220736" "r"       "ACCCACGG"
35 "519"    "0.0227425" "r"       "CCTGGCCG"
36 "1489"   "0.0234114" "r"       "TTGTGAGG"
37 "901"    "0.0240803" "r"       "GCGAGATC"
38 "399"    "0.0247492" "r"       "AATCACGC"
39 "703"    "0.0254181" "r"       "TCAGTGAC"
40 "834"    "0.026087"  "r"       "GTTGAACG"
41 "1387"   "0.0267559" "r"       "CGCAAGGC"
42 "449"    "0.0274247" "r"       "GAAAGAAA"
43 "1385"   "0.0274247" "r"       "TCCGCAAG"
44 "416"    "0.0287625" "r"       "CCAGCCTG"
45 "1254"   "0.0287625" "r"       "TCCAAAGC"
46 "1373"   "0.0301003" "r"       "CCCTGGCC"
47 "27"     "0.0307692" "r"       "GTGGGGGG"
48 "75"     "0.0307692" "r"       "TTGAGTAA"
49 "1014"   "0.0307692" "r"       "GGAGCCAA"
50 "1048"   "0.0307692" "r"       "GTCGCAAA"
51 "1221"   "0.0334448" "r"       "GGTACTAT"
52 "84"     "0.0341137" "r"       "TACATTTA"
53 "321"    "0.0347826" "r"       "CCTGTAAT"
54 "375"    "0.0347826" "r"       "TTGGTCGG"
55 "797"    "0.0361204" "r"       "TGAGTAAG"
56 "664"    "0.0367893" "r"       "ACTATAAG"
57 "884"    "0.0367893" "r"       "CGTTGGAG"
58 "911"    "0.0381271" "r"       "ATCTGGGC"
59 "628"    "0.038796"  "r"       "GCGTGGTG"
60 "1431"   "0.0394649" "r"       "ACGGCGTC"
61 "126"    "0.0401338" "r"       "CAGAATGC"
62 "341"    "0.0408027" "r"       "GGAGGAGG"
63 "360"    "0.0408027" "r"       "CTCGAGCC"
64 "1407"   "0.0408027" "r"       "GCTGACGC"
65 "243"    "0.0428094" "r"       "TCAAGACC"
66 "482"    "0.0434783" "r"       "AGAAGGAA"
67 "170"    "0.0441472" "r"       "GCGCGGTG"
68 "232"    "0.0448161" "r"       "AGGCCGGG"
69 "71"     "0.0454849" "r"       "AAGATTGA"
70 "970"    "0.0461538" "r"       "CTTACGCT"
71 "1038"   "0.0461538" "r"       "AGCTGGGT"
72 "1044"   "0.0461538" "r"       "GTTTGTCG"
73 "320"    "0.0481605" "r"       "GCCTGTAA"
74 "53"     "0.0488294" "r"       "CTCTGTAA"
75 "596"    "0.0488294" "r"       "CAGCCAAC"
> 
> 
> cleanEx()
> nameEx("PredictMATCH")
> ### * PredictMATCH
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PredictMATCH
> ### Title: MATCH algorithm to detect TFBS in a sequence
> ### Aliases: PredictMATCH
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> data(TranscriptionFactor)
> iicc$method<-"MATCH"
> iicc$parametersIdeal<-iicc$parameters
> iicc$model<-ModelMATCH(iicc)
           [,1]       [,2]       [,3]       [,4]
[1,] 0.24819021 3.25655635 0.06016732 0.06016732
[2,] 0.07466564 0.30799575 0.90428381 6.29680195
[3,] 1.52377350 0.24686833 0.41239308 0.03405080
[4,] 0.19206447 0.27485089 0.46801918 1.49825008
[5,] 0.06155903 3.33188262 0.06155903 0.40355366
[6,] 0.52704110 0.07269532 6.13063903 0.47655824
[7,] 2.78417111 0.05459159 0.05459159 0.66116482
[1] 4
[1] 5 4
> DetectedSequences<-PredictMATCH(iicc)
> 
> 
> 
> 
> cleanEx()
> nameEx("PredictPCA")
> ### * PredictPCA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PredictPCA
> ### Title: Q-residuals detection of TFBS, using a principal components
> ###   model
> ### Aliases: PredictPCA
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> data(TranscriptionFactor)
> iicc$method<-"PCA"
> iicc$pvalor<-0.1
> iicc$parameterIdeal<-2
> iicc$model<-ModelPCA(iicc)
> DetectedSequences<-PredictPCA(iicc)
> 
> 
> 
> cleanEx()
> nameEx("Prediction")
> ### * Prediction
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Prediction
> ### Title: To detect Transcription Factor Binding sites by means of a model
> ### Aliases: Prediction
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> Prediction(iicc)
run tine correction entropy 0.079 
Run time Entropy Detection 0.266 
Run time Entropy Detection 0.263 
   Position Value       Direction Sequence  
1  "501"    "0"         "f"       "TGACTCAG"
2  "442"    "0.0006689" "f"       "TGTCTCAG"
3  "76"     "0.0013378" "f"       "TGAGTAAA"
4  "797"    "0.0013378" "f"       "TGAGTAAG"
5  "176"    "0.0026756" "f"       "TGGCTCAC"
6  "699"    "0.0033445" "f"       "TTTCTCAG"
7  "634"    "0.0040134" "f"       "TGCGTGAA"
8  "387"    "0.0046823" "f"       "TCAGTGAG"
9  "703"    "0.0046823" "f"       "TCAGTGAC"
10 "54"     "0.0060201" "f"       "TCTGTAAG"
11 "144"    "0.006689"  "f"       "TTAGTGAC"
12 "41"     "0.0073579" "f"       "TCTCTGAG"
13 "612"    "0.0080268" "f"       "TGATTGGC"
14 "1121"   "0.0086957" "f"       "TGAGTGGC"
15 "793"    "0.0093645" "f"       "TCCATGAG"
16 "720"    "0.0100334" "f"       "TGCTTAGG"
17 "225"    "0.0107023" "f"       "TCTCTCGA"
18 "832"    "0.0113712" "f"       "TGGTTGAA"
19 "1047"   "0.0120401" "f"       "TGTCGCAA"
20 "569"    "0.012709"  "f"       "AGCCTCAC"
21 "109"    "0.0133779" "f"       "TGTGGCAC"
22 "1246"   "0.0140468" "f"       "TTTGTGAT"
23 "1175"   "0.0147157" "f"       "TGGATGAA"
24 "846"    "0.0153846" "f"       "TGCAGCAC"
25 "1145"   "0.0160535" "f"       "TGGTTCGG"
26 "627"    "0.0167224" "f"       "TGCGTGGT"
27 "1045"   "0.0167224" "f"       "TTTGTCGC"
28 "1250"   "0.0180602" "f"       "TGATTCCA"
29 "750"    "0.0187291" "f"       "TCTCTAGG"
30 "72"     "0.019398"  "f"       "AGATTGAG"
31 "1117"   "0.0200669" "f"       "TCGGTGAG"
32 "585"    "0.0207358" "f"       "TGCTTCTC"
33 "6"      "0.0214047" "f"       "TTGATAAT"
34 "357"    "0.0220736" "f"       "TCGCTCGA"
35 "95"     "0.0227425" "f"       "TTCTTAGA"
36 "517"    "0.0227425" "f"       "TCCCTGGC"
37 "1232"   "0.0240803" "f"       "TGGTTTAG"
38 "1059"   "0.0247492" "f"       "TATGTGAC"
39 "872"    "0.0254181" "f"       "TGACTATG"
40 "1494"   "0.026087"  "f"       "AGGTTCAT"
41 "1360"   "0.0267559" "f"       "AGGGTCAA"
42 "940"    "0.0274247" "f"       "TGTGGGAC"
43 "1415"   "0.0280936" "f"       "TTGATGAG"
44 "64"     "0.0287625" "f"       "TGTATTGA"
45 "24"     "0.0294314" "f"       "TGGGTGGG"
46 "375"    "0.0294314" "f"       "TTGGTCGG"
47 "733"    "0.0307692" "f"       "TGCGGCGG"
48 "1228"   "0.0314381" "f"       "TTCGTGGT"
49 "766"    "0.032107"  "f"       "TGGATGGT"
50 "814"    "0.0327759" "f"       "TTAGGAAA"
51 "1236"   "0.0327759" "f"       "TTAGGAAG"
52 "893"    "0.0341137" "f"       "TTATTCTC"
53 "9"      "0.0347826" "f"       "ATAATAAT"
54 "383"    "0.0354515" "f"       "GGCCTCAG"
55 "131"    "0.0361204" "f"       "TGCCACAT"
56 "1277"   "0.0367893" "f"       "TGTGCCAG"
57 "1337"   "0.0374582" "f"       "TCCGGAAC"
58 "1416"   "0.0381271" "f"       "TGATGAGA"
59 "697"    "0.038796"  "f"       "TCTTTCTC"
60 "984"    "0.038796"  "f"       "TCTCTCCC"
61 "664"    "0.0401338" "f"       "ACTATAAG"
62 "124"    "0.0408027" "f"       "TCCAGAAT"
63 "1449"   "0.0414716" "f"       "TGCGTATG"
64 "1205"   "0.0421405" "f"       "TCCGTCCC"
65 "1196"   "0.0428094" "f"       "TCTATCTA"
66 "591"    "0.0434783" "f"       "TCAGCCAG"
67 "868"    "0.0441472" "f"       "ATACTGAC"
68 "973"    "0.0448161" "f"       "ACGCTCAC"
69 "1136"   "0.0454849" "f"       "TCCGGGAA"
70 "1155"   "0.0454849" "f"       "TCCGGGAG"
71 "616"    "0.0468227" "f"       "TGGCTCTG"
72 "319"    "0.0474916" "f"       "TGCCTGTA"
73 "746"    "0.0474916" "f"       "TTTTTCTC"
74 "748"    "0.0474916" "f"       "TTTCTCTA"
75 "1198"   "0.0494983" "f"       "TATCTAGT"
1  "995"    "0"         "r"       "CAGGCGCA"
2  "699"    "0.0006689" "r"       "TTTCTCAG"
3  "1420"   "0.0006689" "r"       "GAGAAGGA"
4  "1348"   "0.0020067" "r"       "CGTGAGGT"
5  "1352"   "0.0026756" "r"       "AGGTCTGG"
6  "793"    "0.0033445" "r"       "TCCATGAG"
7  "1109"   "0.0033445" "r"       "GAAGCTGA"
8  "260"    "0.0046823" "r"       "AACATGGC"
9  "682"    "0.0046823" "r"       "TGGCGCTT"
10 "1215"   "0.0046823" "r"       "AAATTTGG"
11 "1070"   "0.006689"  "r"       "GCGGAGAC"
12 "905"    "0.0073579" "r"       "GATCGGAT"
13 "650"    "0.0080268" "r"       "GAGACGCC"
14 "388"    "0.0086957" "r"       "CAGTGAGC"
15 "1050"   "0.0093645" "r"       "CGCAAAAC"
16 "1365"   "0.0100334" "r"       "CAAATTTA"
17 "87"     "0.0107023" "r"       "ATTTAAAA"
18 "1493"   "0.0113712" "r"       "GAGGTTCA"
19 "414"    "0.0120401" "r"       "CTCCAGCC"
20 "862"    "0.012709"  "r"       "GGGCAGAT"
21 "603"    "0.0133779" "r"       "CGCCTCTT"
22 "691"    "0.0140468" "r"       "CGCGCCTC"
23 "624"    "0.0147157" "r"       "ACGTGCGT"
24 "317"    "0.0153846" "r"       "TGTGCCTG"
25 "1451"   "0.0153846" "r"       "CGTATGGG"
26 "525"    "0.0167224" "r"       "CGGAAAAA"
27 "679"    "0.0173913" "r"       "CCGTGGCG"
28 "1054"   "0.0180602" "r"       "AAACTTAT"
29 "1320"   "0.0180602" "r"       "ACCAGGCG"
30 "246"    "0.019398"  "r"       "AGACCAGC"
31 "1214"   "0.0200669" "r"       "TAAATTTG"
32 "1358"   "0.0207358" "r"       "GGAGGGTC"
33 "1424"   "0.0214047" "r"       "AGGACCCA"
34 "1427"   "0.0220736" "r"       "ACCCACGG"
35 "519"    "0.0227425" "r"       "CCTGGCCG"
36 "1489"   "0.0234114" "r"       "TTGTGAGG"
37 "901"    "0.0240803" "r"       "GCGAGATC"
38 "399"    "0.0247492" "r"       "AATCACGC"
39 "703"    "0.0254181" "r"       "TCAGTGAC"
40 "834"    "0.026087"  "r"       "GTTGAACG"
41 "1387"   "0.0267559" "r"       "CGCAAGGC"
42 "449"    "0.0274247" "r"       "GAAAGAAA"
43 "1385"   "0.0274247" "r"       "TCCGCAAG"
44 "416"    "0.0287625" "r"       "CCAGCCTG"
45 "1254"   "0.0287625" "r"       "TCCAAAGC"
46 "1373"   "0.0301003" "r"       "CCCTGGCC"
47 "27"     "0.0307692" "r"       "GTGGGGGG"
48 "75"     "0.0307692" "r"       "TTGAGTAA"
49 "1014"   "0.0307692" "r"       "GGAGCCAA"
50 "1048"   "0.0307692" "r"       "GTCGCAAA"
51 "1221"   "0.0334448" "r"       "GGTACTAT"
52 "84"     "0.0341137" "r"       "TACATTTA"
53 "321"    "0.0347826" "r"       "CCTGTAAT"
54 "375"    "0.0347826" "r"       "TTGGTCGG"
55 "797"    "0.0361204" "r"       "TGAGTAAG"
56 "664"    "0.0367893" "r"       "ACTATAAG"
57 "884"    "0.0367893" "r"       "CGTTGGAG"
58 "911"    "0.0381271" "r"       "ATCTGGGC"
59 "628"    "0.038796"  "r"       "GCGTGGTG"
60 "1431"   "0.0394649" "r"       "ACGGCGTC"
61 "126"    "0.0401338" "r"       "CAGAATGC"
62 "341"    "0.0408027" "r"       "GGAGGAGG"
63 "360"    "0.0408027" "r"       "CTCGAGCC"
64 "1407"   "0.0408027" "r"       "GCTGACGC"
65 "243"    "0.0428094" "r"       "TCAAGACC"
66 "482"    "0.0434783" "r"       "AGAAGGAA"
67 "170"    "0.0441472" "r"       "GCGCGGTG"
68 "232"    "0.0448161" "r"       "AGGCCGGG"
69 "71"     "0.0454849" "r"       "AAGATTGA"
70 "970"    "0.0461538" "r"       "CTTACGCT"
71 "1038"   "0.0461538" "r"       "AGCTGGGT"
72 "1044"   "0.0461538" "r"       "GTTTGTCG"
73 "320"    "0.0481605" "r"       "GCCTGTAA"
74 "53"     "0.0488294" "r"       "CTCTGTAA"
75 "596"    "0.0488294" "r"       "CAGCCAAC"
> 
> 
> 
> cleanEx()
> nameEx("QresidualsDROSOPHILA")
> ### * QresidualsDROSOPHILA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: QresidualsDROSOPHILA
> ### Title: QresidualsDROSOPHILA: Given a Transcription factor chooses the
> ###   model for a specific organism and method.
> ### Aliases: QresidualsDROSOPHILA
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> QresidualsDROSOPHILA(nameTF=iicc$nameTF)
[1] "Transcription Factor not included"
An object of class "Model"
Slot "model":
[[1]]
[1] "numeric"


> 
> 
> cleanEx()
> nameEx("QresidualsHOMO")
> ### * QresidualsHOMO
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: QresidualsHOMO
> ### Title: QresidualsHOMO: Given a Transcription factor chooses the model
> ###   for a specific organism and method.
> ### Aliases: QresidualsHOMO
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> QresidualsHOMO(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
svd calculated PCA
Importance of component(s):
                 PC1    PC2    PC3    PC4
R2            0.2844 0.1813 0.1520 0.1218
Cumulative R2 0.2844 0.4657 0.6178 0.7396
21 	Variables
18 	Samples
0 	NAs ( 0 %)
4 	Calculated component(s)
Data was mean centered before running PCA 
Data was NOT scaled before running PCA 
Scores structure:
[1] 18  4
Loadings structure:
[1] 21  4

$parametersModel
$parametersModel$NumericalMatrix
        [,1]       [,2]          [,3]
A  0.0000000  0.0000000  1.000000e+00
C -0.4714045  0.8164966 -3.333333e-01
G -0.4714045 -0.8164966 -3.333333e-01
T  0.9428090  0.0000000 -3.333333e-01
-  0.0000000  0.0000000  2.775558e-17
a  0.0000000  0.0000000  1.000000e+00
c -0.4714045  0.8164966 -3.333333e-01
g -0.4714045 -0.8164966 -3.333333e-01
t  0.9428090  0.0000000 -3.333333e-01

$parametersModel$JacksonPars
$parametersModel$JacksonPars$h0
[1] 0.2709839

$parametersModel$JacksonPars$x1
[1] 0.5124868

$parametersModel$JacksonPars$x2
[1] -0.9622048

$parametersModel$JacksonPars$x3
[1] 5.965727


$parametersModel$ncolTFBS
[1] 7



> 
> 
> cleanEx()
> nameEx("QresidualsMUS")
> ### * QresidualsMUS
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: QresidualsMUS
> ### Title: QresidualsMUS: Given a Transcription factor chooses the model
> ###   for a specific organism and method.
> ### Aliases: QresidualsMUS
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> QresidualsMUS(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
svd calculated PCA
Importance of component(s):
                 PC1    PC2    PC3    PC4
R2            0.2844 0.1813 0.1520 0.1218
Cumulative R2 0.2844 0.4657 0.6178 0.7396
21 	Variables
18 	Samples
0 	NAs ( 0 %)
4 	Calculated component(s)
Data was mean centered before running PCA 
Data was NOT scaled before running PCA 
Scores structure:
[1] 18  4
Loadings structure:
[1] 21  4

$parametersModel
$parametersModel$NumericalMatrix
        [,1]       [,2]          [,3]
A  0.0000000  0.0000000  1.000000e+00
C -0.4714045  0.8164966 -3.333333e-01
G -0.4714045 -0.8164966 -3.333333e-01
T  0.9428090  0.0000000 -3.333333e-01
-  0.0000000  0.0000000  2.775558e-17
a  0.0000000  0.0000000  1.000000e+00
c -0.4714045  0.8164966 -3.333333e-01
g -0.4714045 -0.8164966 -3.333333e-01
t  0.9428090  0.0000000 -3.333333e-01

$parametersModel$JacksonPars
$parametersModel$JacksonPars$h0
[1] 0.2709839

$parametersModel$JacksonPars$x1
[1] 0.5124868

$parametersModel$JacksonPars$x2
[1] -0.9622048

$parametersModel$JacksonPars$x3
[1] 5.965727


$parametersModel$ncolTFBS
[1] 7



> 
> 
> cleanEx()
> nameEx("QresidualsRATTUS")
> ### * QresidualsRATTUS
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: QresidualsRATTUS
> ### Title: QresidualsRATTUS: Given a Transcription factor chooses the model
> ###   for a specific organism and method.
> ### Aliases: QresidualsRATTUS
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> QresidualsRATTUS(nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
svd calculated PCA
Importance of component(s):
                 PC1    PC2    PC3    PC4
R2            0.2844 0.1813 0.1520 0.1218
Cumulative R2 0.2844 0.4657 0.6178 0.7396
21 	Variables
18 	Samples
0 	NAs ( 0 %)
4 	Calculated component(s)
Data was mean centered before running PCA 
Data was NOT scaled before running PCA 
Scores structure:
[1] 18  4
Loadings structure:
[1] 21  4

$parametersModel
$parametersModel$NumericalMatrix
        [,1]       [,2]          [,3]
A  0.0000000  0.0000000  1.000000e+00
C -0.4714045  0.8164966 -3.333333e-01
G -0.4714045 -0.8164966 -3.333333e-01
T  0.9428090  0.0000000 -3.333333e-01
-  0.0000000  0.0000000  2.775558e-17
a  0.0000000  0.0000000  1.000000e+00
c -0.4714045  0.8164966 -3.333333e-01
g -0.4714045 -0.8164966 -3.333333e-01
t  0.9428090  0.0000000 -3.333333e-01

$parametersModel$JacksonPars
$parametersModel$JacksonPars$h0
[1] 0.2709839

$parametersModel$JacksonPars$x1
[1] 0.5124868

$parametersModel$JacksonPars$x2
[1] -0.9622048

$parametersModel$JacksonPars$x3
[1] 5.965727


$parametersModel$ncolTFBS
[1] 7



> 
> 
> cleanEx()
> nameEx("QtoJackson")
> ### * QtoJackson
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: QtoJackson
> ### Title: Q to Jackson: transform a Q-residual into a confidence interval
> ### Aliases: QtoJackson
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> data(iicc)
> nPCs<-1
> TFBS<-iicc$Transcriptionfactor
> Prob<-iicc$background
> missing<-iicc$missing
> NumericalMatrix<-numericalDNA(Prob)
> suma<-apply(TFBS,2,function(y){sum(y=="-")})
> threshold<-floor(nrow(TFBS)*missing/100)
> TFBS<-TFBS[, suma<=threshold]
> ncolTFBS<-ncol(TFBS)
> TFBSnum<-apply(TFBS,1,function(x){as.vector(t(NumericalMatrix[x,]))})
> TFBSnum<-t(TFBSnum)
> model<-pca(TFBSnum, nPcs=2, method="svd", center=TRUE)
> JacksonPars<-JacksonParameters(nPCs,TFBSnum)
> Qres<-3.45
> confidence<-QtoJackson(Qres,h0=1,x1=1,x2=1,x3=1)
> 
> 
> 
> cleanEx()
> nameEx("Read.aligned")
> ### * Read.aligned
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Read.aligned
> ### Title: Read nucleotide sequences
> ### Aliases: Read.aligned
> 
> ### ** Examples
> 
> require("MEET")
> pathMEET <- system.file("sequences", package = "MEET")
> Read.aligned(file=paste(pathMEET, "AP1.fa", sep = "/"))
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 
> 
> 
> 
> cleanEx()
> nameEx("chooseModel")
> ### * chooseModel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: chooseModel
> ### Title: ChooseModel: Choose the best model
> ### Aliases: chooseModel
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> detection(iicc)
run tine correction entropy 0.079 
Run time Entropy Detection 0.274 
Run time Entropy Detection 0.278 
   Position Value       Direction Sequence  
1  "501"    "0"         "f"       "TGACTCAG"
2  "442"    "0.0006689" "f"       "TGTCTCAG"
3  "76"     "0.0013378" "f"       "TGAGTAAA"
4  "797"    "0.0013378" "f"       "TGAGTAAG"
5  "176"    "0.0026756" "f"       "TGGCTCAC"
6  "699"    "0.0033445" "f"       "TTTCTCAG"
7  "634"    "0.0040134" "f"       "TGCGTGAA"
8  "387"    "0.0046823" "f"       "TCAGTGAG"
9  "703"    "0.0046823" "f"       "TCAGTGAC"
10 "54"     "0.0060201" "f"       "TCTGTAAG"
11 "144"    "0.006689"  "f"       "TTAGTGAC"
12 "41"     "0.0073579" "f"       "TCTCTGAG"
13 "612"    "0.0080268" "f"       "TGATTGGC"
14 "1121"   "0.0086957" "f"       "TGAGTGGC"
15 "793"    "0.0093645" "f"       "TCCATGAG"
16 "720"    "0.0100334" "f"       "TGCTTAGG"
17 "225"    "0.0107023" "f"       "TCTCTCGA"
18 "832"    "0.0113712" "f"       "TGGTTGAA"
19 "1047"   "0.0120401" "f"       "TGTCGCAA"
20 "569"    "0.012709"  "f"       "AGCCTCAC"
21 "109"    "0.0133779" "f"       "TGTGGCAC"
22 "1246"   "0.0140468" "f"       "TTTGTGAT"
23 "1175"   "0.0147157" "f"       "TGGATGAA"
24 "846"    "0.0153846" "f"       "TGCAGCAC"
25 "1145"   "0.0160535" "f"       "TGGTTCGG"
26 "627"    "0.0167224" "f"       "TGCGTGGT"
27 "1045"   "0.0167224" "f"       "TTTGTCGC"
28 "1250"   "0.0180602" "f"       "TGATTCCA"
29 "750"    "0.0187291" "f"       "TCTCTAGG"
30 "72"     "0.019398"  "f"       "AGATTGAG"
31 "1117"   "0.0200669" "f"       "TCGGTGAG"
32 "585"    "0.0207358" "f"       "TGCTTCTC"
33 "6"      "0.0214047" "f"       "TTGATAAT"
34 "357"    "0.0220736" "f"       "TCGCTCGA"
35 "95"     "0.0227425" "f"       "TTCTTAGA"
36 "517"    "0.0227425" "f"       "TCCCTGGC"
37 "1232"   "0.0240803" "f"       "TGGTTTAG"
38 "1059"   "0.0247492" "f"       "TATGTGAC"
39 "872"    "0.0254181" "f"       "TGACTATG"
40 "1494"   "0.026087"  "f"       "AGGTTCAT"
41 "1360"   "0.0267559" "f"       "AGGGTCAA"
42 "940"    "0.0274247" "f"       "TGTGGGAC"
43 "1415"   "0.0280936" "f"       "TTGATGAG"
44 "64"     "0.0287625" "f"       "TGTATTGA"
45 "24"     "0.0294314" "f"       "TGGGTGGG"
46 "375"    "0.0294314" "f"       "TTGGTCGG"
47 "733"    "0.0307692" "f"       "TGCGGCGG"
48 "1228"   "0.0314381" "f"       "TTCGTGGT"
49 "766"    "0.032107"  "f"       "TGGATGGT"
50 "814"    "0.0327759" "f"       "TTAGGAAA"
51 "1236"   "0.0327759" "f"       "TTAGGAAG"
52 "893"    "0.0341137" "f"       "TTATTCTC"
53 "9"      "0.0347826" "f"       "ATAATAAT"
54 "383"    "0.0354515" "f"       "GGCCTCAG"
55 "131"    "0.0361204" "f"       "TGCCACAT"
56 "1277"   "0.0367893" "f"       "TGTGCCAG"
57 "1337"   "0.0374582" "f"       "TCCGGAAC"
58 "1416"   "0.0381271" "f"       "TGATGAGA"
59 "697"    "0.038796"  "f"       "TCTTTCTC"
60 "984"    "0.038796"  "f"       "TCTCTCCC"
61 "664"    "0.0401338" "f"       "ACTATAAG"
62 "124"    "0.0408027" "f"       "TCCAGAAT"
63 "1449"   "0.0414716" "f"       "TGCGTATG"
64 "1205"   "0.0421405" "f"       "TCCGTCCC"
65 "1196"   "0.0428094" "f"       "TCTATCTA"
66 "591"    "0.0434783" "f"       "TCAGCCAG"
67 "868"    "0.0441472" "f"       "ATACTGAC"
68 "973"    "0.0448161" "f"       "ACGCTCAC"
69 "1136"   "0.0454849" "f"       "TCCGGGAA"
70 "1155"   "0.0454849" "f"       "TCCGGGAG"
71 "616"    "0.0468227" "f"       "TGGCTCTG"
72 "319"    "0.0474916" "f"       "TGCCTGTA"
73 "746"    "0.0474916" "f"       "TTTTTCTC"
74 "748"    "0.0474916" "f"       "TTTCTCTA"
75 "1198"   "0.0494983" "f"       "TATCTAGT"
1  "995"    "0"         "r"       "CAGGCGCA"
2  "699"    "0.0006689" "r"       "TTTCTCAG"
3  "1420"   "0.0006689" "r"       "GAGAAGGA"
4  "1348"   "0.0020067" "r"       "CGTGAGGT"
5  "1352"   "0.0026756" "r"       "AGGTCTGG"
6  "793"    "0.0033445" "r"       "TCCATGAG"
7  "1109"   "0.0033445" "r"       "GAAGCTGA"
8  "260"    "0.0046823" "r"       "AACATGGC"
9  "682"    "0.0046823" "r"       "TGGCGCTT"
10 "1215"   "0.0046823" "r"       "AAATTTGG"
11 "1070"   "0.006689"  "r"       "GCGGAGAC"
12 "905"    "0.0073579" "r"       "GATCGGAT"
13 "650"    "0.0080268" "r"       "GAGACGCC"
14 "388"    "0.0086957" "r"       "CAGTGAGC"
15 "1050"   "0.0093645" "r"       "CGCAAAAC"
16 "1365"   "0.0100334" "r"       "CAAATTTA"
17 "87"     "0.0107023" "r"       "ATTTAAAA"
18 "1493"   "0.0113712" "r"       "GAGGTTCA"
19 "414"    "0.0120401" "r"       "CTCCAGCC"
20 "862"    "0.012709"  "r"       "GGGCAGAT"
21 "603"    "0.0133779" "r"       "CGCCTCTT"
22 "691"    "0.0140468" "r"       "CGCGCCTC"
23 "624"    "0.0147157" "r"       "ACGTGCGT"
24 "317"    "0.0153846" "r"       "TGTGCCTG"
25 "1451"   "0.0153846" "r"       "CGTATGGG"
26 "525"    "0.0167224" "r"       "CGGAAAAA"
27 "679"    "0.0173913" "r"       "CCGTGGCG"
28 "1054"   "0.0180602" "r"       "AAACTTAT"
29 "1320"   "0.0180602" "r"       "ACCAGGCG"
30 "246"    "0.019398"  "r"       "AGACCAGC"
31 "1214"   "0.0200669" "r"       "TAAATTTG"
32 "1358"   "0.0207358" "r"       "GGAGGGTC"
33 "1424"   "0.0214047" "r"       "AGGACCCA"
34 "1427"   "0.0220736" "r"       "ACCCACGG"
35 "519"    "0.0227425" "r"       "CCTGGCCG"
36 "1489"   "0.0234114" "r"       "TTGTGAGG"
37 "901"    "0.0240803" "r"       "GCGAGATC"
38 "399"    "0.0247492" "r"       "AATCACGC"
39 "703"    "0.0254181" "r"       "TCAGTGAC"
40 "834"    "0.026087"  "r"       "GTTGAACG"
41 "1387"   "0.0267559" "r"       "CGCAAGGC"
42 "449"    "0.0274247" "r"       "GAAAGAAA"
43 "1385"   "0.0274247" "r"       "TCCGCAAG"
44 "416"    "0.0287625" "r"       "CCAGCCTG"
45 "1254"   "0.0287625" "r"       "TCCAAAGC"
46 "1373"   "0.0301003" "r"       "CCCTGGCC"
47 "27"     "0.0307692" "r"       "GTGGGGGG"
48 "75"     "0.0307692" "r"       "TTGAGTAA"
49 "1014"   "0.0307692" "r"       "GGAGCCAA"
50 "1048"   "0.0307692" "r"       "GTCGCAAA"
51 "1221"   "0.0334448" "r"       "GGTACTAT"
52 "84"     "0.0341137" "r"       "TACATTTA"
53 "321"    "0.0347826" "r"       "CCTGTAAT"
54 "375"    "0.0347826" "r"       "TTGGTCGG"
55 "797"    "0.0361204" "r"       "TGAGTAAG"
56 "664"    "0.0367893" "r"       "ACTATAAG"
57 "884"    "0.0367893" "r"       "CGTTGGAG"
58 "911"    "0.0381271" "r"       "ATCTGGGC"
59 "628"    "0.038796"  "r"       "GCGTGGTG"
60 "1431"   "0.0394649" "r"       "ACGGCGTC"
61 "126"    "0.0401338" "r"       "CAGAATGC"
62 "341"    "0.0408027" "r"       "GGAGGAGG"
63 "360"    "0.0408027" "r"       "CTCGAGCC"
64 "1407"   "0.0408027" "r"       "GCTGACGC"
65 "243"    "0.0428094" "r"       "TCAAGACC"
66 "482"    "0.0434783" "r"       "AGAAGGAA"
67 "170"    "0.0441472" "r"       "GCGCGGTG"
68 "232"    "0.0448161" "r"       "AGGCCGGG"
69 "71"     "0.0454849" "r"       "AAGATTGA"
70 "970"    "0.0461538" "r"       "CTTACGCT"
71 "1038"   "0.0461538" "r"       "AGCTGGGT"
72 "1044"   "0.0461538" "r"       "GTTTGTCG"
73 "320"    "0.0481605" "r"       "GCCTGTAA"
74 "53"     "0.0488294" "r"       "CTCTGTAA"
75 "596"    "0.0488294" "r"       "CAGCCAAC"
> 
> 
> cleanEx()
> nameEx("classMODEL")
> ### * classMODEL
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: classMODEL
> ### Title: classMODEL: To choose the model
> ### Aliases: classMODEL
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> classMODEL(org=iicc$organism,method=iicc$method,nameTF=iicc$nameTF)
An object of class "Model"
Slot "model":
$model
$model[[1]]
[1] 0.4854608 1.0582770 0.9530139 1.7487726 0.5907239 0.9132830 0.4854608

$model[[2]]
[1] 0.2974722 0.9530139 1.0869877 1.7229713 0.2974722 1.0582770 0.7742433

$model[[3]]
[1] 0.5907239 0.9132830 1.0869877 1.7229713 0.5907239 0.7742433 0.7742433

$model[[4]]
[1] 0.5907239 0.7742433 1.2319818 1.6393210 0.4854608 0.9530139 0.6292492


$parameterModel
$parameterModel$Order
[1] 1

$parameterModel$HXmax
[1] 1.848238

$parameterModel$ErrorHX
[1] 0.1235761

$parameterModel$Redundancy
[1] 0.83251974 0.56549341 0.46628813 0.08833324 0.83251974 0.56549341 0.72770918


$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 


> 
> 
> cleanEx()
> nameEx("correction.entropy")
> ### * correction.entropy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: correction.entropy
> ### Title: Correction entropy from the Finite Sample Size Effect
> ### Aliases: correction.entropy
> 
> ### ** Examples
> 
> data(TranscriptionFactor)
> data(iicc)
> data(BackgroundOrganism)
> data(RenyiOrder)
> correction.entropy(q,p=nrow(TranscriptionFactor),long=1,iicc)
An object of class "correction"
Slot "Herror":
 [1] 0.000000 1.449200 1.876551 1.904745 1.884194 1.859775 1.840366 1.826996
 [9] 1.818757 1.814429 1.812927 1.813400 1.815214 1.817911 1.821162 1.824739
[17] 1.828479 1.832272 1.836044 1.839742

Slot "sderror":
 [1] 0.0000000 0.6177535 0.2944388 0.2082640 0.1628245 0.1396826 0.1296737
 [8] 0.1264132 0.1260184 0.1264794 0.1268829 0.1268755 0.1263695 0.1253940
[15] 0.1240224 0.1223391 0.1204232 0.1183434 0.1161561 0.1139063

> 
> 
> cleanEx()
> nameEx("correction.redundancy")
> ### * correction.redundancy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: correction.redundancy
> ### Title: Correction redundancy from the Finite Sample Size Effect
> ### Aliases: correction.redundancy
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> data(RenyiOrder)
> data(iicc)
> Factortrans<-TranscriptionFactor
> correction<-correction.entropy(q,p=nrow(Factortrans),long=1,iicc)
> Herror<-slot(correction,"Herror")
> HXmax<-iicc$HXmax
> correction.redundancy(r=1,HXmax,Herror,finite=nrow(TranscriptionFactor))
[1] 1
> 
> 
> cleanEx()
> nameEx("correctionaprox")
> ### * correctionaprox
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: correctionaprox
> ### Title: Correction Entropy Approximate from the Finite Sample Size
> ###   Effect.
> ### Aliases: correctionaprox
> 
> ### ** Examples
> 
> require("MEET")
> data(RenyiOrder)
> data(iicc)
> s<-4
> correctionaprox(x=iicc$HXmax,matriu=iicc$TranscriptionFactor,s) 
numeric(0)
> 
> 
> 
> cleanEx()
> nameEx("detection")
> ### * detection
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: detection
> ### Title: Detection: A set of functions for detection of TFBS
> ### Aliases: detection
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> detection(iicc)
run tine correction entropy 0.079 
Run time Entropy Detection 0.272 
Run time Entropy Detection 0.265 
   Position Value       Direction Sequence  
1  "501"    "0"         "f"       "TGACTCAG"
2  "442"    "0.0006689" "f"       "TGTCTCAG"
3  "76"     "0.0013378" "f"       "TGAGTAAA"
4  "797"    "0.0013378" "f"       "TGAGTAAG"
5  "176"    "0.0026756" "f"       "TGGCTCAC"
6  "699"    "0.0033445" "f"       "TTTCTCAG"
7  "634"    "0.0040134" "f"       "TGCGTGAA"
8  "387"    "0.0046823" "f"       "TCAGTGAG"
9  "703"    "0.0046823" "f"       "TCAGTGAC"
10 "54"     "0.0060201" "f"       "TCTGTAAG"
11 "144"    "0.006689"  "f"       "TTAGTGAC"
12 "41"     "0.0073579" "f"       "TCTCTGAG"
13 "612"    "0.0080268" "f"       "TGATTGGC"
14 "1121"   "0.0086957" "f"       "TGAGTGGC"
15 "793"    "0.0093645" "f"       "TCCATGAG"
16 "720"    "0.0100334" "f"       "TGCTTAGG"
17 "225"    "0.0107023" "f"       "TCTCTCGA"
18 "832"    "0.0113712" "f"       "TGGTTGAA"
19 "1047"   "0.0120401" "f"       "TGTCGCAA"
20 "569"    "0.012709"  "f"       "AGCCTCAC"
21 "109"    "0.0133779" "f"       "TGTGGCAC"
22 "1246"   "0.0140468" "f"       "TTTGTGAT"
23 "1175"   "0.0147157" "f"       "TGGATGAA"
24 "846"    "0.0153846" "f"       "TGCAGCAC"
25 "1145"   "0.0160535" "f"       "TGGTTCGG"
26 "627"    "0.0167224" "f"       "TGCGTGGT"
27 "1045"   "0.0167224" "f"       "TTTGTCGC"
28 "1250"   "0.0180602" "f"       "TGATTCCA"
29 "750"    "0.0187291" "f"       "TCTCTAGG"
30 "72"     "0.019398"  "f"       "AGATTGAG"
31 "1117"   "0.0200669" "f"       "TCGGTGAG"
32 "585"    "0.0207358" "f"       "TGCTTCTC"
33 "6"      "0.0214047" "f"       "TTGATAAT"
34 "357"    "0.0220736" "f"       "TCGCTCGA"
35 "95"     "0.0227425" "f"       "TTCTTAGA"
36 "517"    "0.0227425" "f"       "TCCCTGGC"
37 "1232"   "0.0240803" "f"       "TGGTTTAG"
38 "1059"   "0.0247492" "f"       "TATGTGAC"
39 "872"    "0.0254181" "f"       "TGACTATG"
40 "1494"   "0.026087"  "f"       "AGGTTCAT"
41 "1360"   "0.0267559" "f"       "AGGGTCAA"
42 "940"    "0.0274247" "f"       "TGTGGGAC"
43 "1415"   "0.0280936" "f"       "TTGATGAG"
44 "64"     "0.0287625" "f"       "TGTATTGA"
45 "24"     "0.0294314" "f"       "TGGGTGGG"
46 "375"    "0.0294314" "f"       "TTGGTCGG"
47 "733"    "0.0307692" "f"       "TGCGGCGG"
48 "1228"   "0.0314381" "f"       "TTCGTGGT"
49 "766"    "0.032107"  "f"       "TGGATGGT"
50 "814"    "0.0327759" "f"       "TTAGGAAA"
51 "1236"   "0.0327759" "f"       "TTAGGAAG"
52 "893"    "0.0341137" "f"       "TTATTCTC"
53 "9"      "0.0347826" "f"       "ATAATAAT"
54 "383"    "0.0354515" "f"       "GGCCTCAG"
55 "131"    "0.0361204" "f"       "TGCCACAT"
56 "1277"   "0.0367893" "f"       "TGTGCCAG"
57 "1337"   "0.0374582" "f"       "TCCGGAAC"
58 "1416"   "0.0381271" "f"       "TGATGAGA"
59 "697"    "0.038796"  "f"       "TCTTTCTC"
60 "984"    "0.038796"  "f"       "TCTCTCCC"
61 "664"    "0.0401338" "f"       "ACTATAAG"
62 "124"    "0.0408027" "f"       "TCCAGAAT"
63 "1449"   "0.0414716" "f"       "TGCGTATG"
64 "1205"   "0.0421405" "f"       "TCCGTCCC"
65 "1196"   "0.0428094" "f"       "TCTATCTA"
66 "591"    "0.0434783" "f"       "TCAGCCAG"
67 "868"    "0.0441472" "f"       "ATACTGAC"
68 "973"    "0.0448161" "f"       "ACGCTCAC"
69 "1136"   "0.0454849" "f"       "TCCGGGAA"
70 "1155"   "0.0454849" "f"       "TCCGGGAG"
71 "616"    "0.0468227" "f"       "TGGCTCTG"
72 "319"    "0.0474916" "f"       "TGCCTGTA"
73 "746"    "0.0474916" "f"       "TTTTTCTC"
74 "748"    "0.0474916" "f"       "TTTCTCTA"
75 "1198"   "0.0494983" "f"       "TATCTAGT"
1  "995"    "0"         "r"       "CAGGCGCA"
2  "699"    "0.0006689" "r"       "TTTCTCAG"
3  "1420"   "0.0006689" "r"       "GAGAAGGA"
4  "1348"   "0.0020067" "r"       "CGTGAGGT"
5  "1352"   "0.0026756" "r"       "AGGTCTGG"
6  "793"    "0.0033445" "r"       "TCCATGAG"
7  "1109"   "0.0033445" "r"       "GAAGCTGA"
8  "260"    "0.0046823" "r"       "AACATGGC"
9  "682"    "0.0046823" "r"       "TGGCGCTT"
10 "1215"   "0.0046823" "r"       "AAATTTGG"
11 "1070"   "0.006689"  "r"       "GCGGAGAC"
12 "905"    "0.0073579" "r"       "GATCGGAT"
13 "650"    "0.0080268" "r"       "GAGACGCC"
14 "388"    "0.0086957" "r"       "CAGTGAGC"
15 "1050"   "0.0093645" "r"       "CGCAAAAC"
16 "1365"   "0.0100334" "r"       "CAAATTTA"
17 "87"     "0.0107023" "r"       "ATTTAAAA"
18 "1493"   "0.0113712" "r"       "GAGGTTCA"
19 "414"    "0.0120401" "r"       "CTCCAGCC"
20 "862"    "0.012709"  "r"       "GGGCAGAT"
21 "603"    "0.0133779" "r"       "CGCCTCTT"
22 "691"    "0.0140468" "r"       "CGCGCCTC"
23 "624"    "0.0147157" "r"       "ACGTGCGT"
24 "317"    "0.0153846" "r"       "TGTGCCTG"
25 "1451"   "0.0153846" "r"       "CGTATGGG"
26 "525"    "0.0167224" "r"       "CGGAAAAA"
27 "679"    "0.0173913" "r"       "CCGTGGCG"
28 "1054"   "0.0180602" "r"       "AAACTTAT"
29 "1320"   "0.0180602" "r"       "ACCAGGCG"
30 "246"    "0.019398"  "r"       "AGACCAGC"
31 "1214"   "0.0200669" "r"       "TAAATTTG"
32 "1358"   "0.0207358" "r"       "GGAGGGTC"
33 "1424"   "0.0214047" "r"       "AGGACCCA"
34 "1427"   "0.0220736" "r"       "ACCCACGG"
35 "519"    "0.0227425" "r"       "CCTGGCCG"
36 "1489"   "0.0234114" "r"       "TTGTGAGG"
37 "901"    "0.0240803" "r"       "GCGAGATC"
38 "399"    "0.0247492" "r"       "AATCACGC"
39 "703"    "0.0254181" "r"       "TCAGTGAC"
40 "834"    "0.026087"  "r"       "GTTGAACG"
41 "1387"   "0.0267559" "r"       "CGCAAGGC"
42 "449"    "0.0274247" "r"       "GAAAGAAA"
43 "1385"   "0.0274247" "r"       "TCCGCAAG"
44 "416"    "0.0287625" "r"       "CCAGCCTG"
45 "1254"   "0.0287625" "r"       "TCCAAAGC"
46 "1373"   "0.0301003" "r"       "CCCTGGCC"
47 "27"     "0.0307692" "r"       "GTGGGGGG"
48 "75"     "0.0307692" "r"       "TTGAGTAA"
49 "1014"   "0.0307692" "r"       "GGAGCCAA"
50 "1048"   "0.0307692" "r"       "GTCGCAAA"
51 "1221"   "0.0334448" "r"       "GGTACTAT"
52 "84"     "0.0341137" "r"       "TACATTTA"
53 "321"    "0.0347826" "r"       "CCTGTAAT"
54 "375"    "0.0347826" "r"       "TTGGTCGG"
55 "797"    "0.0361204" "r"       "TGAGTAAG"
56 "664"    "0.0367893" "r"       "ACTATAAG"
57 "884"    "0.0367893" "r"       "CGTTGGAG"
58 "911"    "0.0381271" "r"       "ATCTGGGC"
59 "628"    "0.038796"  "r"       "GCGTGGTG"
60 "1431"   "0.0394649" "r"       "ACGGCGTC"
61 "126"    "0.0401338" "r"       "CAGAATGC"
62 "341"    "0.0408027" "r"       "GGAGGAGG"
63 "360"    "0.0408027" "r"       "CTCGAGCC"
64 "1407"   "0.0408027" "r"       "GCTGACGC"
65 "243"    "0.0428094" "r"       "TCAAGACC"
66 "482"    "0.0434783" "r"       "AGAAGGAA"
67 "170"    "0.0441472" "r"       "GCGCGGTG"
68 "232"    "0.0448161" "r"       "AGGCCGGG"
69 "71"     "0.0454849" "r"       "AAGATTGA"
70 "970"    "0.0461538" "r"       "CTTACGCT"
71 "1038"   "0.0461538" "r"       "AGCTGGGT"
72 "1044"   "0.0461538" "r"       "GTTTGTCG"
73 "320"    "0.0481605" "r"       "GCCTGTAA"
74 "53"     "0.0488294" "r"       "CTCTGTAA"
75 "596"    "0.0488294" "r"       "CAGCCAAC"
> 
> 
> cleanEx()
> nameEx("detector_1rOrdre_diff")
> ### * detector_1rOrdre_diff
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: detector_1rOrdre_diff
> ### Title: Detection of Transcription Factor Binding Sites Through
> ###   Differential Renyi Entropy
> ### Aliases: detector_1rOrdre_diff
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> test<-detector_1rOrdre_diff(training.set=iicc$Transcriptionfactor,val.set=NULL,iicc) 
> 
> 
> cleanEx()
> nameEx("detector_2nOrdre")
> ### * detector_2nOrdre
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: detector_2nOrdre
> ### Title: Detection of Transcription Factor Binding Sites Through
> ###   Parametric PredictDivergence
> ### Aliases: detector_2nOrdre
> 
> ### ** Examples
> 
> require("MEET")
> data(BackgroundOrganism)
> data(iicc)
> training.set<-iicc$Transcriptionfactor
> val.set<-sample(c('A','T','C','G'),ncol(training.set), replace=TRUE,Prob)
> iicc<-detector_2nOrdre_init(training.set, val.set, iicc)
> out<-detector_2nOrdre(training.set, val.set, iicc)
> 
> 
> 
> cleanEx()
> nameEx("detector_2nOrdre_init")
> ### * detector_2nOrdre_init
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: detector_2nOrdre_init
> ### Title: Detection of Transcription Factor Binding Sites Through
> ###   Parametric PredictDivergence
> ### Aliases: detector_2nOrdre_init
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> training.set<-TranscriptionFactor
> val.set<-sample(c('A','T','C','G'),ncol(TranscriptionFactor), replace=TRUE,Prob)
> detector_2nOrdre_init(training.set, val.set, iicc)
$mode
[1] "validation"

$method
[1] "Entropy"

$background
   A    T    C    G 
0.32 0.32 0.18 0.18 

$alignment
[1] "NONE"

$Transcriptionfactor
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,] "T"  "G"  "A"  "C"  "T"  "C"  "A" 
 [2,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [3,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [4,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [5,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
 [6,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [7,] "T"  "G"  "A"  "A"  "T"  "C"  "A" 
 [8,] "A"  "G"  "T"  "G"  "T"  "C"  "A" 
 [9,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[10,] "T"  "G"  "A"  "G"  "T"  "A"  "A" 
[11,] "T"  "G"  "A"  "T"  "T"  "C"  "A" 
[12,] "T"  "C"  "A"  "G"  "T"  "C"  "A" 
[13,] "T"  "G"  "C"  "G"  "T"  "C"  "A" 
[14,] "T"  "G"  "A"  "G"  "T"  "C"  "A" 
[15,] "T"  "G"  "A"  "C"  "G"  "C"  "G" 
[16,] "T"  "G"  "A"  "T"  "T"  "A"  "A" 
[17,] "T"  "T"  "C"  "C"  "T"  "G"  "G" 
[18,] "T"  "C"  "T"  "T"  "T"  "C"  "A" 

$nummotif
[1] 2

$lenmotif
[1] 9

$DNAreal
   [1] "T" "A" "T" "G" "T" "A" "A" "A" "C" "A" "C" "T" "T" "A" "T" "T" "T" "T"
  [19] "A" "T" "T" "G" "T" "G" "G" "T" "A" "A" "T" "A" "T" "T" "T" "T" "T" "T"
  [37] "G" "A" "T" "A" "A" "C" "A" "A" "C" "A" "C" "A" "T" "C" "T" "G" "A" "A"
  [55] "A" "C" "A" "A" "A" "A" "T" "A" "A" "T" "G" "C" "A" "A" "A" "G" "C" "C"
  [73] "G" "A" "A" "T" "A" "G" "T" "T" "A" "G" "G" "C" "T" "A" "A" "A" "A" "A"
  [91] "T" "G" "T" "A" "C" "T" "C" "T" "T" "A" "G" "A" "C" "A" "T" "T" "T" "A"
 [109] "A" "A" "A" "A" "G" "G" "T" "T" "T" "A" "T" "G" "A" "A" "T" "C" "C" "T"
 [127] "A" "T" "G" "G" "T" "A" "T" "T" "T" "A" "A" "T" "A" "T" "A" "T" "T" "A"
 [145] "A" "A" "G" "A" "A" "C" "G" "A" "A" "G" "T" "A" "A" "A" "T" "G" "G" "G"
 [163] "A" "A" "A" "A" "A" "A" "T" "G" "T" "G" "T" "A" "A" "A" "C" "A" "C" "T"
 [181] "A" "T" "A" "A" "G" "C" "G" "T" "G" "A" "T" "G" "A" "T" "A" "G" "A" "A"
 [199] "T" "T" "A" "T" "T" "A" "A" "T" "A" "T" "A" "A" "G" "A" "T" "G" "A" "T"
 [217] "G" "C" "C" "G" "T" "G" "C" "G" "T" "T" "T" "A" "C" "C" "A" "T" "A" "C"
 [235] "G" "A" "T" "T" "G" "C" "C" "A" "G" "C" "A" "A" "T" "A" "C" "G" "G" "T"
 [253] "G" "G" "A" "A" "A" "T" "A" "A" "A" "A" "A" "C" "A" "C" "T" "T" "A" "T"
 [271] "G" "C" "C" "A" "T" "T" "A" "T" "T" "G" "G" "T" "C" "A" "A" "C" "A" "G"
 [289] "A" "C" "C" "A" "T" "T" "G" "G" "C" "A" "A" "T" "A" "C" "C" "A" "A" "C"
 [307] "G" "T" "A" "G" "G" "T" "T" "G" "A" "G" "A" "T" "T" "T" "C" "T" "A" "A"
 [325] "A" "G" "A" "G" "G" "C" "A" "G" "T" "A" "C" "T" "A" "G" "A" "T" "C" "C"
 [343] "T" "A" "T" "T" "A" "T" "G" "C" "T" "A" "C" "T" "T" "G" "C" "T" "G" "G"
 [361] "T" "G" "T" "G" "C" "T" "A" "C" "G" "A" "G" "G" "C" "T" "G" "T" "T" "G"
 [379] "C" "G" "A" "C" "A" "T" "A" "G" "T" "A" "C" "T" "T" "A" "A" "C" "C" "C"
 [397] "G" "G" "A" "A" "G" "T" "T" "A" "T" "T" "A" "G" "A" "C" "T" "C" "T" "T"
 [415] "G" "G" "T" "G" "T" "T" "G" "C" "C" "A" "G" "T" "T" "T" "C" "G" "T" "C"
 [433] "A" "A" "T" "T" "G" "T" "T" "T" "A" "G" "T" "G" "A" "T" "A" "C" "A" "G"
 [451] "A" "A" "A" "C" "A" "A" "C" "A" "C" "T" "A" "C" "T" "G" "C" "T" "G" "T"
 [469] "G" "A" "C" "C" "A" "A" "T" "C" "A" "C" "A" "T" "C" "G" "G" "T" "C" "G"
 [487] "C" "G" "G" "A" "T" "C" "A" "G" "C" "A" "T" "G" "A" "T" "C" "C" "T" "A"
 [505] "T" "T" "G" "T" "T" "G" "C" "T" "T" "T" "G" "A" "A" "G" "A" "G" "G" "T"
 [523] "G" "A" "C" "T" "A" "C" "T" "G" "T" "T" "T" "T" "C" "G" "T" "C" "T" "C"
 [541] "A" "G" "C" "A" "G" "C" "T" "C" "C" "A" "G" "T" "A" "C" "T" "G" "G" "T"
 [559] "A" "G" "T" "T" "G" "T" "C" "T" "C" "A" "G" "C" "A" "G" "C" "T" "C" "C"
 [577] "A" "G" "T" "A" "T" "T" "G" "G" "T" "T" "G" "T" "T" "G" "T" "C" "T" "C"
 [595] "A" "C" "T" "G" "G" "T" "A" "G" "C" "A" "C" "T" "G" "T" "T" "C" "A" "T"
 [613] "T" "T" "T" "A" "G" "A" "G" "C" "T" "G" "A" "C" "A" "G" "A" "C" "T" "C"
 [631] "T" "T" "C" "A" "T" "T" "C" "G" "T" "A" "G" "T" "C" "T" "G" "T" "G" "G"
 [649] "C" "C" "T" "C" "C" "A" "T" "G" "T" "T" "G" "G" "A" "T" "A" "G" "A" "C"
 [667] "C" "G" "T" "A" "A" "C" "A" "A" "C" "A" "T" "C" "A" "T" "T" "C" "A" "C"
 [685] "A" "G" "T" "A" "G" "C" "C" "G" "T" "G" "G" "C" "C" "G" "T" "C" "G" "A"
 [703] "A" "A" "C" "A" "A" "T" "G" "G" "C" "A" "G" "G" "T" "G" "A" "A" "G" "C"
 [721] "A" "G" "T" "T" "T" "C" "G" "G" "A" "A" "C" "A" "C" "A" "C" "A" "C" "C"
 [739] "A" "G" "A" "T" "T" "C" "G" "C" "A" "G" "G" "A" "A" "G" "T" "A" "A" "C"
 [757] "A" "G" "T" "A" "A" "C" "T" "A" "G" "C" "G" "T" "A" "G" "T" "T" "T" "G"
 [775] "T" "T" "G" "C" "C" "T" "C" "G" "A" "T" "T" "C" "T" "G" "T" "G" "G" "T"
 [793] "G" "G" "A" "A" "A" "T" "A" "G" "G" "A" "C" "A" "C" "C" "A" "T" "G" "T"
 [811] "C" "G" "T" "G" "T" "A" "T" "T" "C" "T" "G" "T" "G" "G" "T" "A" "A" "C"
 [829] "G" "C" "C" "G" "T" "T" "A" "A" "T" "A" "G" "T" "A" "G" "C" "A" "G" "T"
 [847] "G" "C" "T" "T" "A" "T" "A" "G" "A" "T" "A" "C" "A" "A" "T" "G" "G" "C"
 [865] "T" "G" "G" "A" "G" "A" "A" "G" "C" "A" "G" "T" "C" "T" "T" "A" "G" "A"
 [883] "G" "C" "A" "T" "A" "C" "G" "T" "C" "A" "G" "A" "T" "T" "C" "A" "C" "A"
 [901] "A" "G" "A" "A" "G" "A" "A" "A" "T" "T" "G" "T" "A" "A" "C" "T" "A" "C"
 [919] "T" "G" "T" "G" "G" "T" "T" "T" "G" "C" "T" "C" "T" "G" "T" "T" "G" "T"
 [937] "C" "C" "C" "C" "T" "T" "G" "G" "T" "T" "T" "G" "C" "T" "T" "T" "G" "T"
 [955] "T" "G" "T" "C" "T" "C" "C" "G" "T" "A" "G" "T" "T" "T" "G" "C" "T" "T"
 [973] "T" "G" "T" "T" "A" "T" "C" "T" "C" "T" "G" "T" "G" "G" "T" "A" "G" "A"
 [991] "A" "A" "T" "A" "G" "G" "G" "C" "A" "C" "C" "A" "T" "G" "T" "G" "G" "T"
[1009] "A" "T" "A" "C" "T" "C" "T" "G" "T" "T" "G" "T" "G" "G" "C" "A" "C" "C"
[1027] "G" "C" "T" "A" "A" "C" "A" "G" "T" "A" "A" "C" "G" "G" "T" "G" "G" "C"
[1045] "C" "G" "T" "G" "G" "A"

$outsequence
[1] 20

$position
[1] 501

$missing
[1] 50

$vector
[1] 0.1 0.5 1.0 1.5 2.0

$gapopen
[1] -500

$maxiters
[1] 16

$gapextend
[1] -2

$outsequence
NULL

$parameters
[1] 1

$DNA
$DNA[[1]]
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  [19] "T" "A" "C" "C" "A" "T" "G" "G" "G" "T" "G" "G" "G" "G" "G" "G" "G" "C"
  [37] "A" "A" "C" "T" "T" "C" "T" "C" "T" "G" "A" "G" "A" "G" "T" "G" "C" "T"
  [55] "C" "T" "G" "T" "A" "A" "G" "T" "A" "T" "G" "T" "A" "T" "T" "G" "A" "A"
  [73] "G" "A" "T" "T" "G" "A" "G" "T" "A" "A" "A" "T" "A" "C" "A" "T" "T" "T"
  [91] "A" "A" "A" "A" "T" "T" "C" "T" "T" "A" "G" "A" "A" "C" "A" "G" "T" "A"
 [109] "T" "G" "T" "G" "G" "C" "A" "C" "A" "T" "A" "G" "C" "G" "T" "T" "C" "C"
 [127] "A" "G" "A" "A" "T" "G" "C" "C" "A" "C" "A" "T" "T" "A" "T" "T" "G" "T"
 [145] "T" "A" "G" "T" "G" "A" "C" "A" "G" "A" "A" "A" "T" "A" "A" "T" "C" "T"
 [163] "C" "G" "G" "C" "T" "G" "G" "G" "C" "G" "C" "G" "G" "T" "G" "G" "C" "T"
 [181] "C" "A" "C" "G" "C" "C" "T" "G" "T" "A" "A" "T" "C" "C" "C" "A" "G" "C"
 [199] "A" "C" "T" "T" "T" "G" "G" "G" "G" "G" "G" "G" "C" "C" "A" "C" "G" "G"
 [217] "C" "G" "G" "G" "A" "G" "G" "C" "T" "C" "T" "C" "T" "C" "G" "A" "G" "G"
 [235] "C" "C" "G" "G" "G" "A" "G" "T" "T" "C" "A" "A" "G" "A" "C" "C" "A" "G"
 [253] "C" "C" "T" "G" "G" "G" "C" "A" "A" "C" "A" "T" "G" "G" "C" "A" "A" "G"
 [271] "A" "C" "G" "C" "C" "G" "A" "C" "T" "G" "T" "T" "A" "A" "A" "A" "A" "A"
 [289] "A" "A" "A" "A" "A" "A" "A" "T" "G" "C" "T" "A" "C" "C" "C" "G" "G" "G"
 [307] "C" "G" "T" "C" "G" "T" "G" "G" "C" "G" "T" "G" "T" "G" "C" "C" "T" "G"
 [325] "T" "A" "A" "T" "C" "C" "C" "A" "G" "C" "T" "A" "C" "T" "G" "G" "G" "G"
 [343] "A" "G" "G" "A" "G" "G" "T" "G" "G" "G" "A" "G" "G" "A" "T" "C" "G" "C"
 [361] "T" "C" "G" "A" "G" "C" "C" "C" "G" "A" "G" "A" "G" "G" "T" "T" "G" "G"
 [379] "T" "C" "G" "G" "G" "G" "C" "C" "T" "C" "A" "G" "T" "G" "A" "G" "C" "C"
 [397] "G" "A" "A" "A" "T" "C" "A" "C" "G" "C" "C" "A" "C" "T" "G" "C" "A" "C"
 [415] "T" "C" "C" "A" "G" "C" "C" "T" "G" "G" "G" "C" "G" "A" "C" "G" "G" "A"
 [433] "G" "C" "G" "A" "G" "A" "C" "C" "C" "T" "G" "T" "C" "T" "C" "A" "G" "A"
 [451] "A" "A" "G" "A" "A" "A" "A" "A" "G" "A" "A" "A" "A" "A" "C" "C" "A" "C"
 [469] "C" "G" "T" "C" "C" "A" "G" "G" "G" "G" "C" "G" "G" "A" "G" "A" "A" "G"
 [487] "G" "A" "A" "G" "G" "T" "T" "C" "T" "C" "C" "C" "T" "A" "T" "G" "A" "C"
 [505] "T" "C" "A" "G" "G" "T" "T" "T" "C" "C" "A" "C" "T" "C" "C" "C" "T" "G"
 [523] "G" "C" "C" "G" "G" "A" "A" "A" "A" "A" "A" "C" "C" "T" "A" "G" "T" "C"
 [541] "C" "T" "C" "C" "C" "A" "G" "G" "T" "T" "A" "G" "C" "A" "C" "G" "C" "C"
 [559] "G" "C" "T" "C" "T" "A" "G" "C" "C" "C" "A" "G" "C" "C" "T" "C" "A" "C"
 [577] "G" "T" "C" "T" "C" "C" "A" "C" "T" "G" "C" "T" "T" "C" "T" "C" "A" "G"
 [595] "C" "C" "A" "G" "C" "C" "A" "A" "C" "G" "C" "C" "T" "C" "T" "T" "C" "T"
 [613] "G" "A" "T" "T" "G" "G" "C" "T" "C" "T" "G" "A" "C" "G" "T" "G" "C" "G"
 [631] "T" "G" "G" "T" "G" "C" "G" "T" "G" "A" "A" "A" "A" "C" "G" "T" "C" "A"
 [649] "C" "G" "A" "G" "A" "C" "G" "C" "C" "G" "G" "C" "G" "T" "T" "A" "C" "T"
 [667] "A" "T" "A" "A" "G" "A" "G" "C" "G" "C" "A" "G" "C" "C" "G" "T" "G" "G"
 [685] "C" "G" "C" "T" "T" "G" "C" "G" "C" "G" "C" "C" "T" "C" "T" "T" "T" "C"
 [703] "T" "C" "A" "G" "T" "G" "A" "C" "C" "G" "G" "G" "T" "G" "G" "T" "T" "T"
 [721] "G" "C" "T" "T" "A" "G" "G" "T" "G" "A" "G" "G" "T" "G" "C" "G" "G" "C"
 [739] "G" "G" "T" "G" "T" "G" "C" "T" "T" "T" "T" "T" "C" "T" "C" "T" "A" "G"
 [757] "G" "G" "T" "T" "T" "G" "G" "G" "T" "T" "G" "G" "A" "T" "G" "G" "T" "G"
 [775] "G" "C" "C" "C" "G" "G" "G" "C" "C" "T" "T" "C" "C" "G" "A" "G" "T" "T"
 [793] "T" "C" "C" "A" "T" "G" "A" "G" "T" "A" "A" "G" "C" "T" "A" "A" "A" "G"
 [811] "A" "C" "G" "T" "T" "A" "G" "G" "A" "A" "A" "C" "A" "G" "A" "G" "C" "A"
 [829] "G" "G" "G" "T" "G" "G" "T" "T" "G" "A" "A" "C" "G" "G" "G" "A" "G" "T"
 [847] "G" "C" "A" "G" "C" "A" "C" "G" "G" "T" "T" "G" "T" "G" "G" "G" "G" "G"
 [865] "C" "A" "G" "A" "T" "A" "C" "T" "G" "A" "C" "T" "A" "T" "G" "A" "G" "A"
 [883] "G" "C" "G" "T" "T" "G" "G" "A" "G" "G" "T" "T" "A" "T" "T" "C" "T" "C"
 [901] "G" "C" "G" "A" "G" "A" "T" "C" "G" "G" "A" "T" "C" "T" "G" "G" "G" "C"
 [919] "T" "C" "C" "G" "C" "G" "A" "G" "G" "T" "T" "T" "T" "G" "G" "C" "G" "T"
 [937] "A" "G" "T" "T" "G" "T" "G" "G" "G" "A" "C" "T" "G" "C" "G" "C" "A" "G"
 [955] "G" "C" "G" "C" "C" "G" "T" "T" "T" "G" "G" "A" "G" "C" "C" "C" "T" "T"
 [973] "A" "C" "G" "C" "T" "C" "A" "C" "A" "C" "T" "T" "C" "T" "C" "T" "C" "C"
 [991] "C" "G" "C" "G" "C" "A" "G" "G" "C" "G" "C" "A" "G" "A" "C" "G" "G" "G"
[1009] "G" "A" "A" "G" "C" "G" "G" "A" "G" "C" "C" "A" "A" "C" "A" "T" "G" "C"
[1027] "C" "A" "G" "T" "G" "G" "C" "C" "C" "G" "G" "A" "G" "C" "T" "G" "G" "G"
[1045] "T" "T" "T" "G" "T" "C" "G" "C" "A" "A" "A" "A" "C" "T" "T" "A" "T" "G"
[1063] "T" "G" "A" "C" "C" "C" "C" "G" "C" "G" "G" "A" "G" "A" "C" "C" "C" "T"
[1081] "T" "C" "G" "A" "G" "A" "A" "A" "T" "C" "T" "C" "G" "T" "C" "T" "C" "G"
[1099] "A" "C" "C" "A" "A" "G" "A" "G" "C" "T" "G" "A" "A" "G" "C" "T" "G" "A"
[1117] "T" "C" "G" "G" "T" "G" "A" "G" "T" "G" "G" "C" "C" "A" "A" "G" "G" "C"
[1135] "T" "T" "C" "C" "G" "G" "G" "A" "A" "G" "T" "G" "G" "T" "T" "C" "G" "G"
[1153] "C" "T" "T" "C" "C" "G" "G" "G" "A" "G" "G" "C" "G" "G" "T" "T" "A" "G"
[1171] "C" "A" "C" "G" "T" "G" "G" "A" "T" "G" "A" "A" "G" "G" "T" "G" "C" "C"
[1189] "C" "A" "T" "G" "T" "A" "C" "T" "C" "T" "A" "T" "C" "T" "A" "G" "T" "C"
[1207] "C" "G" "T" "C" "C" "C" "C" "T" "A" "A" "A" "T" "T" "T" "G" "G" "T" "A"
[1225] "C" "T" "A" "T" "T" "C" "G" "T" "G" "G" "T" "T" "T" "A" "G" "G" "A" "A"
[1243] "G" "G" "T" "T" "T" "T" "G" "T" "G" "A" "T" "T" "C" "C" "A" "A" "A" "G"
[1261] "C" "T" "G" "C" "C" "A" "G" "T" "C" "T" "A" "G" "T" "T" "G" "T" "T" "G"
[1279] "T" "G" "C" "C" "A" "G" "T" "A" "C" "G" "T" "G" "G" "G" "A" "C" "T" "A"
[1297] "C" "A" "C" "T" "T" "G" "T" "C" "C" "A" "C" "C" "C" "C" "C" "T" "T" "C"
[1315] "T" "C" "C" "C" "C" "A" "C" "C" "A" "G" "G" "C" "G" "A" "G" "T" "A" "T"
[1333] "G" "G" "G" "C" "T" "C" "C" "G" "G" "A" "A" "C" "A" "A" "A" "C" "G" "T"
[1351] "G" "A" "G" "G" "T" "C" "T" "G" "G" "A" "G" "G" "G" "T" "C" "A" "A" "A"
[1369] "T" "T" "T" "A" "C" "C" "C" "T" "G" "G" "C" "C" "A" "A" "G" "A" "T" "C"
[1387] "C" "G" "C" "A" "A" "G" "G" "C" "C" "G" "C" "C" "C" "G" "G" "G" "A" "A"
[1405] "C" "T" "G" "C" "T" "G" "A" "C" "G" "C" "T" "T" "G" "A" "T" "G" "A" "G"
[1423] "A" "A" "G" "G" "A" "C" "C" "C" "A" "C" "G" "G" "C" "G" "T" "C" "T" "G"
[1441] "T" "T" "C" "G" "A" "A" "G" "G" "T" "G" "C" "G" "T" "A" "T" "G" "G" "G"
[1459] "A" "G" "T" "C" "C" "A" "C" "A" "G" "C" "A" "G" "A" "G" "G" "G" "A" "T"
[1477] "G" "G" "G" "G" "T" "G" "C" "A" "G" "G" "G" "C" "T" "T" "G" "T" "G" "A"
[1495] "G" "G" "T" "T" "C" "A" "T"

$DNA[[2]]
   [1] "A" "T" "G" "A" "A" "C" "C" "T" "C" "A" "C" "A" "A" "G" "C" "C" "C" "T"
  [19] "G" "C" "A" "C" "C" "C" "C" "A" "T" "C" "C" "C" "T" "C" "T" "G" "C" "T"
  [37] "G" "T" "G" "G" "A" "C" "T" "C" "C" "C" "A" "T" "A" "C" "G" "C" "A" "C"
  [55] "C" "T" "T" "C" "G" "A" "A" "C" "A" "G" "A" "C" "G" "C" "C" "G" "T" "G"
  [73] "G" "G" "T" "C" "C" "T" "T" "C" "T" "C" "A" "T" "C" "A" "A" "G" "C" "G"
  [91] "T" "C" "A" "G" "C" "A" "G" "T" "T" "C" "C" "C" "G" "G" "G" "C" "G" "G"
 [109] "C" "C" "T" "T" "G" "C" "G" "G" "A" "T" "C" "T" "T" "G" "G" "C" "C" "A"
 [127] "G" "G" "G" "T" "A" "A" "A" "T" "T" "T" "G" "A" "C" "C" "C" "T" "C" "C"
 [145] "A" "G" "A" "C" "C" "T" "C" "A" "C" "G" "T" "T" "T" "G" "T" "T" "C" "C"
 [163] "G" "G" "A" "G" "C" "C" "C" "A" "T" "A" "C" "T" "C" "G" "C" "C" "T" "G"
 [181] "G" "T" "G" "G" "G" "G" "A" "G" "A" "A" "G" "G" "G" "G" "G" "T" "G" "G"
 [199] "A" "C" "A" "A" "G" "T" "G" "T" "A" "G" "T" "C" "C" "C" "A" "C" "G" "T"
 [217] "A" "C" "T" "G" "G" "C" "A" "C" "A" "A" "C" "A" "A" "C" "T" "A" "G" "A"
 [235] "C" "T" "G" "G" "C" "A" "G" "C" "T" "T" "T" "G" "G" "A" "A" "T" "C" "A"
 [253] "C" "A" "A" "A" "A" "C" "C" "T" "T" "C" "C" "T" "A" "A" "A" "C" "C" "A"
 [271] "C" "G" "A" "A" "T" "A" "G" "T" "A" "C" "C" "A" "A" "A" "T" "T" "T" "A"
 [289] "G" "G" "G" "G" "A" "C" "G" "G" "A" "C" "T" "A" "G" "A" "T" "A" "G" "A"
 [307] "G" "T" "A" "C" "A" "T" "G" "G" "G" "C" "A" "C" "C" "T" "T" "C" "A" "T"
 [325] "C" "C" "A" "C" "G" "T" "G" "C" "T" "A" "A" "C" "C" "G" "C" "C" "T" "C"
 [343] "C" "C" "G" "G" "A" "A" "G" "C" "C" "G" "A" "A" "C" "C" "A" "C" "T" "T"
 [361] "C" "C" "C" "G" "G" "A" "A" "G" "C" "C" "T" "T" "G" "G" "C" "C" "A" "C"
 [379] "T" "C" "A" "C" "C" "G" "A" "T" "C" "A" "G" "C" "T" "T" "C" "A" "G" "C"
 [397] "T" "C" "T" "T" "G" "G" "T" "C" "G" "A" "G" "A" "C" "G" "A" "G" "A" "T"
 [415] "T" "T" "C" "T" "C" "G" "A" "A" "G" "G" "G" "T" "C" "T" "C" "C" "G" "C"
 [433] "G" "G" "G" "G" "T" "C" "A" "C" "A" "T" "A" "A" "G" "T" "T" "T" "T" "G"
 [451] "C" "G" "A" "C" "A" "A" "A" "C" "C" "C" "A" "G" "C" "T" "C" "C" "G" "G"
 [469] "G" "C" "C" "A" "C" "T" "G" "G" "C" "A" "T" "G" "T" "T" "G" "G" "C" "T"
 [487] "C" "C" "G" "C" "T" "T" "C" "C" "C" "C" "G" "T" "C" "T" "G" "C" "G" "C"
 [505] "C" "T" "G" "C" "G" "C" "G" "G" "G" "A" "G" "A" "G" "A" "A" "G" "T" "G"
 [523] "T" "G" "A" "G" "C" "G" "T" "A" "A" "G" "G" "G" "C" "T" "C" "C" "A" "A"
 [541] "A" "C" "G" "G" "C" "G" "C" "C" "T" "G" "C" "G" "C" "A" "G" "T" "C" "C"
 [559] "C" "A" "C" "A" "A" "C" "T" "A" "C" "G" "C" "C" "A" "A" "A" "A" "C" "C"
 [577] "T" "C" "G" "C" "G" "G" "A" "G" "C" "C" "C" "A" "G" "A" "T" "C" "C" "G"
 [595] "A" "T" "C" "T" "C" "G" "C" "G" "A" "G" "A" "A" "T" "A" "A" "C" "C" "T"
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 [631] "G" "T" "A" "T" "C" "T" "G" "C" "C" "C" "C" "C" "A" "C" "A" "A" "C" "C"
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 [775] "C" "C" "T" "A" "A" "G" "C" "A" "A" "A" "C" "C" "A" "C" "C" "C" "G" "G"
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 [865] "C" "G" "C" "A" "C" "C" "A" "C" "G" "C" "A" "C" "G" "T" "C" "A" "G" "A"
 [883] "G" "C" "C" "A" "A" "T" "C" "A" "G" "A" "A" "G" "A" "G" "G" "C" "G" "T"
 [901] "T" "G" "G" "C" "T" "G" "G" "C" "T" "G" "A" "G" "A" "A" "G" "C" "A" "G"
 [919] "T" "G" "G" "A" "G" "A" "C" "G" "T" "G" "A" "G" "G" "C" "T" "G" "G" "G"
 [937] "C" "T" "A" "G" "A" "G" "C" "G" "G" "C" "G" "T" "G" "C" "T" "A" "A" "C"
 [955] "C" "T" "G" "G" "G" "A" "G" "G" "A" "C" "T" "A" "G" "G" "T" "T" "T" "T"
 [973] "T" "T" "C" "C" "G" "G" "C" "C" "A" "G" "G" "G" "A" "G" "T" "G" "G" "A"
 [991] "A" "A" "C" "C" "T" "G" "A" "G" "T" "C" "A" "T" "A" "G" "G" "G" "A" "G"
[1009] "A" "A" "C" "C" "T" "T" "C" "C" "T" "T" "C" "T" "C" "C" "G" "C" "C" "C"
[1027] "C" "T" "G" "G" "A" "C" "G" "G" "T" "G" "G" "T" "T" "T" "T" "T" "C" "T"
[1045] "T" "T" "T" "T" "C" "T" "T" "T" "C" "T" "G" "A" "G" "A" "C" "A" "G" "G"
[1063] "G" "T" "C" "T" "C" "G" "C" "T" "C" "C" "G" "T" "C" "G" "C" "C" "C" "A"
[1081] "G" "G" "C" "T" "G" "G" "A" "G" "T" "G" "C" "A" "G" "T" "G" "G" "C" "G"
[1099] "T" "G" "A" "T" "T" "T" "C" "G" "G" "C" "T" "C" "A" "C" "T" "G" "A" "G"
[1117] "G" "C" "C" "C" "C" "G" "A" "C" "C" "A" "A" "C" "C" "T" "C" "T" "C" "G"
[1135] "G" "G" "C" "T" "C" "G" "A" "G" "C" "G" "A" "T" "C" "C" "T" "C" "C" "C"
[1153] "A" "C" "C" "T" "C" "C" "T" "C" "C" "C" "C" "A" "G" "T" "A" "G" "C" "T"
[1171] "G" "G" "G" "A" "T" "T" "A" "C" "A" "G" "G" "C" "A" "C" "A" "C" "G" "C"
[1189] "C" "A" "C" "G" "A" "C" "G" "C" "C" "C" "G" "G" "G" "T" "A" "G" "C" "A"
[1207] "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "T" "A" "A" "C" "A" "G"
[1225] "T" "C" "G" "G" "C" "G" "T" "C" "T" "T" "G" "C" "C" "A" "T" "G" "T" "T"
[1243] "G" "C" "C" "C" "A" "G" "G" "C" "T" "G" "G" "T" "C" "T" "T" "G" "A" "A"
[1261] "C" "T" "C" "C" "C" "G" "G" "C" "C" "T" "C" "G" "A" "G" "A" "G" "A" "G"
[1279] "C" "C" "T" "C" "C" "C" "G" "C" "C" "G" "T" "G" "G" "C" "C" "C" "C" "C"
[1297] "C" "C" "A" "A" "A" "G" "T" "G" "C" "T" "G" "G" "G" "A" "T" "T" "A" "C"
[1315] "A" "G" "G" "C" "G" "T" "G" "A" "G" "C" "C" "A" "C" "C" "G" "C" "G" "C"
[1333] "C" "C" "A" "G" "C" "C" "G" "A" "G" "A" "T" "T" "A" "T" "T" "T" "C" "T"
[1351] "G" "T" "C" "A" "C" "T" "A" "A" "C" "A" "A" "T" "A" "A" "T" "G" "T" "G"
[1369] "G" "C" "A" "T" "T" "C" "T" "G" "G" "A" "A" "C" "G" "C" "T" "A" "T" "G"
[1387] "T" "G" "C" "C" "A" "C" "A" "T" "A" "C" "T" "G" "T" "T" "C" "T" "A" "A"
[1405] "G" "A" "A" "T" "T" "T" "T" "A" "A" "A" "T" "G" "T" "A" "T" "T" "T" "A"
[1423] "C" "T" "C" "A" "A" "T" "C" "T" "T" "C" "A" "A" "T" "A" "C" "A" "T" "A"
[1441] "C" "T" "T" "A" "C" "A" "G" "A" "G" "C" "A" "C" "T" "C" "T" "C" "A" "G"
[1459] "A" "G" "A" "A" "G" "T" "T" "G" "C" "C" "C" "C" "C" "C" "C" "A" "C" "C"
[1477] "C" "A" "T" "G" "G" "T" "A" "C" "T" "A" "T" "T" "A" "T" "T" "A" "T" "C"
[1495] "A" "A" "T" "A" "G" "C" "C"


$pvalor
[1] 0.05

$MDscan
[1] "MDscan/MDscan.mac"

$call.meme
[1] "/Users/janmaynou/bin/meme"

$call.mast
[1] "/Users/janmaynou/bin/mast"

$call.transfac2meme
[1] "/Users/janmaynou/bin/transfac2meme"

$call.clustalw
[1] "/Applications/clustalw-2.0.12-macosx/clustalw2"

$call.muscle
[1] "/Applications/muscle"

$direction
[1] "b"

$organism
[1] "Homo sapiens"

$nameTF
[1] "AP1"

$model
$model$model
$model$model[[1]]
[1] 0.4854608 1.0582770 0.9530139 1.7487726 0.5907239 0.9132830 0.4854608

$model$model[[2]]
[1] 0.2974722 0.9530139 1.0869877 1.7229713 0.2974722 1.0582770 0.7742433

$model$model[[3]]
[1] 0.5907239 0.9132830 1.0869877 1.7229713 0.5907239 0.7742433 0.7742433

$model$model[[4]]
[1] 0.5907239 0.7742433 1.2319818 1.6393210 0.4854608 0.9530139 0.6292492


$model$parameterModel
$model$parameterModel$Order
[1] 1

$model$parameterModel$HXmax
[1] 1.848238

$model$parameterModel$ErrorHX
[1] 0.1235761

$model$parameterModel$Redundancy
[1] 0.83251974 0.56549341 0.46628813 0.08833324 0.83251974 0.56549341 0.72770918



$missing.fun
[1] TRUE

$q
[1] 1

$classentropy
[1] "Shannon"

$Redundancia_corregida
[1] 0.83251974 0.56549341 0.46628813 0.08833324 0.83251974 0.56549341 0.72770918

$ErrorHX
[1] 0.1235761

$Entropy
$Entropy[[1]]
[1] 0.4854608 1.0582770 0.9530139 1.7487726 0.5907239 0.9132830 0.4854608

$Entropy[[2]]
[1] 0.2974722 0.9530139 1.0869877 1.7229713 0.2974722 1.0582770 0.7742433

$Entropy[[3]]
[1] 0.5907239 0.9132830 1.0869877 1.7229713 0.5907239 0.7742433 0.7742433

$Entropy[[4]]
[1] 0.5907239 0.7742433 1.2319818 1.6393210 0.4854608 0.9530139 0.6292492


$threshold
[1] 0.05

$q
[1] 1

$probparella
 [1] 0.1024 0.1024 0.0576 0.0576 0.1024 0.1024 0.0576 0.0576 0.0576 0.0576
[11] 0.0324 0.0324 0.0576 0.0576 0.0324 0.0324

$D
           [,1]      [,2]      [,3]     [,4] [,5] [,6] [,7] [,8]      [,9]
 [1,] 1.3689956 0.0000000 0.1985001 0.000000    0    0    0    0 0.0000000
 [2,] 0.0000000 1.3527242 0.1772354 0.000000    0    0    0    0 0.0000000
 [3,] 0.1985001 0.1772354 1.5954618 0.000000    0    0    0    0 0.0000000
 [4,] 0.0000000 0.0000000 0.0000000 0.286397    0    0    0    0 0.0000000
 [5,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [6,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [7,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [8,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [9,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.5689956
[10,] 0.3130248 0.0000000 0.2394911 0.000000    0    0    0    0 0.0000000
[11,] 0.4262579 0.1722422 0.3772354 0.000000    0    0    0    0 0.1710878
[12,] 0.5080315 0.2540158 0.3344978 0.000000    0    0    0    0 0.1935068
          [,10]     [,11]     [,12]
 [1,] 0.3130248 0.4262579 0.5080315
 [2,] 0.0000000 0.1722422 0.2540158
 [3,] 0.2394911 0.3772354 0.3344978
 [4,] 0.0000000 0.0000000 0.0000000
 [5,] 0.0000000 0.0000000 0.0000000
 [6,] 0.0000000 0.0000000 0.0000000
 [7,] 0.0000000 0.0000000 0.0000000
 [8,] 0.0000000 0.0000000 0.0000000
 [9,] 0.0000000 0.1710878 0.1935068
[10,] 1.6854753 0.6232197 0.9049933
[11,] 0.6232197 1.8397421 0.6477309
[12,] 0.9049933 0.6477309 1.8397421

$correction_1rOrdre
An object of class "correction"
Slot "Herror":
 [1] 0.000000 1.449200 1.876551 1.904745 1.884194 1.859775 1.840366 1.826996
 [9] 1.818757 1.814429 1.812927 1.813400 1.815214 1.817911 1.821162 1.824739
[17] 1.828479 1.832272 1.836044 1.839742

Slot "sderror":
 [1] 0.0000000 0.6177535 0.2944388 0.2082640 0.1628245 0.1396826 0.1296737
 [8] 0.1264132 0.1260184 0.1264794 0.1268829 0.1268755 0.1263695 0.1253940
[15] 0.1240224 0.1223391 0.1204232 0.1183434 0.1161561 0.1139063


$correction_1rOrdre_val
An object of class "correction"
Slot "Herror":
 [1] 0.000000 1.449200 1.876551 1.904745 1.884194 1.859775 1.840366 1.826996
 [9] 1.818757 1.814429 1.812927 1.813400 1.815214 1.817911 1.821162 1.824739
[17] 1.828479 1.832272 1.836044 1.839742 1.843335

Slot "sderror":
 [1] 0.0000000 0.6177535 0.2944388 0.2082640 0.1628245 0.1396826 0.1296737
 [8] 0.1264132 0.1260184 0.1264794 0.1268829 0.1268755 0.1263695 0.1253940
[15] 0.1240224 0.1223391 0.1204232 0.1183434 0.1161561 0.1139063 0.1116292


$Mperfil
           [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]
 [1,] 0.2558764 0.2602610 0.1843235 0.4648048 0.5058422 0.5058422 0.5058422
 [2,] 0.2602610 0.2647208 0.1874821 0.4727696 0.5145102 0.5145102 0.5145102
 [3,] 0.1843235 0.1874821 0.1327796 0.3348276 0.3643894 0.3643894 0.3643894
 [4,] 0.4648048 0.4727696 0.3348276 0.8443277 0.9188730 0.9188730 0.9188730
 [5,] 0.5058422 0.5145102 0.3643894 0.9188730 1.0000000 1.0000000 1.0000000
 [6,] 0.5058422 0.5145102 0.3643894 0.9188730 1.0000000 1.0000000 1.0000000
 [7,] 0.5058422 0.5145102 0.3643894 0.9188730 1.0000000 1.0000000 1.0000000
 [8,] 0.5058422 0.5145102 0.3643894 0.9188730 1.0000000 1.0000000 1.0000000
 [9,] 0.4204033 0.4276072 0.3028424 0.7636714 0.8310956 0.8310956 0.8310956
[10,] 0.1464782 0.1489882 0.1055173 0.2660807 0.2895728 0.2895728 0.2895728
[11,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[12,] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
           [,8]      [,9]      [,10] [,11] [,12]
 [1,] 0.5058422 0.4204033 0.14647817     0     0
 [2,] 0.5145102 0.4276072 0.14898818     0     0
 [3,] 0.3643894 0.3028424 0.10551727     0     0
 [4,] 0.9188730 0.7636714 0.26608066     0     0
 [5,] 1.0000000 0.8310956 0.28957283     0     0
 [6,] 1.0000000 0.8310956 0.28957283     0     0
 [7,] 1.0000000 0.8310956 0.28957283     0     0
 [8,] 1.0000000 0.8310956 0.28957283     0     0
 [9,] 0.8310956 0.6907199 0.24066271     0     0
[10,] 0.2895728 0.2406627 0.08385242     0     0
[11,] 0.0000000 0.0000000 0.00000000     0     0
[12,] 0.0000000 0.0000000 0.00000000     0     0

$Hmax
[1] 1.839742

$HX
        1         2         3         4         5         6         7         8 
1.3689956 1.3527242 1.5954618 0.2863970 0.0000000 0.0000000 0.0000000 0.0000000 
        9        10        11        12 
0.5689956 1.6854753 1.9037017 1.9854753 

> 
> 
> cleanEx()
> nameEx("diffInstructions")
> ### * diffInstructions
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: diffInstructions
> ### Title: The measurement of the variation of the total redundancy
> ### Aliases: diffInstructions
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> data(BackgroundOrganism)
> q<-iicc$q
> training.set<-iicc$Transcriptionfactor
> correction<-correction.entropy(q,p=nrow(training.set),long=1,iicc)
> Herror<-slot(correction,"Herror")
> HXmax<-iicc$HXmax
> prob<-probability(training.set,Prob)
> HX<-entropy.Shannon(prob)
> Redundancia_corregida<-CalculRedundancy(training.set,q,iicc)
> test<-diffInstructions (training.set,HX,HXmax,Herror,Redundancia_corregida)
> 
> 
> cleanEx()
> nameEx("divergence.Renyi")
> ### * divergence.Renyi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: divergence.Renyi
> ### Title: Renyi divergence
> ### Aliases: divergence.Renyi
> 
> ### ** Examples
> 
> require("MEET")
> data(iicc)
> data(BackgroundOrganism)
> training.set<-iicc$Transcriptionfactor
> q<-iicc$q<-0.5
> correction<-correction.entropy(q,p=nrow(training.set),long=1,iicc)
> HXmax<-iicc$HXmax
> pmX<-probability(training.set,Prob)
> Probtrans<-probability.couple(Prob)
> H<-entropy.Renyi(pmX,q)
> pmXY<-joint.probability(training.set, Prob, Probtrans)
> HXY<-entropy.joint(pmXY,q,iicc)
> divergence.Renyi(training.set,pmX,pmXY,q,correction)
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
L=0.500000
m=4
          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]     [,7]
[1,] 0.0000000 1.8322724 1.8322724 1.8322724 0.3348493 1.8322724 1.832272
[2,] 1.8322724 0.6049598 1.8322724 0.7725604 1.8322724 1.8322724 1.832272
[3,] 1.8322724 1.8322724 0.7918209 1.8322724 1.8322724 1.8322724 1.001980
[4,] 1.8322724 0.7725604 1.8322724 1.5361205 1.8322724 1.8322724 1.832272
[5,] 0.3348493 1.8322724 1.8322724 1.8322724 0.0000000 1.8322724 1.832272
[6,] 1.8322724 1.8322724 1.8322724 1.8322724 1.8322724 0.6049598 1.832272
[7,] 1.8322724 1.8322724 1.0019803 1.8322724 1.8322724 1.8322724 0.384995
> 
> 
> 
> cleanEx()
> nameEx("divergence.Shannon")
> ### * divergence.Shannon
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: divergence.Shannon
> ### Title: Divergencia.Shannon: Mutual Information
> ### Aliases: divergence.Shannon
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> data(iicc)
> q<-1
> training.set<-TranscriptionFactor
> correction<-correction.entropy(q,p=nrow(training.set),long=1,iicc)
> HXmax<-entropy.Shannon(as.matrix(Prob))
> pmX<-probability(training.set,Prob)
> Probtrans<-probability.couple(Prob)
> H<-entropy.Shannon(pmX)
> pmXY<-joint.probability(training.set, Prob, Probtrans)
> HXY<-entropy.joint(pmXY,q,iicc)
> divergence.Shannon(training.set,H,HXY,correction)
           [,1]      [,2]      [,3]     [,4] [,5] [,6] [,7] [,8]      [,9]
 [1,] 1.3689956 0.0000000 0.1985001 0.000000    0    0    0    0 0.0000000
 [2,] 0.0000000 1.3527242 0.1772354 0.000000    0    0    0    0 0.0000000
 [3,] 0.1985001 0.1772354 1.5954618 0.000000    0    0    0    0 0.0000000
 [4,] 0.0000000 0.0000000 0.0000000 0.286397    0    0    0    0 0.0000000
 [5,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [6,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [7,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [8,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.0000000
 [9,] 0.0000000 0.0000000 0.0000000 0.000000    0    0    0    0 0.5689956
[10,] 0.3130248 0.0000000 0.2394911 0.000000    0    0    0    0 0.0000000
[11,] 0.4262579 0.1722422 0.3772354 0.000000    0    0    0    0 0.1710878
[12,] 0.5080315 0.2540158 0.3344978 0.000000    0    0    0    0 0.1935068
          [,10]     [,11]     [,12]
 [1,] 0.3130248 0.4262579 0.5080315
 [2,] 0.0000000 0.1722422 0.2540158
 [3,] 0.2394911 0.3772354 0.3344978
 [4,] 0.0000000 0.0000000 0.0000000
 [5,] 0.0000000 0.0000000 0.0000000
 [6,] 0.0000000 0.0000000 0.0000000
 [7,] 0.0000000 0.0000000 0.0000000
 [8,] 0.0000000 0.0000000 0.0000000
 [9,] 0.0000000 0.1710878 0.1935068
[10,] 1.6854753 0.6232197 0.9049933
[11,] 0.6232197 1.8397421 0.6477309
[12,] 0.9049933 0.6477309 1.8397421
> 
> 
> cleanEx()
> nameEx("entropy.Renyi")
> ### * entropy.Renyi
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: entropy.Renyi
> ### Title: Renyi Entropy
> ### Aliases: entropy.Renyi
> 
> ### ** Examples
> 
>  require("MEET")
>  data(TranscriptionFactor)
>  data(RenyiOrder)
>  data(BackgroundOrganism)
>  training.set<-TranscriptionFactor
>  wind<-probability(training.set,Prob)
>  test<-entropy.Renyi(wind,q)
> 
> 
> cleanEx()
> nameEx("entropy.Shannon")
> ### * entropy.Shannon
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: entropy.Shannon
> ### Title: Shannon Entropy
> ### Aliases: entropy.Shannon
> 
> ### ** Examples
> 
>  require("MEET")
>  data(TranscriptionFactor)
>  data(BackgroundOrganism)
>  training.set<-TranscriptionFactor
>  wind<-probability(training.set,Prob)
>  test<-entropy.Shannon(wind)
> 
> 
> cleanEx()
> nameEx("entropy.corrected")
> ### * entropy.corrected
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: entropy.corrected
> ### Title: Correction of the Finite Sample Size Effect
> ### Aliases: entropy.corrected
> 
> ### ** Examples
> 
> require("MEET")
> data(BackgroundOrganism)
> data(iicc)
> Factortrans<-iicc$Transcriptionfactor
> correction<-correction.entropy(q=iicc$q,p=nrow(Factortrans),long=1,iicc)
> ErrorHX<-slot(correction,"sderror")[nrow(Factortrans)]
> HXmax<-iicc$HXmax
> prob<-probability(Factortrans,Prob)
> H<-entropy.Shannon(prob)
> test<-entropy.corrected(H,ErrorHX,HXmax)
> 
> 
> 
> cleanEx()
> nameEx("entropy.joint")
> ### * entropy.joint
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: entropy.joint
> ### Title: To calculate joint entropy
> ### Aliases: entropy.joint
> 
> ### ** Examples
> 
> require("MEET")
> data(BackgroundOrganism)
> data(iicc)
> training.set<-iicc$Transcriptionfactor
> Probtrans<-probability.couple(Prob)
> pmXY<-joint.probability(training.set, Prob, Probtrans)
> test<-entropy.joint(pmXY,q=iicc$q,iicc)
> 
> 
> cleanEx()
> nameEx("joint.probability")
> ### * joint.probability
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: joint.probability
> ### Title: Joint Probability
> ### Aliases: joint.probability
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> training.set<-TranscriptionFactor
> Probtrans<-probability.couple(Prob)
> pmXY<-joint.probability(training.set, Prob,Probtrans)
> 
> 
> 
> cleanEx()
> nameEx("kfold.MATCH")
> ### * kfold.MATCH
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kfold.MATCH
> ### Title: MATCH validation process
> ### Aliases: kfold.MATCH
> 
> ### ** Examples
> 
> require("MEET")
> require("seqinr")
> data(iicc)
> data(TranscriptionFactor)
> pathMEET <- system.file("sequences", package = "MEET")
> iicc$method<-"MATCH"
> iicc$vector<-c(0.5, 0.8)
> kfold.MATCH(iicc, Seqin = paste(pathMEET, "AP1.fa", sep = "/"))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      1    4750    9396    9441   14146   18791 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      1    4750    9396    9441   14146   18791 
[[1]]
    [1] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
   [37] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
   [73] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
  [109] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
  [145] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
  [181] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
  [217] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
  [253] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
  [289] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
  [325] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
  [361] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
  [397] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
  [433] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
  [469] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
  [505] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
  [541] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
  [577] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
  [613] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
  [649] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
  [685] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
  [721] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
  [757] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
  [793] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
  [829] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
  [865] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
  [901] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
  [937] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
  [973] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
 [1009] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
 [1045] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
 [1081] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
 [1117] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
 [1153] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
 [1189] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
 [1225] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
 [1261] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
 [1297] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
 [1333] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
 [1369] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
 [1405] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
 [1441] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
 [1477] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
 [1513] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
 [1549] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
 [1585] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
 [1621] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
 [1657] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
 [1693] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
 [1729] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
 [1765] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
 [1801] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
 [1837] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
 [1873] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
 [1909] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
 [1945] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
 [1981] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
 [2017] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
 [2053] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
 [2089] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
 [2125] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
 [2161] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
 [2197] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
 [2233] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
 [2269] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
 [2305] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
 [2341] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
 [2377] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
 [2413] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
 [2449] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
 [2485] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
 [2521] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
 [2557] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
 [2593] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
 [2629] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
 [2665] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
 [2701] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
 [2737] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
 [2773] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
 [2809] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
 [2845] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
 [2881] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
 [2917] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
 [2953] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
 [2989] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
 [3025] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
 [3061] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
 [3097] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
 [3133] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
 [3169] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
 [3205] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
 [3241] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
 [3277] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
 [3313] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
 [3349] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
 [3385] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
 [3421] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
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[12421] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
[12457] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
[12493] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
[12529] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
[12565] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
[12601] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
[12637] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[12673] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
[12709] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
[12745] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
[12781] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
[12817] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
[12853] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
[12889] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
[12925] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
[12961] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
[12997] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
[13033] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
[13069] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
[13105] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
[13141] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
[13177] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
[13213] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
[13249] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
[13285] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
[13321] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
[13357] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
[13393] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
[13429] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
[13465] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
[13501] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
[13537] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
[13573] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
[13609] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
[13645] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
[13681] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[13717] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
[13753] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
[13789] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
[13825] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
[13861] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
[13897] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
[13933] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
[13969] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
[14005] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
[14041] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
[14077] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
[14113] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
[14149] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
[14185] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
[14221] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
[14257] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
[14293] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
[14329] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
[14365] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
[14401] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
[14437] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
[14473] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
[14509] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
[14545] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
[14581] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
[14617] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
[14653] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
[14689] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
[14725] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[14761] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
[14797] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
[14833] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
[14869] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
[14905] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
[14941] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
[14977] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
[15013] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
[15049] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
[15085] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
[15121] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
[15157] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
[15193] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
[15229] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
[15265] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
[15301] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
[15337] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
[15373] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
[15409] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
[15445] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
[15481] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
[15517] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
[15553] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
[15589] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
[15625] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
[15661] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
[15697] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
[15733] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
[15769] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[15805] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
[15841] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
[15877] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
[15913] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
[15949] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
[15985] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
[16021] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
[16057] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
[16093] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
[16129] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
[16165] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
[16201] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
[16237] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
[16273] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
[16309] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
[16345] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
[16381] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
[16417] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
[16453] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
[16489] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
[16525] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
[16561] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
[16597] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
[16633] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
[16669] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
[16705] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
[16741] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
[16777] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
[16813] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[16849] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
[16885] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
[16921] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
[16957] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
[16993] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
[17029] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
[17065] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
[17101] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
[17137] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
[17173] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
[17209] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
[17245] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
[17281] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
[17317] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
[17353] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
[17389] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
[17425] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
[17461] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
[17497] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
[17533] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
[17569] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
[17605] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
[17641] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
[17677] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
[17713] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1
[17749] 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1
[17785] 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1
[17821] 1 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1
[17857] 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0
[17893] 1 1 0 0 1 1 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1
[17929] 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0
[17965] 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1
[18001] 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 0
[18037] 1 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1
[18073] 0 0 0 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 1
[18109] 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 1
[18145] 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 1 1
[18181] 1 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 0
[18217] 0 1 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0
[18253] 1 1 0 0 1 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0
[18289] 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1
[18325] 1 1 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 1
[18361] 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
[18397] 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1
[18433] 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1
[18469] 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 1 0 1 0 1
[18505] 1 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
[18541] 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0
[18577] 0 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 0 0
[18613] 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1
[18649] 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0
[18685] 0 1 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1
[18721] 1 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 1
[18757] 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1

> 
> 
> 
> 
> cleanEx()
> nameEx("kfold.PCA")
> ### * kfold.PCA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kfold.PCA
> ### Title: PCA
> ### Aliases: kfold.PCA
> 
> ### ** Examples
> 
> require("MEET")
> require("seqinr")
> pathMEET <- system.file("sequences", package = "MEET")
> data(iicc)
> data(TranscriptionFactor)
> iicc$method<-"PCA"
> #Define the numner of principal components
> iicc$vector<-c(1,3,5)
> kfold.PCA(iicc, TF = paste(pathMEET, "AP1.fa", sep = "/"))
[[1]]
    [1] 0.1066340222 0.0050940815 0.0159884252 0.0257691367 0.0115549502
    [6] 0.0104097216 0.0016128825 0.0495714647 0.0097598649 0.0017699982
   [11] 0.0071391927 0.0805030745 0.0671444890 0.0118574022 0.0139214848
   [16] 0.1079761145 0.1399567652 0.0326798732 0.0395684577 0.0364454173
   [21] 0.0799851578 0.0233643774 0.0160189850 0.0077322998 0.0148117046
   [26] 0.0123317358 0.0042124620 0.0169283523 0.0275553687 0.0100639161
   [31] 0.1504579884 0.1735302882 0.0246037842 0.0279349735 0.0776918017
   [36] 0.0406514410 0.0080526825 0.0102100325 0.0232987331 0.0109023094
   [41] 0.0029731427 0.0268006731 0.0019055625 0.0050487991 0.0162396832
   [46] 0.0078044489 0.0342994215 0.0147344654 0.0304537441 0.0016602951
   [51] 0.3116003553 0.0068105052 0.0111625035 0.0043528933 0.0029731427
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 [1151] 0.0306489964 0.0095633467 0.0051583774 0.0042158559 0.0051151748
 [1156] 0.0438364414 0.0181159569 0.0079242792 0.1373687977 0.0867081657
 [1161] 0.0081732076 0.0015645484 0.4066663787 0.0086175291 0.0061661628
 [1166] 0.0471492373 0.0478766190 0.0257494089 0.0124824305 0.0269261367
 [1171] 0.0085951040 0.0156006059 0.0128675351 0.0488916285 0.0859807527
 [1176] 0.0028423311 0.0229045766 0.0845184271 0.0413813478 0.0053431124
 [1181] 0.0046796966 0.0626759142 0.0611988726 0.0211487905 0.0065117166
 [1186] 0.0733214997 0.0510307052 0.0041847194 0.0059662255 0.0337738142
 [1191] 0.0189853414 0.0051498918 0.0019606537 0.0295049600 0.0746137649
 [1196] 0.0035272473 0.0036958673 0.0094783688 0.1655267208 0.0189581589
 [1201] 0.0077662687 0.0075349604 0.0805501704 0.0093570124 0.0312403918
 [1206] 0.0085483862 0.0027833277 0.0051583774 0.0153499307 0.0094583855
 [1211] 0.0089154978 0.0062557159 0.3744736585 0.0051465561 0.0182808983
 [1216] 0.0321508321 0.0138895338 0.0145966463 0.0040803093 0.0295966417
 [1221] 0.0048315195 0.0200411268 0.0141343414 0.0103333004 0.0344620503
 [1226] 0.0230665249 0.0134642809 0.1049906632 0.0022077891 0.0075993764
 [1231] 0.0236913827 0.0612479372 0.0044855040 0.0191697325 0.1634447191
 [1236] 0.0043376513 0.0056085648 0.0046697780 0.1273328544 0.0137239408
 [1241] 0.0013643594 0.0191994133 0.2471369778 0.0083344864 0.0045558878
 [1246] 0.0845184271 0.0413813478 0.0163239407 0.0142196924 0.0229045766
 [1251] 0.0059650206 0.0297699103 0.0157588980 0.0130544184 0.0027382244
 [1256] 0.0113960208 0.1536055313 0.0080268053 0.0120408870 0.2843667674
 [1261] 0.0151665914 0.0136302285 0.0059178632 0.0083743570 0.1098120469
 [1266] 0.0152098862 0.0181159569 0.0064216918 0.0663969691 0.0594880653
 [1271] 0.0511007333 0.0097345894 0.0215635367 0.0043601138 0.0171757877
 [1276] 0.0096384543 0.0109255911 0.0499749669 0.0051659439 0.0221306062
 [1281] 0.1190286341 0.1228134274 0.0191562153 0.0190481548 0.0027206557
 [1286] 0.0123020555 0.0141830918 0.0173953579 0.0057813196 0.0334948292
 [1291] 0.0348758041 0.0198034166 0.0163092288 0.0233575484 0.0056987290
 [1296] 0.0150018387 0.0258218706 0.0391174796 0.0106580039 0.0036285223
 [1301] 0.0122296956 0.0138895338 0.0284819351 0.0027833277 0.0145966463
 [1306] 0.0017080462 0.0645401815 0.0096416881 0.0046204390 0.0137650665
 [1311] 0.0863528791 0.0809251879 0.0057481108 0.0603704647 0.0202344899
 [1316] 0.0169380668 0.0013234007 0.0206290403 0.3700905474 0.0200847296
 [1321] 0.0028320866 0.3031703332 0.0531748575 0.0077622092 0.0183874483
 [1326] 0.0379487908 0.0200943108 0.0028469280 0.0158977739 0.0079981072
 [1331] 0.0159636912 0.0056214576 0.0229924136 0.0553370767 0.0036614671
 [1336] 0.0260760952 0.0289436558 0.0335586114 0.0115706731 0.0141830918
 [1341] 0.0086175291 0.0105349075 0.0239176630 0.0344055219 0.0151765248
 [1346] 0.0044949226 0.0163416893 0.0200213316 0.0080726774 0.0065706405
 [1351] 0.0057518287 0.0469056340 0.0562697327 0.0138155783 0.0084766447
 [1356] 0.0141800829 0.0566564826 0.0035625533 0.0388185418 0.0161414580
 [1361] 0.0056142336 0.2118711009 0.0500617672 0.0215688025 0.0147097534
 [1366] 0.0433079195 0.0096341606 0.0022854034 0.2360950000 0.0025980236
 [1371] 0.0106732446 0.0147187544 0.0220937105 0.0031303225 0.0102925533
 [1376] 0.1194219290 0.0439227175 0.0021768516 0.0044576602 0.0550016186
 [1381] 0.0303529553 0.0059797561 0.0471492373 0.0149903060 0.0157266193
 [1386] 0.0210846821 0.0200847296 0.0138981841 0.0863528791 0.0226225081
 [1391] 0.0203617837 0.0757059088 0.0013104910 0.1079441526 0.0826823627
 [1396] 0.0066675146 0.0049464799 0.5888470738 0.0741725965 0.0047946735
 [1401] 0.0111764987 0.0519808551 0.0258192685 0.0074688935 0.0806282978
 [1406] 0.0203865856 0.1664407192 0.0054217848 0.0609339345 0.0470361078
 [1411] 0.0028694330 0.0503924683 0.0022360335 0.0389742311 0.0074828958
 [1416] 0.0138782781 0.0148300504 0.1479953850 0.0084766447 0.0504316415
 [1421] 0.2733698312 0.0012441097 0.0361622497 0.0207431600 0.0058360388
 [1426] 0.0036809267 0.0247816560 0.0165684377 0.0031272219 0.0761233896
 [1431] 0.1114784377 0.0019787326 0.0050847142 0.0534843659 0.1694420494
 [1436] 0.0577852292 0.0376533102 0.0096183166 0.0032409075 0.0038702182
 [1441] 0.0087265601 0.0614695084 0.0053616217 0.0037918851 0.0182386523
 [1446] 0.2309916081 0.0283151523 0.0031965214 0.0721285265 0.0046697780
 [1451] 0.1579538576 0.0021793739 0.0421851350 0.1256092094 0.0250971422
 [1456] 0.0073015005 0.0570931774 0.0208814013 0.0320253866 0.0148300504
 [1461] 0.0715989591 0.0204152729 0.1190286341 0.1228134274 0.0030977988
 [1466] 0.0183856491 0.0044145681 0.0908843348 0.0050315589 0.0552029954
 [1471] 0.3897893094 0.0214212286 0.0077622092 0.0033353195 0.1189552432
 [1476] 0.0181599709 0.0019171962 0.0089154978 0.0890042991 0.1728112056
 [1481] 0.0050315589 0.0501197200 0.3327695275 0.0096413875 0.0190782914
 [1486] 0.0069517242 0.3990281647 0.0031594191 0.0367156768 0.0320508916
 [1491] 0.0210587737 0.0009968660 0.1079999995 0.0085483862 0.0060892086
 [1496] 0.0128086610 0.0034522644 0.0140813894 0.0040803093 0.0295966417
 [1501] 0.0057365339 0.0035119468 0.0712914762 0.0427907196 0.0035318269
 [1506] 0.0401372345 0.1099946473 0.0320044214 0.0033972978 0.0756040077
 [1511] 0.0110142466 0.1902628481 0.0062988959 0.0021400325 0.1388657303
 [1516] 0.0032087168 0.0178531827 0.0037919562 0.0342734905 0.0437060263
 [1521] 0.0156809545 0.0102211430 0.0044842974 0.1562537255 0.0048658749
 [1526] 0.0665575793 0.0194265243 0.0852632127 0.0080193121 0.0013217495
 [1531] 0.1269749851 0.0189076849 0.0063339841 0.0256357619 0.0241996323
 [1536] 0.0083743570 0.0725976069 0.0053235003 0.0013057715 0.0175277683
 [1541] 0.1293209669 0.0059797561 0.0471492373 0.0149903060 0.0131004429
 [1546] 0.0312499258 0.0102682181 0.0194062279 0.0765583582 0.0715989591
 [1551] 0.0020088551 0.0929850074 0.2337300920 0.0273021452 0.0085834583
 [1556] 0.0302443277 0.0318244184 0.1007281423 0.0093238669 0.0022854034
 [1561] 0.0232161680 0.0215012510 0.0047977354 0.0159410508 0.0035625533
 [1566] 0.2628482501 0.0057365339 0.0035119468 0.0712914762 0.0179091554
 [1571] 0.0077187241 0.0076910193 0.0755066080 0.0625088381 0.0275590327
 [1576] 0.0552029954 0.1615471293 0.0073701250 0.1909609470 0.0184828213
 [1581] 0.0433267329 0.0248156724 0.0426807041 0.0051821787 0.0171913774
 [1586] 0.0039775154 0.0231358902 0.0475544865 0.0143174412 0.0348035226
 [1591] 0.0343459365 0.0220937105 0.0031303225 0.0067517923 0.2300450909
 [1596] 0.0383514335 0.0070704447 0.0178782177 0.0257977866 0.0039008090
 [1601] 0.0368827275 0.0404445198 0.0050222604 0.0267579738 0.0110215676
 [1606] 0.6274369586 0.0184828213 0.0433267329 0.0248156724 0.0426807041
 [1611] 0.0051821787 0.0171913774 0.0039775154 0.0231358902 0.0475544865
 [1616] 0.0143174412 0.0348035226 0.0343459365 0.0220937105 0.0010800801
 [1621] 0.0267391195 0.0875157228 0.0200847296 0.0028320866 0.0785269362
 [1626] 0.0956898895 0.0048284696 0.0189354435 0.0765583582 0.0253976921
 [1631] 0.0267579738 0.0110215676 0.6274369586 0.0050420294 0.0479780919
 [1636] 0.0119956773 0.5125265666 0.0070347017 0.0070704447 0.0178782177
 [1641] 0.0257977866 0.0398305318 0.0052778794 0.0319543564 0.0110543514
 [1646] 0.0449645465 0.0032562100 0.0077187241 0.0549718373 0.0221790245
 [1651] 0.0063339841 0.0637823504 0.0710989362 0.0150525890 0.0306933225
 [1656] 0.0489519894 0.0554931058 0.0355328314 0.0179852193 0.0187897772
 [1661] 0.0334366290 0.0088216251 0.0210618449 0.0053565295 0.2313543768
 [1666] 0.0028126192 0.0158977739 0.0008564134 0.1579538576 0.0021793739
 [1671] 0.0138400934 0.0872108711 0.0103837104 0.0063339841 0.0637823504
 [1676] 0.3406560262 0.0024479260 0.0162804836 0.1704389629 0.0137856959
 [1681] 0.0039008090 0.0652359684 0.0164977133 0.0471866015 0.0282388684
 [1686] 0.1153681558 0.0064870238 0.0873126961 0.0050652627 0.0293666903
 [1691] 0.1028053903 0.0179111818 0.0041839759 0.0607931362 0.0787219855
 [1696] 0.0045718329 0.0111893020 0.0487841512 0.0966338381 0.0031809699
 [1701] 0.0244624561 0.1187058322 0.0361972605 0.0016925638 0.0160092598
 [1706] 0.0308755790 0.0231378582 0.0144437561 0.0371507993 0.0015646754
 [1711] 0.0347397983 0.0114976334 0.0315332804 0.0128086610 0.0034522644
 [1716] 0.0051714401 0.0545938351 0.0041842111 0.0029313485 0.0124016031
 [1721] 0.5298589805 0.0015342081 0.0289091004 0.0708272904 0.0655806426
 [1726] 0.0052759631 0.0372921586 0.0019991238 0.0128035503 0.0266904707
 [1731] 0.0285793331 0.0605197576 0.0392229748 0.0037627518 0.0184366557
 [1736] 0.0107437598 0.0558055527 0.0174933823 0.0563253246 0.0054966996
 [1741] 0.0248175724 0.0089403755 0.0067486551 0.0978067359 0.0085483862
 [1746] 0.0060892086 0.0051362637 0.0438407894 0.0064305993 0.0212967221
 [1751] 0.0047199968 0.1748846807 0.0109382249 0.0094712444 0.0215012510
 [1756] 0.0140732454 0.0089360252 0.0030665305 0.3997152426 0.0067612008
 [1761] 0.0051203445 0.0060336629 0.0183856491 0.0044145681 0.0908843348
 [1766] 0.0188872753 0.1670683369 0.0590318074 0.0082730965 0.0286044984
 [1771] 0.0083612767 0.0375939844 0.0019660213 0.0487920998 0.0019254309
 [1776] 0.0180960039 0.0120814350 0.0214892863 0.0196546007 0.0035707916
 [1781] 0.0039775154 0.0035625533 0.1888228997 0.0024479260 0.0401523528
 [1786] 0.0527138577 0.1357452102 0.0231706647 0.0212789862 0.0025980236
 [1791] 0.0040120724 0.0295049600 0.0746137649 0.0012936771 0.0379948174
 [1796] 0.0107811888 0.0529367440 0.0054673310 0.0991081445 0.0012522036
 [1801] 0.0103350419 0.0097158066 0.2675489621 0.0044547467 0.0129648230
 [1806] 0.0068542033 0.0364233521 0.0522574264 0.0025097839 0.0039008090
 [1811] 0.0195236468 0.0580063189 0.0246506398 0.0036614671 0.0943657834
 [1816] 0.0765583582 0.0715989591 0.0061955908 0.0285793331 0.7571830968
 [1821] 0.0104702685 0.0063820404 0.0030694110 0.0232875121 0.1200651444
 [1826] 0.0036614671 0.0476394320 0.1438916054 0.1153681558 0.0064870238
 [1831] 0.0510487963 0.0290724552 0.0804239429 0.0233575484 0.0056987290
 [1836] 0.0150018387 0.0258218706 0.0229351152 0.0125319226 0.0061110392
 [1841] 0.0071218757 0.0671635521 0.0030185656 0.0386343154 0.0120814350
 [1846] 0.0076006508 0.0225977191 0.0154934378 0.0045718329 0.0334708342
 [1851] 0.0040981959 0.1505083753 0.0216716618 0.0238614108 0.0241064139
 [1856] 0.0019991238 0.0635562762 0.0523837078 0.0200847296 0.0476394320
 [1861] 0.1438916054 0.1153681558 0.0064870238 0.0901847461 0.0256766964
 [1866] 0.0182194022 0.0183766210 0.0064539154 0.0961992975 0.0121055586
 [1871] 0.0064554694 0.0174933823 0.0289168814 0.0180184058 0.0042443445
 [1876] 0.0043601138 0.1856168012 0.0241639189 0.0038789738 0.0445914537
 [1881] 0.0110215676 0.0431841153 0.0108043682 0.0285793331 0.0163949686
 [1886] 0.0239894391 0.0042226484 0.0046637078 0.0334366290 0.1188889796
 [1891] 0.0042744241 0.0232471488 0.0896877574 0.0092146774 0.0163052621
 [1896] 0.0218938885 0.1166614510 0.0034061872 0.0114387545 0.0283942217
 [1901] 0.0438407894 0.0064305993 0.0058225612 0.0041109623 0.4536568339
 [1906] 0.0288877700 0.0055253052 0.0069362357 0.0553272998 0.0208809979
 [1911] 0.0015361944 0.1161531311 0.0067612008 0.0051203445 0.0060336629
 [1916] 0.0670083422 0.0010800801 0.0688406744 0.0593060448 0.0349850967
 [1921] 0.0103448274 0.0355328314 0.0486197504 0.0156737101 0.0048896693
 [1926] 0.0211560750 0.0215635367 0.0018816223 0.0152396812 0.3269389024
 [1931] 0.0042942971 0.0261335097 0.0058169578 0.0203224643 0.0035625533
 [1936] 0.4394700703 0.0015342081 0.0289091004 0.0396573190 0.1090251494
 [1941] 0.0065755665 0.0135941922 0.0067612008 0.0037227138 0.0385690882
 [1946] 0.0156303135 0.0010350994 0.0075117968 0.0198157153 0.0188540273
 [1951] 0.0047080101 0.0336518701 0.0125460753 0.0481443753 0.0097158066
 [1956] 0.1129920183 0.0059024322 0.0035119468 0.0712914762 0.0609426589
 [1961] 0.0240916581 0.0064870238 0.0268029884 0.0069362357 0.1279453621
 [1966] 0.0237213495 0.0091228208 0.0107607689 0.1966630923 0.0741725965
 [1971] 0.0275771464 0.0070347017 0.0695414352 0.0148300504 0.0253976921
 [1976] 0.0267579738 0.0278518826 0.0908870804 0.0273469325 0.0386450141
 [1981] 0.0866649920 0.0668168997 0.0046028665 0.0027382244 0.0356282258
 [1986] 0.1279453621 0.0237213495 0.0091228208 0.0107607689 0.0929850074
 [1991] 0.2337300920 0.0117219827 0.0036614671 0.0943657834 0.0765583582
 [1996] 0.0253976921 0.0267579738 0.0110215676 0.3024357740 0.0339719617
 [2001] 0.0433267329 0.0304782031 0.1180895239 0.0025097839 0.0039008090
 [2006] 0.0195236468 0.0580063189 0.0246506398 0.0091228208 0.0107607689
 [2011] 0.0929850074 0.2337300920 0.0117219827 0.0036614671 0.1213809244
 [2016] 0.0461045581 0.0190056906 0.0204897266 0.0241639189 0.1352574839
 [2021] 0.0162828448 0.1367491334 0.0282388684 0.1153681558 0.0064870238
 [2026] 0.0901847461 0.0178782177 0.0626391236 0.0137044127 0.0180625178
 [2031] 0.0054166607 0.0283121154 0.0229351152 0.0125319226 0.0091253227
 [2036] 0.0096341606 0.0758655915 0.0045864014 0.0151109684 0.0384149903
 [2041] 0.0074288175 0.0225977191 0.0154934378 0.0116879042 0.0189354435
 [2046] 0.0064063897 0.0901847461 0.0081549045 0.0449319288 0.0152277509
 [2051] 0.0129541603 0.0057115242 0.0915356817 0.0383514335 0.0274694070
 [2056] 0.0070347017 0.0695414352 0.0148300504 0.0627614423 0.0352757668
 [2061] 0.0354978564 0.2958055729 0.0019186709 0.0755854655 0.0276794799
 [2066] 0.0125653273 0.0195048334 0.0069437055 0.0281028492 0.0061615978
 [2071] 0.0120205377 0.0107811888 0.0529367440 0.0054673310 0.0991081445
 [2076] 0.0012522036 0.0191509279 0.0197261572 0.0794756339 0.0065561960
 [2081] 0.0198034166 0.0068597629 0.0184366557 0.0107437598 0.0558055527
 [2086] 0.0621287160 0.0392229748 0.0075691626 0.1178021120 0.0053616217
 [2091] 0.0182808983 0.0321508321 0.0138895338 0.0145966463 0.0017080462
 [2096] 0.0645401815 0.0096416881 0.0017044287 0.0073610252 0.0896877574
 [2101] 0.0734716658 0.0110498001 0.0134430210 0.1042784832 0.1497646730
 [2106] 0.0306861148 0.0355502654 0.0354978564 0.0901847461 0.0256766964
 [2111] 0.0182194022 0.0095206598 0.0171575891 0.0144902092 0.0045558878
 [2116] 0.0188540273 0.0283151523 0.0095990475 0.1358615323 0.1682704839
 [2121] 0.0226726841 0.0297326264 0.0857337397 0.0489756722 0.0097725005
 [2126] 0.0122296956 0.0315332804 0.0128086610 0.0034522644 0.0375939844
 [2131] 0.0019660213 0.0066022445 0.0183874483 0.0082009711 0.0405345223
 [2136] 0.0140430631 0.0331142011 0.0017639259 0.3722439151 0.0085483862
 [2141] 0.0150748253 0.0047878399 0.0034522644 0.0071227653 0.0425442344
 [2146] 0.0033700303 0.0027980221 0.0252672228 0.1552024017 0.0059127392
 [2151] 0.0103875327 0.0644915029 0.0044949226 0.0091252092 0.0194410430
 [2156] 0.0090794385 0.0382013076 0.0026177293 0.0029036492 0.0619586356
 [2161] 0.0045425933 0.0015040246 0.0739536842 0.0655546108 0.0161779365
 [2166] 0.0058249262 0.0879169166 0.0185795009 0.0668292979 0.0162956567
 [2171] 0.0042160717 0.0114976334 0.0138895338 0.0051583774 0.0153499307
 [2176] 0.0108772957 0.0074722783 0.0054015841 0.0155933874 0.0892231803
 [2181] 0.0119906904 0.0305623523 0.0593060448 0.0349850967 0.0031594191
 [2186] 0.2330224287 0.0054166607 0.0136962771 0.0065755665 0.0488414979
 [2191] 0.0178430003 0.0065563409 0.0266234229 0.0570889657 0.0306489964
 [2196] 0.0095633467 0.0051583774 0.0042158559 0.0051151748 0.0438364414
 [2201] 0.0181159569 0.0079242792 0.1373687977 0.0867081657 0.0081732076
 [2206] 0.0015645484 0.4066663787 0.0086175291 0.0061661628 0.0471492373
 [2211] 0.0478766190 0.0257494089 0.0124824305 0.0269261367 0.0085951040
 [2216] 0.0156006059 0.0128675351 0.0488916285 0.0859807527 0.0028423311
 [2221] 0.0229045766 0.0845184271 0.0413813478 0.0053431124 0.0046796966
 [2226] 0.0626759142 0.0611988726 0.0211487905 0.0065117166 0.0733214997
 [2231] 0.0510307052 0.0041847194 0.0059662255 0.0337738142 0.0189853414
 [2236] 0.0051498918 0.0019606537 0.0295049600 0.0746137649 0.0035272473
 [2241] 0.0036958673 0.0094783688 0.1655267208 0.0189581589 0.0077662687
 [2246] 0.0075349604 0.0805501704 0.0093570124 0.0312403918 0.0085483862
 [2251] 0.0027833277 0.0051583774 0.0153499307 0.0094583855 0.0089154978
 [2256] 0.0062557159 0.3744736585 0.0051465561 0.0182808983 0.0321508321
 [2261] 0.0138895338 0.0145966463 0.0040803093 0.0295966417 0.0048315195
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[18786] 0.0030378535 0.0092588739 0.0051144171 0.0379912892 0.0482766159
[18791] 0.0175232530 0.0022492621

[[2]]
    [1] 5.134498e-02 1.286579e-03 2.124008e-03 3.101162e-03 2.736230e-03
    [6] 9.615359e-04 1.160997e-04 9.808781e-03 2.144732e-03 1.579928e-03
   [11] 1.224643e-03 2.717163e-02 1.003737e-01 9.470727e-03 4.000406e-03
   [16] 2.600389e-02 4.956680e-02 6.650819e-03 5.249556e-02 5.892630e-03
   [21] 1.697678e-02 4.794957e-03 3.663839e-03 8.514797e-04 2.049376e-03
   [26] 2.665617e-03 8.701518e-04 2.800881e-03 1.664676e-02 7.536472e-03
   [31] 8.601954e-02 2.671372e-01 7.793015e-03 8.606442e-03 2.110486e-02
   [36] 2.755193e-02 1.583364e-03 1.179110e-03 4.829885e-03 1.740301e-03
   [41] 2.932612e-04 3.084540e-03 1.263817e-04 3.746759e-04 2.280182e-03
   [46] 1.112986e-03 1.165735e-02 2.966534e-03 1.338987e-02 1.040497e-04
   [51] 1.622294e-01 1.027815e-03 1.105290e-03 4.312065e-04 2.932612e-04
   [56] 5.564786e-04 6.801566e-03 4.197271e-04 2.736925e-04 2.454449e-03
   [61] 5.432732e-02 1.627320e-03 8.638277e-04 1.198313e-02 3.205602e-04
   [66] 7.225830e-04 3.470864e-03 8.199961e-04 3.736033e-03 3.574324e-04
   [71] 3.252309e-04 1.665335e-02 4.276428e-04 1.161225e-04 1.564639e-02
   [76] 2.351659e-02 2.657458e-03 5.808786e-04 2.027072e-02 2.179285e-03
   [81] 7.753274e-03 2.450021e-03 1.519583e-03 1.340829e-03 2.736230e-03
   [86] 3.443102e-04 1.258737e-03 1.371897e-03 2.654779e-03 7.535138e-04
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  [996] 3.910218e-03 5.409933e-04 2.569608e-03 4.825035e-03 1.731166e-03
 [1001] 9.046079e-03 7.679147e-04 1.697678e-02 1.024138e-03 9.325588e-03
 [1006] 1.753152e-03 1.276559e-03 5.381539e-04 1.180846e-02 6.179445e-03
 [1011] 1.937273e-02 7.547099e-04 4.146149e-02 3.219311e-03 1.191521e-02
 [1016] 2.657020e-02 5.892630e-03 1.138082e-01 1.459620e-04 9.068196e-03
 [1021] 3.210233e-03 8.314563e-04 2.060988e-03 3.817231e-03 5.759384e-03
 [1026] 5.835247e-04 4.982450e-03 1.352609e-03 6.700772e-03 4.461909e-04
 [1031] 2.373840e-02 1.486757e-04 2.064491e-03 2.221302e-03 1.479182e-02
 [1036] 2.311192e-03 1.175447e-02 5.433289e-04 3.694367e-03 2.783019e-03
 [1041] 6.447144e-03 1.238916e-02 2.992941e-02 2.293682e-03 7.741949e-02
 [1046] 1.664309e-03 2.421509e-03 4.187102e-03 2.696271e-03 1.495388e-03
 [1051] 1.456972e-04 1.423963e-02 2.774941e-03 2.002864e-03 1.580372e-03
 [1056] 3.250316e-02 1.310435e-01 1.277292e-02 5.170394e-03 3.103269e-02
 [1061] 5.813291e-02 7.510530e-03 7.969722e-02 6.638925e-03 2.286020e-02
 [1066] 7.534636e-03 3.898713e-03 1.112925e-03 2.248119e-03 3.094729e-03
 [1071] 1.107907e-03 3.583839e-03 2.215312e-02 1.005046e-02 1.074919e-01
 [1076] 3.442636e-01 8.778411e-03 1.254960e-02 2.575320e-02 2.617780e-02
 [1081] 1.967265e-03 1.398256e-03 6.251008e-03 2.257000e-03 3.388845e-04
 [1086] 5.006589e-03 1.524449e-04 5.373995e-04 2.967710e-03 1.326048e-03
 [1091] 1.589690e-02 3.681910e-03 2.054902e-02 1.161782e-04 2.167588e-01
 [1096] 1.191804e-03 1.602956e-03 5.575720e-04 3.388845e-04 7.101683e-04
 [1101] 8.601071e-03 4.718957e-04 3.253799e-04 3.027592e-03 7.027076e-02
 [1106] 2.612875e-03 1.216147e-03 1.113072e-02 3.811513e-04 9.336127e-04
 [1111] 4.439206e-03 1.158335e-03 5.317255e-03 5.494514e-04 4.203111e-04
 [1116] 1.648229e-02 5.040797e-04 1.384706e-04 2.238149e-02 2.596800e-02
 [1121] 3.195383e-03 6.787496e-04 2.331545e-02 3.032530e-03 1.145131e-02
 [1126] 2.996471e-03 1.940271e-03 1.510091e-03 2.696271e-03 3.901489e-04
 [1131] 1.579165e-03 1.710042e-03 4.359003e-03 9.569391e-04 1.693775e-03
 [1136] 1.596728e-02 2.000966e-03 3.622110e-02 2.048552e-02 9.222663e-02
 [1141] 2.969579e-04 7.346659e-02 5.192443e-04 1.756984e-03 7.563623e-04
 [1146] 4.431062e-02 3.430385e-02 6.527219e-03 7.237959e-03 1.596396e-02
 [1151] 7.333481e-03 8.376857e-04 3.901489e-04 4.404782e-04 1.633818e-03
 [1156] 1.467991e-02 7.369338e-03 2.129841e-03 8.295397e-02 5.238313e-02
 [1161] 1.324502e-03 1.146362e-04 2.212720e-01 1.074996e-03 1.235434e-03
 [1166] 1.223139e-02 1.704144e-02 1.252157e-02 1.500610e-03 6.822035e-03
 [1171] 2.579546e-03 2.293935e-03 1.655190e-03 1.779079e-02 2.144945e-01
 [1176] 1.295876e-03 5.195802e-03 2.314115e-02 1.608762e-02 1.307042e-03
 [1181] 4.887897e-04 2.792869e-02 3.188573e-02 2.236743e-02 9.199111e-04
 [1186] 1.458961e-02 1.809951e-02 3.392877e-04 5.545470e-04 4.623152e-03
 [1191] 2.754607e-03 1.492788e-03 2.442107e-04 6.289323e-03 1.344553e-02
 [1196] 1.031401e-03 3.216574e-04 9.043374e-04 4.425508e-02 2.207498e-03
 [1201] 4.651153e-03 1.377913e-03 1.443866e-02 9.615264e-04 7.606274e-03
 [1206] 1.191804e-03 2.097729e-04 3.901489e-04 1.579165e-03 9.721752e-04
 [1211] 1.130152e-02 1.189142e-03 2.102244e-01 1.427317e-03 2.421509e-03
 [1216] 4.187102e-03 2.696271e-03 1.495388e-03 4.952732e-04 4.498582e-03
 [1221] 1.026864e-03 3.271157e-02 2.348048e-03 1.410617e-03 5.018933e-03
 [1226] 3.074787e-03 1.572110e-03 1.159507e-01 5.484130e-04 1.541893e-03
 [1231] 4.652399e-03 4.016802e-02 1.505602e-03 2.663470e-03 7.939995e-02
 [1236] 5.927944e-04 5.426142e-04 3.647716e-04 2.855767e-02 3.964700e-03
 [1241] 4.619699e-04 1.105389e-02 2.712695e-01 3.893253e-03 1.107907e-03
 [1246] 2.314115e-02 1.608762e-02 8.284418e-03 2.010216e-03 5.195802e-03
 [1251] 5.202500e-04 4.760135e-03 3.322683e-03 2.306268e-03 6.479866e-04
 [1256] 1.928180e-03 7.189709e-02 3.198154e-03 1.784305e-03 1.366351e-01
 [1261] 1.465797e-02 2.562446e-03 1.309559e-03 7.527445e-04 4.372740e-02
 [1266] 2.106984e-02 7.369338e-03 2.276231e-03 2.005626e-02 1.284315e-02
 [1271] 8.192869e-03 2.812533e-03 4.047758e-03 7.670393e-04 1.945333e-03
 [1276] 1.988569e-03 2.136156e-03 1.041421e-02 1.098141e-03 3.419242e-02
 [1281] 2.355378e-02 2.961380e-02 3.490256e-03 2.467859e-03 1.753398e-04
 [1286] 1.330374e-03 2.627626e-03 2.225048e-03 9.025480e-04 4.847138e-03
 [1291] 1.126531e-02 1.774522e-02 2.238664e-03 3.874565e-03 8.270186e-04
 [1296] 1.933676e-03 4.387426e-03 8.912867e-03 2.860544e-03 7.794432e-04
 [1301] 1.398256e-03 2.696271e-03 3.529528e-03 2.097729e-04 1.495388e-03
 [1306] 1.456972e-04 1.423963e-02 2.774941e-03 3.916137e-03 3.832583e-03
 [1311] 2.250744e-02 4.519703e-02 5.145233e-04 1.053404e-02 7.776938e-03
 [1316] 1.025387e-02 3.401634e-04 6.448984e-03 1.765940e-01 5.904209e-03
 [1321] 1.193093e-03 2.241090e-01 2.139851e-02 6.620699e-04 2.967710e-03
 [1326] 9.065799e-03 2.185836e-03 2.043647e-04 2.985105e-03 8.296414e-04
 [1331] 2.251393e-03 5.477524e-04 7.203597e-03 1.913491e-02 7.873323e-04
 [1336] 2.804304e-02 4.919924e-03 4.972609e-03 1.083512e-03 2.627626e-03
 [1341] 1.074996e-03 2.950826e-03 2.772662e-03 4.379446e-03 3.325973e-03
 [1346] 3.811513e-04 1.905070e-03 1.057140e-02 2.581193e-03 5.347727e-04
 [1351] 6.243666e-04 1.191176e-02 2.021792e-02 2.072280e-03 5.386507e-03
 [1356] 1.558397e-03 1.295815e-02 3.136822e-04 7.151982e-03 1.183154e-02
 [1361] 4.791690e-03 1.138212e-01 2.123904e-02 5.588962e-03 1.533813e-03
 [1366] 1.436696e-02 1.711898e-03 1.568428e-04 1.067338e-01 2.558446e-04
 [1371] 1.155872e-03 1.432054e-03 3.122642e-03 3.883738e-04 1.432136e-03
 [1376] 2.554452e-02 1.486147e-02 4.228956e-04 4.314498e-04 9.411895e-03
 [1381] 4.332329e-03 8.667010e-04 1.223139e-02 5.570098e-03 1.549126e-02
 [1386] 5.864847e-03 5.904209e-03 5.807817e-03 2.250744e-02 7.370718e-03
 [1391] 3.095779e-03 1.412187e-02 2.517050e-04 2.174248e-02 3.459417e-02
 [1396] 5.406058e-03 1.750906e-03 3.099728e-01 1.296964e-02 1.477631e-03
 [1401] 4.224027e-03 9.472513e-03 1.095235e-02 8.871396e-04 2.281212e-02
 [1406] 2.579381e-03 3.869335e-02 2.403202e-03 1.409892e-02 2.191518e-02
 [1411] 2.918924e-04 1.565057e-02 3.051991e-04 6.417537e-03 6.567104e-04
 [1416] 1.306969e-02 4.335688e-03 3.834313e-02 5.386507e-03 7.377636e-03
 [1421] 1.629796e-01 1.218038e-04 6.453885e-03 2.671802e-03 1.138278e-03
 [1426] 4.347219e-04 3.937294e-03 3.003170e-03 2.661255e-04 1.286371e-02
 [1431] 9.569219e-02 2.819888e-03 7.105842e-04 9.293252e-03 5.613678e-02
 [1436] 1.864549e-02 1.178640e-02 2.373303e-03 4.192632e-04 2.617085e-04
 [1441] 1.468129e-03 1.162525e-02 1.664309e-03 6.656622e-04 7.170704e-03
 [1446] 1.367566e-01 2.215312e-02 3.548706e-04 1.596267e-02 3.647716e-04
 [1451] 4.132681e-02 2.677035e-04 2.643642e-02 6.149947e-02 1.949566e-02
 [1456] 2.250447e-03 2.198284e-02 3.154556e-03 1.157516e-02 4.335688e-03
 [1461] 1.709034e-02 1.585761e-02 2.355378e-02 2.961380e-02 2.814170e-04
 [1466] 2.795489e-03 9.375981e-04 4.104873e-02 1.875769e-03 9.293334e-03
 [1471] 4.906795e-01 2.545019e-02 6.620699e-04 2.869613e-04 3.142652e-02
 [1476] 3.465285e-03 4.371781e-04 1.130152e-02 6.090065e-02 9.836222e-02
 [1481] 1.875769e-03 7.828030e-03 1.289923e-01 1.945396e-03 7.802946e-03
 [1486] 7.677248e-04 2.796834e-01 2.969579e-04 4.833902e-03 4.592051e-03
 [1491] 3.396428e-03 5.956079e-05 2.266374e-02 1.191804e-03 4.914137e-04
 [1496] 2.257000e-03 3.388845e-04 1.479422e-03 4.952732e-04 4.498582e-03
 [1501] 8.619205e-04 8.290728e-04 1.160004e-02 7.685484e-03 2.771056e-03
 [1506] 7.580837e-03 2.189635e-02 8.770243e-02 4.261925e-04 2.444314e-02
 [1511] 1.284992e-03 1.073058e-01 5.385530e-04 3.102746e-04 4.778220e-02
 [1516] 5.407909e-04 5.139320e-03 3.705085e-04 5.039822e-03 8.538985e-03
 [1521] 2.855395e-03 1.244693e-03 2.238558e-03 9.119496e-02 4.535504e-04
 [1526] 1.304698e-02 1.509801e-02 1.785764e-02 1.227018e-03 2.057883e-04
 [1531] 2.781734e-02 4.367167e-03 8.405779e-04 5.001770e-03 2.789870e-03
 [1536] 7.527445e-04 1.777992e-02 1.415745e-03 2.759537e-04 3.207452e-03
 [1541] 4.953708e-02 8.667010e-04 1.223139e-02 5.570098e-03 4.849440e-03
 [1546] 6.449133e-03 2.618270e-03 2.684009e-02 3.169388e-02 1.709034e-02
 [1551] 5.960581e-04 1.815983e-02 7.274622e-02 1.734288e-01 4.400364e-03
 [1556] 7.500046e-03 4.724832e-03 8.759745e-02 1.303447e-03 1.568428e-04
 [1561] 6.913197e-03 8.289755e-03 4.762640e-04 4.666516e-03 3.136822e-04
 [1566] 1.462400e-01 8.619205e-04 8.290728e-04 1.160004e-02 2.650910e-03
 [1571] 1.639711e-02 1.858851e-03 3.532182e-02 4.599462e-02 1.288754e-02
 [1576] 9.293334e-03 1.551095e-01 2.591056e-03 6.723884e-02 3.234931e-03
 [1581] 1.307213e-02 3.642200e-03 5.765127e-03 4.319461e-04 2.220892e-03
 [1586] 3.402906e-04 3.257660e-03 1.159332e-02 2.752861e-03 7.539802e-03
 [1591] 6.459608e-03 3.122642e-03 3.883738e-04 6.331527e-04 6.583771e-02
 [1596] 7.020840e-03 6.486188e-03 4.050403e-03 3.997729e-03 2.735035e-04
 [1601] 7.493057e-03 1.404203e-02 6.817097e-04 2.942046e-02 1.921811e-03
 [1606] 3.660477e-01 3.234931e-03 1.307213e-02 3.642200e-03 5.765127e-03
 [1611] 4.319461e-04 2.220892e-03 3.402906e-04 3.257660e-03 1.159332e-02
 [1616] 2.752861e-03 7.539802e-03 6.459608e-03 3.122642e-03 1.157301e-04
 [1621] 4.424646e-03 2.023206e-02 5.904209e-03 1.193093e-03 2.854348e-02
 [1626] 2.679714e-02 5.866097e-04 1.340972e-02 3.169388e-02 4.933817e-03
 [1631] 2.942046e-02 1.921811e-03 3.660477e-01 8.313200e-04 1.392178e-02
 [1636] 1.395132e-03 2.238710e-01 8.928832e-04 6.486188e-03 4.050403e-03
 [1641] 3.997729e-03 5.855179e-03 4.312741e-04 3.995161e-03 1.225760e-03
 [1646] 6.807010e-03 3.316712e-04 1.639711e-02 3.248835e-02 6.985282e-03
 [1651] 8.405779e-04 1.270117e-02 4.832750e-02 2.169860e-02 2.775164e-02
 [1656] 4.380627e-02 9.371537e-03 5.929606e-03 1.866097e-03 4.213409e-03
 [1661] 6.055967e-03 9.026831e-04 2.687387e-02 4.115027e-04 9.925461e-02
 [1666] 1.834073e-04 2.985105e-03 4.565157e-05 4.132681e-02 2.677035e-04
 [1671] 8.665400e-03 6.520243e-02 1.528837e-02 8.405779e-04 1.270117e-02
 [1676] 2.340635e-01 7.357578e-04 3.482526e-03 9.049555e-02 5.359709e-03
 [1681] 2.735035e-04 1.695379e-02 2.588812e-03 1.199947e-02 4.420390e-03
 [1686] 1.405566e-01 7.903971e-04 2.737510e-02 1.236464e-03 3.908438e-03
 [1691] 2.623310e-02 1.259282e-02 7.835433e-04 1.456193e-02 2.724569e-02
 [1696] 7.609080e-04 5.258539e-03 1.921149e-02 3.691798e-02 5.271750e-04
 [1701] 5.006494e-03 2.778548e-02 7.767884e-03 1.383882e-04 3.445486e-03
 [1706] 3.738151e-03 3.435393e-03 1.654046e-03 5.369908e-02 5.317988e-04
 [1711] 5.752731e-03 1.510091e-03 6.251008e-03 2.257000e-03 3.388845e-04
 [1716] 4.158182e-04 2.042563e-02 1.135111e-03 2.901634e-04 2.584665e-03
 [1721] 7.592756e-01 4.994015e-04 1.122495e-02 1.154947e-02 1.115236e-02
 [1726] 4.490074e-04 7.078637e-03 2.408930e-04 1.407837e-03 6.217513e-03
 [1731] 3.448875e-03 1.722562e-02 4.543008e-02 7.839881e-04 4.907359e-03
 [1736] 3.592332e-03 9.138740e-03 2.380206e-03 7.083449e-02 3.199179e-03
 [1741] 2.972525e-03 2.275351e-03 1.002680e-03 2.382769e-02 1.191804e-03
 [1746] 4.914137e-04 5.422155e-04 7.089861e-03 7.182477e-04 1.633715e-02
 [1751] 9.411996e-04 5.252596e-02 1.084268e-03 1.014382e-03 8.289755e-03
 [1756] 2.403632e-03 1.292268e-03 2.332121e-04 2.972396e-01 6.211630e-04
 [1761] 5.271712e-04 5.202127e-04 2.795489e-03 9.375981e-04 4.104873e-02
 [1766] 6.783770e-03 5.082227e-02 1.487239e-02 1.125383e-03 3.417791e-03
 [1771] 1.396501e-03 5.006589e-03 1.524449e-04 7.851155e-03 1.284268e-04
 [1776] 1.947733e-03 1.577416e-03 2.403973e-03 3.127780e-03 3.946233e-04
 [1781] 3.402906e-04 3.136822e-04 4.976543e-02 7.357578e-04 1.249682e-02
 [1786] 1.615152e-02 3.123986e-02 2.654704e-03 2.474705e-03 2.558446e-04
 [1791] 2.882964e-04 6.289323e-03 1.344553e-02 1.902856e-04 7.860603e-03
 [1796] 1.225202e-03 9.385597e-03 5.664963e-04 3.515478e-02 1.742860e-04
 [1801] 1.248813e-03 9.628186e-04 1.071445e-01 9.013316e-04 3.036846e-03
 [1806] 8.479456e-04 4.701848e-03 2.521096e-02 7.134347e-04 2.735035e-04
 [1811] 2.915288e-03 1.695688e-02 1.134973e-02 7.873323e-04 6.338840e-02
 [1816] 3.169388e-02 1.709034e-02 3.104304e-03 3.448875e-03 5.666930e-01
 [1821] 7.209875e-03 9.481232e-04 9.509684e-04 5.185314e-03 3.336074e-02
 [1826] 7.873323e-04 2.737765e-02 3.577326e-02 1.405566e-01 7.903971e-04
 [1831] 1.257962e-02 1.495746e-02 2.020864e-02 3.874565e-03 8.270186e-04
 [1836] 1.933676e-03 4.387426e-03 3.333365e-03 1.395943e-03 1.093326e-03
 [1841] 7.332126e-04 1.895098e-02 1.966067e-04 8.297723e-03 1.577416e-03
 [1846] 6.924981e-04 3.414216e-03 6.423107e-03 7.609080e-04 2.723977e-02
 [1851] 7.345066e-04 3.557136e-02 1.970339e-02 1.300960e-02 5.719916e-03
 [1856] 2.408930e-04 1.231302e-02 1.198183e-02 5.904209e-03 2.737765e-02
 [1861] 3.577326e-02 1.405566e-01 7.903971e-04 2.286020e-02 7.534636e-03
 [1866] 3.898713e-03 2.053447e-03 5.152043e-04 2.683152e-02 1.122514e-02
 [1871] 9.848160e-04 2.380206e-03 3.091320e-02 3.557696e-02 8.141075e-04
 [1876] 7.670393e-04 6.464821e-02 5.081525e-03 5.746474e-04 9.019852e-03
 [1881] 1.921811e-03 7.500133e-03 1.211043e-03 3.448875e-03 2.121126e-03
 [1886] 4.450605e-03 4.374624e-04 1.246344e-03 6.055967e-03 4.035052e-02
 [1891] 8.774076e-03 5.052545e-03 3.250316e-02 1.138835e-03 2.627301e-03
 [1896] 3.470542e-03 3.064825e-02 6.001517e-04 1.055360e-03 3.441316e-03
 [1901] 7.089861e-03 7.182477e-04 2.910720e-03 1.307911e-03 1.971071e-01
 [1906] 4.310597e-03 1.241253e-03 1.811486e-03 1.226025e-02 3.695545e-03
 [1911] 9.506665e-05 2.874545e-02 6.211630e-04 5.271712e-04 5.202127e-04
 [1916] 1.708396e-02 1.157301e-04 2.026504e-02 2.048552e-02 9.222663e-02
 [1921] 1.097946e-03 5.929606e-03 7.350834e-03 1.995831e-03 4.031081e-04
 [1926] 2.581442e-03 4.047758e-03 2.022291e-04 1.684536e-03 3.231398e-01
 [1931] 1.288679e-03 3.783278e-03 4.624516e-04 2.309749e-03 3.136822e-04
 [1936] 2.402645e-01 4.994015e-04 1.122495e-02 6.668666e-03 2.875088e-02
 [1941] 7.563623e-04 2.150755e-03 6.211630e-04 3.948246e-04 8.382360e-03
 [1946] 1.573266e-03 6.469203e-05 7.460652e-04 2.284324e-03 3.583839e-03
 [1951] 2.146014e-03 4.895971e-02 2.957145e-03 7.126220e-03 9.628186e-04
 [1956] 3.080241e-02 1.187601e-03 8.290728e-04 1.160004e-02 1.098325e-02
 [1961] 7.006064e-02 7.903971e-04 4.715307e-03 1.811486e-03 3.491397e-02
 [1966] 8.383767e-03 2.307889e-03 2.961309e-03 5.468760e-02 1.296964e-02
 [1971] 1.527478e-02 8.928832e-04 5.913106e-02 4.335688e-03 4.933817e-03
 [1976] 2.942046e-02 5.633882e-03 1.662925e-02 5.515324e-03 6.670198e-03
 [1981] 2.876286e-02 5.288121e-02 2.794784e-03 6.479866e-04 9.702070e-03
 [1986] 3.491397e-02 8.383767e-03 2.307889e-03 2.961309e-03 1.815983e-02
 [1991] 7.274622e-02 3.138395e-02 7.873323e-04 6.338840e-02 3.169388e-02
 [1996] 4.933817e-03 2.942046e-02 1.921811e-03 1.016135e-01 7.259867e-03
 [2001] 1.307213e-02 4.829674e-03 5.971880e-02 7.134347e-04 2.735035e-04
 [2006] 2.915288e-03 1.695688e-02 1.134973e-02 2.307889e-03 2.961309e-03
 [2011] 1.815983e-02 7.274622e-02 3.138395e-02 7.873323e-04 1.179484e-01
 [2016] 2.163104e-02 4.232557e-03 5.932528e-03 5.081525e-03 4.498556e-02
 [2021] 3.622370e-03 8.929992e-02 4.420390e-03 1.405566e-01 7.903971e-04
 [2026] 2.286020e-02 4.050403e-03 1.640419e-02 1.684206e-03 1.980037e-03
 [2031] 5.192443e-04 3.928591e-03 3.333365e-03 1.395943e-03 1.242866e-03
 [2036] 1.711898e-03 1.959185e-02 5.393250e-04 1.684623e-03 5.348312e-03
 [2041] 7.366568e-04 3.414216e-03 6.423107e-03 1.884066e-03 1.340972e-02
 [2046] 9.214296e-04 2.286020e-02 1.530207e-03 1.175441e-02 2.188316e-03
 [2051] 1.645351e-03 6.610015e-04 1.760963e-02 7.020840e-03 2.437216e-02
 [2056] 8.928832e-04 5.913106e-02 4.335688e-03 1.295547e-02 3.966141e-02
 [2061] 6.638925e-03 1.433801e-01 2.009904e-04 1.428746e-02 3.989972e-03
 [2066] 1.189091e-03 2.873132e-03 6.043112e-03 8.237447e-03 6.612902e-04
 [2071] 5.888975e-03 1.225202e-03 9.385597e-03 5.664963e-04 3.515478e-02
 [2076] 1.742860e-04 2.583466e-03 2.236202e-03 1.971830e-02 3.129655e-03
 [2081] 1.774522e-02 6.188275e-04 4.907359e-03 3.592332e-03 9.138740e-03
 [2086] 1.609981e-02 4.543008e-02 2.975868e-03 7.741949e-02 1.664309e-03
 [2091] 2.421509e-03 4.187102e-03 2.696271e-03 1.495388e-03 1.456972e-04
 [2096] 1.423963e-02 2.774941e-03 2.002864e-03 1.580372e-03 3.250316e-02
 [2101] 1.310435e-01 1.277292e-02 5.170394e-03 3.103269e-02 5.813291e-02
 [2106] 7.510530e-03 7.969722e-02 6.638925e-03 2.286020e-02 7.534636e-03
 [2111] 3.898713e-03 1.112925e-03 2.248119e-03 3.094729e-03 1.107907e-03
 [2116] 3.583839e-03 2.215312e-02 1.005046e-02 1.074919e-01 3.442636e-01
 [2121] 8.778411e-03 1.254960e-02 2.575320e-02 2.617780e-02 1.967265e-03
 [2126] 1.398256e-03 6.251008e-03 2.257000e-03 3.388845e-04 5.006589e-03
 [2131] 1.524449e-04 5.373995e-04 2.967710e-03 1.326048e-03 1.589690e-02
 [2136] 3.681910e-03 2.054902e-02 1.161782e-04 2.167588e-01 1.191804e-03
 [2141] 1.602956e-03 5.575720e-04 3.388845e-04 7.101683e-04 8.601071e-03
 [2146] 4.718957e-04 3.253799e-04 3.027592e-03 7.027076e-02 2.612875e-03
 [2151] 1.216147e-03 1.113072e-02 3.811513e-04 9.336127e-04 4.439206e-03
 [2156] 1.158335e-03 5.317255e-03 5.494514e-04 4.203111e-04 1.648229e-02
 [2161] 5.040797e-04 1.384706e-04 2.238149e-02 2.596800e-02 3.195383e-03
 [2166] 6.787496e-04 2.331545e-02 3.032530e-03 1.145131e-02 2.996471e-03
 [2171] 1.940271e-03 1.510091e-03 2.696271e-03 3.901489e-04 1.579165e-03
 [2176] 1.710042e-03 4.359003e-03 9.569391e-04 1.693775e-03 1.596728e-02
 [2181] 2.000966e-03 3.622110e-02 2.048552e-02 9.222663e-02 2.969579e-04
 [2186] 7.346659e-02 5.192443e-04 1.756984e-03 7.563623e-04 4.431062e-02
 [2191] 3.430385e-02 6.527219e-03 7.237959e-03 1.596396e-02 7.333481e-03
 [2196] 8.376857e-04 3.901489e-04 4.404782e-04 1.633818e-03 1.467991e-02
 [2201] 7.369338e-03 2.129841e-03 8.295397e-02 5.238313e-02 1.324502e-03
 [2206] 1.146362e-04 2.212720e-01 1.074996e-03 1.235434e-03 1.223139e-02
 [2211] 1.704144e-02 1.252157e-02 1.500610e-03 6.822035e-03 2.579546e-03
 [2216] 2.293935e-03 1.655190e-03 1.779079e-02 2.144945e-01 1.295876e-03
 [2221] 5.195802e-03 2.314115e-02 1.608762e-02 1.307042e-03 4.887897e-04
 [2226] 2.792869e-02 3.188573e-02 2.236743e-02 9.199111e-04 1.458961e-02
 [2231] 1.809951e-02 3.392877e-04 5.545470e-04 4.623152e-03 2.754607e-03
 [2236] 1.492788e-03 2.442107e-04 6.289323e-03 1.344553e-02 1.031401e-03
 [2241] 3.216574e-04 9.043374e-04 4.425508e-02 2.207498e-03 4.651153e-03
 [2246] 1.377913e-03 1.443866e-02 9.615264e-04 7.606274e-03 1.191804e-03
 [2251] 2.097729e-04 3.901489e-04 1.579165e-03 9.721752e-04 1.130152e-02
 [2256] 1.189142e-03 2.102244e-01 1.427317e-03 2.421509e-03 4.187102e-03
 [2261] 2.696271e-03 1.495388e-03 4.952732e-04 4.498582e-03 1.026864e-03
 [2266] 3.271157e-02 2.348048e-03 1.410617e-03 5.018933e-03 3.074787e-03
 [2271] 1.572110e-03 1.159507e-01 5.484130e-04 1.541893e-03 4.652399e-03
 [2276] 4.016802e-02 1.505602e-03 2.663470e-03 7.939995e-02 5.927944e-04
 [2281] 5.426142e-04 3.647716e-04 2.855767e-02 3.964700e-03 4.619699e-04
 [2286] 1.105389e-02 2.712695e-01 3.893253e-03 1.107907e-03 2.314115e-02
 [2291] 1.608762e-02 8.284418e-03 2.010216e-03 5.195802e-03 5.202500e-04
 [2296] 4.760135e-03 3.322683e-03 2.306268e-03 6.479866e-04 1.928180e-03
 [2301] 7.189709e-02 3.198154e-03 1.784305e-03 1.366351e-01 1.465797e-02
 [2306] 2.562446e-03 1.309559e-03 7.527445e-04 4.372740e-02 2.106984e-02
 [2311] 7.369338e-03 2.276231e-03 2.005626e-02 1.284315e-02 8.192869e-03
 [2316] 2.812533e-03 4.047758e-03 7.670393e-04 1.945333e-03 1.988569e-03
 [2321] 2.136156e-03 1.041421e-02 1.098141e-03 3.419242e-02 2.355378e-02
 [2326] 2.961380e-02 3.490256e-03 2.467859e-03 1.753398e-04 1.330374e-03
 [2331] 2.627626e-03 2.225048e-03 9.025480e-04 4.847138e-03 1.126531e-02
 [2336] 1.774522e-02 2.238664e-03 3.874565e-03 8.270186e-04 1.933676e-03
 [2341] 4.387426e-03 8.912867e-03 2.860544e-03 7.794432e-04 1.398256e-03
 [2346] 2.696271e-03 3.529528e-03 2.097729e-04 1.495388e-03 1.456972e-04
 [2351] 1.423963e-02 2.774941e-03 3.916137e-03 3.832583e-03 2.250744e-02
 [2356] 4.519703e-02 5.145233e-04 1.053404e-02 7.776938e-03 1.025387e-02
 [2361] 3.401634e-04 6.448984e-03 1.765940e-01 5.904209e-03 1.193093e-03
 [2366] 2.241090e-01 2.139851e-02 6.620699e-04 2.967710e-03 9.065799e-03
 [2371] 2.185836e-03 2.043647e-04 2.985105e-03 8.296414e-04 2.251393e-03
 [2376] 5.477524e-04 7.203597e-03 1.913491e-02 7.873323e-04 2.804304e-02
 [2381] 4.919924e-03 4.972609e-03 1.083512e-03 2.627626e-03 1.074996e-03
 [2386] 2.950826e-03 2.772662e-03 4.379446e-03 3.325973e-03 3.811513e-04
 [2391] 1.905070e-03 1.057140e-02 2.581193e-03 5.347727e-04 6.243666e-04
 [2396] 1.191176e-02 2.021792e-02 2.072280e-03 5.386507e-03 1.558397e-03
 [2401] 1.295815e-02 3.136822e-04 7.151982e-03 1.183154e-02 4.791690e-03
 [2406] 1.138212e-01 2.123904e-02 5.588962e-03 1.533813e-03 1.436696e-02
 [2411] 1.711898e-03 1.568428e-04 1.067338e-01 2.558446e-04 1.155872e-03
 [2416] 1.432054e-03 3.122642e-03 3.883738e-04 1.432136e-03 2.554452e-02
 [2421] 1.486147e-02 4.228956e-04 4.314498e-04 9.411895e-03 4.332329e-03
 [2426] 8.667010e-04 1.223139e-02 5.570098e-03 1.549126e-02 5.864847e-03
 [2431] 5.904209e-03 5.807817e-03 2.250744e-02 7.370718e-03 3.095779e-03
 [2436] 1.412187e-02 2.517050e-04 2.174248e-02 3.459417e-02 5.406058e-03
 [2441] 1.750906e-03 3.099728e-01 1.296964e-02 1.477631e-03 4.224027e-03
 [2446] 9.472513e-03 1.095235e-02 8.871396e-04 2.281212e-02 2.579381e-03
 [2451] 3.869335e-02 2.403202e-03 1.409892e-02 2.191518e-02 2.918924e-04
 [2456] 1.565057e-02 3.051991e-04 6.417537e-03 6.567104e-04 1.306969e-02
 [2461] 4.335688e-03 3.834313e-02 5.386507e-03 7.377636e-03 1.629796e-01
 [2466] 1.218038e-04 6.453885e-03 2.671802e-03 1.138278e-03 4.347219e-04
 [2471] 3.937294e-03 3.003170e-03 2.661255e-04 1.286371e-02 9.569219e-02
 [2476] 2.819888e-03 7.105842e-04 9.293252e-03 5.613678e-02 1.864549e-02
 [2481] 1.178640e-02 2.373303e-03 4.192632e-04 2.617085e-04 1.468129e-03
 [2486] 1.162525e-02 1.664309e-03 6.656622e-04 7.170704e-03 1.367566e-01
 [2491] 2.215312e-02 3.548706e-04 1.596267e-02 3.647716e-04 4.132681e-02
 [2496] 2.677035e-04 2.643642e-02 6.149947e-02 1.949566e-02 2.250447e-03
 [2501] 2.198284e-02 3.154556e-03 1.157516e-02 4.335688e-03 1.709034e-02
 [2506] 1.585761e-02 2.355378e-02 2.961380e-02 2.814170e-04 2.795489e-03
 [2511] 9.375981e-04 4.104873e-02 1.875769e-03 9.293334e-03 4.906795e-01
 [2516] 2.545019e-02 6.620699e-04 2.869613e-04 3.142652e-02 3.465285e-03
 [2521] 4.371781e-04 1.130152e-02 6.090065e-02 9.836222e-02 1.875769e-03
 [2526] 7.828030e-03 1.289923e-01 1.945396e-03 7.802946e-03 7.677248e-04
 [2531] 2.796834e-01 2.969579e-04 4.833902e-03 4.592051e-03 3.396428e-03
 [2536] 5.956079e-05 2.266374e-02 1.191804e-03 4.914137e-04 2.257000e-03
 [2541] 3.388845e-04 1.479422e-03 4.952732e-04 4.498582e-03 8.619205e-04
 [2546] 8.290728e-04 1.160004e-02 7.685484e-03 2.771056e-03 7.580837e-03
 [2551] 2.189635e-02 8.770243e-02 4.261925e-04 2.444314e-02 1.284992e-03
 [2556] 1.073058e-01 5.385530e-04 3.102746e-04 4.778220e-02 5.407909e-04
 [2561] 5.139320e-03 3.705085e-04 5.039822e-03 8.538985e-03 2.855395e-03
 [2566] 1.244693e-03 2.238558e-03 9.119496e-02 4.535504e-04 1.304698e-02
 [2571] 1.509801e-02 1.785764e-02 1.227018e-03 2.057883e-04 2.781734e-02
 [2576] 4.367167e-03 8.405779e-04 5.001770e-03 2.789870e-03 7.527445e-04
 [2581] 1.777992e-02 1.415745e-03 2.759537e-04 3.207452e-03 4.953708e-02
 [2586] 8.667010e-04 1.223139e-02 5.570098e-03 4.849440e-03 6.449133e-03
 [2591] 2.618270e-03 2.684009e-02 3.169388e-02 1.709034e-02 5.960581e-04
 [2596] 1.815983e-02 7.274622e-02 1.734288e-01 4.400364e-03 7.500046e-03
 [2601] 4.724832e-03 8.759745e-02 1.303447e-03 1.568428e-04 6.913197e-03
 [2606] 8.289755e-03 4.762640e-04 4.666516e-03 3.136822e-04 1.462400e-01
 [2611] 8.619205e-04 8.290728e-04 1.160004e-02 2.650910e-03 1.639711e-02
 [2616] 1.858851e-03 3.532182e-02 4.599462e-02 1.288754e-02 9.293334e-03
 [2621] 1.551095e-01 2.591056e-03 6.723884e-02 3.234931e-03 1.307213e-02
 [2626] 3.642200e-03 5.765127e-03 4.319461e-04 2.220892e-03 3.402906e-04
 [2631] 3.257660e-03 1.159332e-02 2.752861e-03 7.539802e-03 6.459608e-03
 [2636] 3.122642e-03 3.883738e-04 6.331527e-04 6.583771e-02 7.020840e-03
 [2641] 6.486188e-03 4.050403e-03 3.997729e-03 2.735035e-04 7.493057e-03
 [2646] 1.404203e-02 6.817097e-04 2.942046e-02 1.921811e-03 3.660477e-01
 [2651] 3.234931e-03 1.307213e-02 3.642200e-03 5.765127e-03 4.319461e-04
 [2656] 2.220892e-03 3.402906e-04 3.257660e-03 1.159332e-02 2.752861e-03
 [2661] 7.539802e-03 6.459608e-03 3.122642e-03 1.157301e-04 4.424646e-03
 [2666] 2.023206e-02 5.904209e-03 1.193093e-03 2.854348e-02 2.679714e-02
 [2671] 5.866097e-04 1.340972e-02 3.169388e-02 4.933817e-03 2.942046e-02
 [2676] 1.921811e-03 3.660477e-01 8.313200e-04 1.392178e-02 1.395132e-03
 [2681] 2.238710e-01 8.928832e-04 6.486188e-03 4.050403e-03 3.997729e-03
 [2686] 5.855179e-03 4.312741e-04 3.995161e-03 1.225760e-03 6.807010e-03
 [2691] 3.316712e-04 1.639711e-02 3.248835e-02 6.985282e-03 8.405779e-04
 [2696] 1.270117e-02 4.832750e-02 2.169860e-02 2.775164e-02 4.380627e-02
 [2701] 9.371537e-03 5.929606e-03 1.866097e-03 4.213409e-03 6.055967e-03
 [2706] 9.026831e-04 2.687387e-02 4.115027e-04 9.925461e-02 1.834073e-04
 [2711] 2.985105e-03 4.565157e-05 4.132681e-02 2.677035e-04 8.665400e-03
 [2716] 6.520243e-02 1.528837e-02 8.405779e-04 1.270117e-02 2.340635e-01
 [2721] 7.357578e-04 3.482526e-03 9.049555e-02 5.359709e-03 2.735035e-04
 [2726] 1.695379e-02 2.588812e-03 1.199947e-02 4.420390e-03 1.405566e-01
 [2731] 7.903971e-04 2.737510e-02 1.236464e-03 3.908438e-03 2.623310e-02
 [2736] 1.259282e-02 7.835433e-04 1.456193e-02 2.724569e-02 7.609080e-04
 [2741] 5.258539e-03 1.921149e-02 3.691798e-02 5.271750e-04 5.006494e-03
 [2746] 2.778548e-02 7.767884e-03 1.383882e-04 3.445486e-03 3.738151e-03
 [2751] 3.435393e-03 1.654046e-03 5.369908e-02 5.317988e-04 5.752731e-03
 [2756] 1.510091e-03 6.251008e-03 2.257000e-03 3.388845e-04 4.158182e-04
 [2761] 2.042563e-02 1.135111e-03 2.901634e-04 2.584665e-03 7.592756e-01
 [2766] 4.994015e-04 1.122495e-02 1.154947e-02 1.115236e-02 4.490074e-04
 [2771] 7.078637e-03 2.408930e-04 1.407837e-03 6.217513e-03 3.448875e-03
 [2776] 1.722562e-02 4.543008e-02 7.839881e-04 4.907359e-03 3.592332e-03
 [2781] 9.138740e-03 2.380206e-03 7.083449e-02 3.199179e-03 2.972525e-03
 [2786] 2.275351e-03 1.002680e-03 2.382769e-02 1.191804e-03 4.914137e-04
 [2791] 5.422155e-04 7.089861e-03 7.182477e-04 1.633715e-02 9.411996e-04
 [2796] 5.252596e-02 1.084268e-03 1.014382e-03 8.289755e-03 2.403632e-03
 [2801] 1.292268e-03 2.332121e-04 2.972396e-01 6.211630e-04 5.271712e-04
 [2806] 5.202127e-04 2.795489e-03 9.375981e-04 4.104873e-02 6.783770e-03
 [2811] 5.082227e-02 1.487239e-02 1.125383e-03 3.417791e-03 1.396501e-03
 [2816] 5.006589e-03 1.524449e-04 7.851155e-03 1.284268e-04 1.947733e-03
 [2821] 1.577416e-03 2.403973e-03 3.127780e-03 3.946233e-04 3.402906e-04
 [2826] 3.136822e-04 4.976543e-02 7.357578e-04 1.249682e-02 1.615152e-02
 [2831] 3.123986e-02 2.654704e-03 2.474705e-03 2.558446e-04 2.882964e-04
 [2836] 6.289323e-03 1.344553e-02 1.902856e-04 7.860603e-03 1.225202e-03
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[18426] 3.661520e-05 2.524090e-03 1.079067e-02 4.086761e-03 1.054106e-04
[18431] 5.437636e-03 3.225089e-05 9.435261e-04 2.741457e-03 1.233468e-03
[18436] 9.145458e-03 3.513462e-02 4.228698e-05 2.936512e-03 1.461166e-03
[18441] 4.127013e-03 8.927206e-04 3.016850e-02 1.379462e-04 9.809841e-04
[18446] 4.686927e-03 1.812766e-04 1.692368e-02 5.146613e-04 1.864487e-04
[18451] 1.661192e-04 2.250485e-03 1.664600e-04 5.987560e-03 7.086290e-04
[18456] 2.815435e-02 4.029333e-04 3.145502e-04 6.798620e-03 8.663045e-04
[18461] 6.077794e-04 6.185158e-05 3.760185e-01 2.725533e-04 1.540264e-04
[18466] 1.745725e-04 1.074901e-03 1.258254e-04 6.494594e-03 7.683142e-04
[18471] 1.662991e-02 2.146563e-02 4.827367e-04 1.474428e-03 5.950846e-04
[18476] 2.765053e-03 5.930368e-05 3.459148e-03 5.059325e-05 1.014915e-03
[18481] 3.824335e-04 8.415459e-04 1.366810e-03 7.188213e-05 8.816889e-05
[18486] 6.534043e-05 2.567472e-02 3.706268e-05 4.673989e-03 2.527467e-02
[18491] 1.063209e-02 1.116266e-03 1.070160e-03 1.203519e-04 8.852641e-05
[18496] 2.713948e-03 2.314870e-02 3.661520e-05 2.507434e-03 5.938156e-04
[18501] 4.120048e-03 1.353248e-04 3.846350e-02 3.732273e-05 4.652729e-04
[18506] 3.051065e-04 9.651958e-02 2.099903e-04 9.285131e-04 3.711061e-04
[18511] 1.579600e-03 4.677686e-02 3.461078e-05 6.530995e-05 1.806357e-03
[18516] 4.354683e-03 1.959448e-03 4.395000e-05 3.793028e-02 1.506665e-02
[18521] 4.197044e-03 8.350305e-04 1.233468e-03 5.039441e-01 5.878405e-04
[18526] 8.994184e-05 5.148480e-04 7.473043e-04 1.372530e-02 4.395000e-05
[18531] 9.308010e-03 1.431460e-02 4.773315e-02 1.442552e-04 5.171712e-03
[18536] 1.407519e-02 7.736506e-03 1.858885e-03 8.722628e-04 9.594504e-04
[18541] 1.759023e-03 2.008645e-03 3.974761e-04 1.332936e-04 3.403126e-04
[18546] 8.070843e-03 6.888805e-05 6.122940e-03 3.824335e-04 1.918096e-04
[18551] 1.361336e-03 1.477820e-03 7.805777e-05 9.888371e-03 1.402056e-04
[18556] 2.127747e-02 3.451998e-02 6.918652e-04 5.484501e-03 3.225089e-05
[18561] 3.830014e-03 2.664018e-03 4.687484e-04 9.308010e-03 1.431460e-02
[18566] 4.773315e-02 1.442552e-04 1.008021e-02 1.926310e-02 2.464031e-03
[18571] 1.160279e-03 1.752384e-04 3.110848e-02 5.605583e-03 1.750418e-04
[18576] 8.927206e-04 1.705306e-02 4.413679e-03 1.538028e-04 8.145666e-05
[18581] 7.370036e-02 1.722604e-03 5.952100e-05 1.271045e-02 3.667513e-04
[18586] 4.119861e-03 7.233279e-04 1.233468e-03 9.060972e-04 2.203696e-03
[18591] 5.648418e-05 4.037401e-04 2.519959e-03 9.127636e-02 6.773812e-05
[18596] 1.860774e-03 1.717187e-02 2.059383e-04 1.332029e-03 2.026215e-03
[18601] 1.195242e-02 7.794498e-05 5.341752e-04 1.286404e-03 2.250485e-03
[18606] 1.664600e-04 1.165113e-03 5.418221e-04 1.481785e-01 1.732497e-03
[18611] 2.273543e-04 1.113681e-03 6.530507e-03 1.368709e-03 1.908623e-05
[18616] 3.170693e-02 2.725533e-04 1.540264e-04 1.745725e-04 7.080152e-03
[18621] 1.113185e-05 6.679639e-03 3.729396e-02 1.630701e-03 2.564701e-04
[18626] 3.216459e-03 3.406871e-03 9.651882e-04 9.127356e-05 2.648029e-03
[18631] 6.478245e-04 2.339542e-05 6.564188e-04 2.263183e-01 1.137708e-04
[18636] 1.184637e-03 2.120668e-04 7.820142e-04 6.534043e-05 1.073823e-01
[18641] 3.661520e-05 2.524090e-03 6.947871e-03 1.411124e-02 2.418335e-04
[18646] 4.858498e-04 2.725533e-04 8.145666e-05 3.986288e-03 1.209555e-03
[18651] 1.509758e-05 2.468765e-04 8.576259e-04 1.193972e-03 7.041166e-05
[18656] 6.519662e-02 1.531852e-03 3.353246e-03 3.051065e-04 2.731242e-02
[18661] 1.298052e-04 5.368075e-05 7.213587e-03 3.170725e-03 1.899281e-02
[18666] 1.442552e-04 1.703837e-03 1.113681e-03 1.752806e-02 1.545384e-03
[18671] 1.451334e-04 9.542925e-04 2.578531e-02 6.440805e-03 1.469474e-03
[18676] 1.895421e-04 1.430915e-02 1.423779e-03 7.924961e-04 9.131483e-03
[18681] 7.681753e-04 1.115425e-02 2.348625e-03 1.709136e-03 1.019141e-02
[18686] 1.030604e-02 4.802168e-05 1.554438e-04 7.523040e-03 1.752806e-02
[18691] 1.545384e-03 1.451334e-04 9.542925e-04 9.187681e-03 5.746520e-02
[18696] 5.198578e-04 4.395000e-05 3.793028e-02 1.506665e-02 7.924961e-04
[18701] 9.131483e-03 3.667513e-04 5.959326e-02 1.516258e-03 1.547218e-03
[18706] 1.962147e-03 9.121830e-02 3.461078e-05 6.530995e-05 1.806357e-03
[18711] 4.354683e-03 1.959448e-03 1.451334e-04 9.542925e-04 9.187681e-03
[18716] 5.746520e-02 5.198578e-04 4.395000e-05 7.596134e-02 2.239254e-02
[18721] 5.991709e-04 8.255138e-04 1.722604e-03 1.286913e-02 1.152219e-03
[18726] 1.232758e-02 1.303092e-03 4.773315e-02 1.442552e-04 1.008021e-02
[18731] 6.358515e-03 6.996752e-03 4.328378e-04 2.127516e-03 1.780054e-04
[18736] 1.563963e-03 2.008645e-03 3.974761e-04 2.817265e-04 6.496322e-04
[18741] 1.001603e-02 3.054677e-04 9.429227e-04 2.283560e-03 2.121053e-04
[18746] 1.361336e-03 1.477820e-03 2.861503e-04 6.392775e-03 1.634780e-04
[18751] 1.008021e-02 2.623681e-03 3.771947e-03 8.275626e-04 3.347286e-04
[18756] 8.415920e-05 9.931740e-03 2.059285e-03 2.330698e-03 1.895421e-04
[18761] 1.430915e-02 1.423779e-03 3.007222e-03 2.801379e-02 1.693370e-03
[18766] 1.088583e-01 1.456118e-04 9.913365e-03 2.274993e-03 3.245544e-04
[18771] 1.627550e-03 2.009459e-03 2.390093e-03 4.998734e-04 2.947569e-04
[18776] 5.938156e-04 4.120048e-03 1.353248e-04 3.846350e-02 3.732273e-05
[18781] 1.557025e-03 9.171305e-04 9.270157e-03 1.242839e-03 8.418957e-03
[18786] 1.556285e-04 2.936512e-03 1.461166e-03 4.127013e-03 7.533659e-03
[18791] 3.513462e-02 1.369523e-04

[[3]]
    [1] 2.241042e-02 3.688604e-04 4.494632e-04 2.613471e-04 1.508726e-04
    [6] 1.122233e-04 7.107218e-06 6.462406e-04 5.122102e-04 1.721165e-04
   [11] 8.042709e-05 3.657377e-03 1.722425e-02 9.093345e-03 2.533488e-04
   [16] 2.403937e-03 1.089165e-02 7.342035e-04 1.637178e-02 3.581075e-04
   [21] 2.715215e-03 2.203617e-03 7.240164e-04 2.658494e-04 1.658437e-04
   [26] 1.353724e-04 1.492475e-04 2.162623e-04 1.513101e-03 2.062280e-03
   [31] 1.325514e-02 9.446897e-02 9.637943e-04 6.816019e-04 4.131985e-03
   [36] 5.737191e-03 3.412119e-04 1.106152e-04 3.400920e-04 2.120497e-04
   [41] 1.122560e-05 2.671861e-04 1.444226e-05 1.493354e-05 1.381286e-04
   [46] 5.427188e-05 1.278277e-03 1.116159e-03 1.538470e-03 5.308727e-06
   [51] 5.868551e-02 5.673424e-05 1.111022e-04 6.477577e-05 1.122560e-05
   [56] 2.170992e-05 1.024889e-03 4.412162e-05 7.274888e-05 1.475052e-04
   [61] 1.191423e-02 5.794381e-04 1.510423e-04 2.224675e-03 1.290506e-05
   [66] 1.347320e-04 2.875085e-04 6.325221e-05 5.960397e-03 2.752420e-05
   [71] 1.189323e-05 3.875573e-03 1.614374e-05 4.036031e-05 2.234088e-03
   [76] 4.463304e-03 5.382331e-03 4.719361e-05 2.833190e-03 1.352068e-04
   [81] 1.290331e-02 1.547757e-03 9.127178e-05 8.509109e-05 1.508726e-04
   [86] 2.252818e-05 6.703164e-05 1.168779e-04 1.138339e-03 7.260416e-05
   [91] 1.814931e-04 8.343061e-04 8.210125e-05 4.511176e-03 7.940692e-03
   [96] 8.291868e-03 2.825212e-05 1.143999e-02 2.112575e-05 2.117334e-04
  [101] 2.586411e-05 3.213769e-03 1.247167e-02 3.892384e-03 5.546348e-04
  [106] 1.671551e-03 5.686537e-04 4.626493e-05 2.252818e-05 1.560494e-05
  [111] 8.311631e-05 8.056048e-03 8.268853e-03 1.666058e-04 8.441846e-03
  [116] 8.917468e-03 7.331745e-05 5.389133e-06 4.807187e-02 5.108287e-05
  [121] 1.951476e-04 4.117823e-03 1.203145e-03 7.576993e-04 6.380499e-05
  [126] 2.966760e-04 5.708624e-04 1.471016e-04 3.220700e-04 1.371352e-03
  [131] 9.257607e-02 5.042735e-04 2.431774e-04 3.685801e-03 9.925365e-04
  [136] 3.310516e-04 2.111777e-05 1.896092e-03 1.251462e-02 3.071609e-03
  [141] 2.709844e-04 1.117670e-03 1.425750e-03 2.186070e-05 1.113191e-04
  [146] 6.759053e-04 1.259559e-04 2.170131e-04 7.691490e-06 7.515159e-04
  [151] 3.013169e-03 1.964822e-04 6.435654e-04 3.703864e-05 5.081463e-03
  [156] 1.964873e-04 5.150259e-04 1.650161e-04 3.367814e-03 1.775505e-04
  [161] 1.239780e-03 5.673424e-05 1.255509e-05 2.252818e-05 6.703164e-05
  [166] 8.996668e-05 1.252849e-03 1.038959e-04 2.138922e-01 1.578553e-04
  [171] 4.494632e-04 2.613471e-04 1.508726e-04 1.122233e-04 2.652751e-05
  [176] 2.129053e-04 4.380837e-05 5.442054e-03 2.198883e-04 5.644944e-05
  [181] 1.748591e-03 2.267341e-04 2.163672e-04 9.237038e-02 2.041119e-05
  [186] 2.115404e-04 2.353973e-04 9.960882e-03 6.195474e-05 4.199171e-04
  [191] 1.046909e-02 6.592061e-05 4.582769e-05 1.057271e-05 6.146875e-03
  [196] 4.278194e-04 7.915433e-05 1.858907e-03 2.539356e-01 3.322337e-04
  [201] 1.492475e-04 3.685801e-03 9.925365e-04 3.286971e-03 2.829343e-04
  [206] 2.431774e-04 4.215815e-05 3.087644e-04 2.172023e-04 1.697137e-03
  [211] 2.514085e-05 1.119067e-04 1.800695e-02 2.540633e-04 1.020671e-04
  [216] 2.948691e-02 1.469513e-03 4.200521e-04 1.358215e-04 2.775456e-05
  [221] 7.327330e-03 1.226637e-03 8.268853e-03 2.139875e-04 2.468631e-03
  [226] 1.071495e-03 3.623832e-04 1.793453e-04 4.485087e-04 3.953858e-05
  [231] 1.570273e-04 9.615502e-05 9.864701e-05 1.094990e-03 2.754757e-04
  [236] 5.523190e-03 2.336949e-03 2.617226e-03 4.232374e-04 1.620482e-04
  [241] 7.346726e-06 3.130187e-04 1.238314e-04 1.550208e-04 1.711257e-04
  [246] 2.734841e-04 6.796950e-04 9.689434e-04 3.104610e-04 1.707935e-03
  [251] 8.162724e-05 1.995334e-04 3.216134e-04 7.598071e-04 3.817782e-04
  [256] 2.082424e-04 1.106152e-04 1.508726e-04 3.910600e-04 1.255509e-05
  [261] 1.122233e-04 7.107218e-06 6.462406e-04 5.122102e-04 4.672819e-04
  [266] 2.145395e-04 2.718631e-03 3.956007e-03 5.278752e-05 6.662853e-04
  [271] 3.590299e-04 8.594640e-04 1.980680e-05 1.180243e-03 5.144683e-02
  [276] 3.669521e-04 1.073467e-04 4.401618e-02 6.518809e-03 2.507529e-04
  [281] 1.381286e-04 7.950448e-04 6.523384e-05 2.401336e-05 1.708231e-04
  [286] 5.276096e-05 6.312939e-04 1.660108e-05 3.713056e-04 1.389437e-03
  [291] 6.199009e-05 7.212074e-03 8.038413e-04 5.269528e-04 8.638976e-05
  [296] 1.238314e-04 5.108287e-05 1.615743e-03 2.323988e-04 3.150140e-04
  [301] 4.716116e-04 1.290506e-05 6.284892e-05 3.438917e-03 1.222290e-04
  [306] 1.762900e-04 2.011864e-05 8.537585e-04 1.128663e-02 3.216719e-03
  [311] 2.988607e-04 8.402163e-05 8.990670e-04 9.199432e-06 1.478165e-03
  [316] 1.392685e-03 9.677777e-04 2.796740e-02 5.984122e-03 2.932514e-04
  [321] 5.494848e-05 1.452931e-03 7.447022e-05 1.156939e-05 4.287594e-02
  [326] 1.381175e-05 2.344992e-04 1.640103e-04 3.041282e-04 1.781173e-05
  [331] 1.706055e-03 3.167757e-03 1.178754e-03 5.141228e-05 1.435934e-05
  [336] 6.691032e-04 1.149104e-03 4.706780e-05 4.117823e-03 2.871398e-04
  [341] 9.473469e-04 9.145814e-04 3.669521e-04 7.355924e-04 2.718631e-03
  [346] 3.221169e-04 5.157719e-04 1.122103e-03 1.034092e-05 1.233843e-03
  [351] 4.578522e-03 3.761371e-03 7.510171e-05 6.923952e-02 1.442501e-03
  [356] 5.867606e-05 3.250288e-04 1.369610e-03 1.799191e-03 5.882916e-05
  [361] 2.351052e-03 2.387419e-04 5.155119e-03 1.034534e-04 1.201603e-03
  [366] 2.336620e-03 1.385730e-05 1.909583e-03 8.475003e-06 1.548825e-03
  [371] 8.426389e-05 5.716826e-04 2.792313e-04 4.469961e-02 2.988607e-04
  [376] 4.578342e-04 1.952279e-02 3.131207e-06 3.750537e-04 2.599173e-04
  [381] 3.678155e-05 4.013931e-05 7.515481e-04 1.533990e-04 2.628310e-04
  [386] 6.447958e-04 5.852928e-02 3.521069e-04 8.297002e-05 9.984741e-04
  [391] 9.416534e-03 2.158230e-03 6.729004e-04 1.218009e-04 1.419137e-05
  [396] 1.794570e-05 1.644198e-04 8.621734e-04 3.688604e-04 8.474344e-04
  [401] 4.796951e-04 6.787825e-02 1.513101e-03 1.073753e-04 1.564009e-03
  [406] 1.057271e-05 9.162321e-03 9.230555e-06 3.445375e-03 1.087360e-02
  [411] 1.301707e-03 1.244229e-04 3.980598e-03 2.776465e-04 2.069384e-03
  [416] 2.792313e-04 9.879204e-03 1.071008e-03 2.336949e-03 2.617226e-03
  [421] 9.289175e-06 1.591162e-04 3.704832e-05 1.563926e-01 1.131783e-04
  [426] 5.733521e-04 2.027185e-01 2.416862e-03 2.507529e-04 8.811593e-06
  [431] 4.962887e-03 1.711901e-04 1.283703e-04 1.252849e-03 7.670328e-03
  [436] 8.833494e-02 1.131783e-04 8.347026e-04 9.549758e-02 1.238263e-04
  [441] 4.649441e-03 5.342805e-05 6.116976e-02 2.825212e-05 2.205735e-04
  [446] 3.004741e-04 1.867613e-04 2.397327e-06 1.014074e-02 5.673424e-05
  [451] 2.800026e-05 2.120497e-04 1.122560e-05 7.406289e-05 2.652751e-05
  [456] 2.129053e-04 3.433047e-05 9.908522e-05 6.399611e-04 5.494103e-04
  [461] 1.337992e-04 4.087308e-04 3.509336e-03 8.516240e-03 3.307938e-05
  [466] 2.936389e-03 6.710586e-05 1.936146e-02 3.254899e-05 9.791543e-06
  [471] 8.864735e-03 3.162843e-05 2.283911e-03 4.174403e-05 3.192823e-04
  [476] 6.016191e-04 1.229297e-04 9.398777e-05 1.250781e-04 2.218328e-02
  [481] 4.002657e-05 4.245809e-02 1.401702e-03 1.140582e-03 2.602273e-04
  [486] 4.552466e-06 4.620422e-03 1.157666e-03 4.129406e-05 7.187753e-04
  [491] 3.252285e-04 2.775456e-05 6.076766e-03 5.858500e-05 8.684120e-06
  [496] 2.533037e-04 6.073648e-03 4.706780e-05 4.117823e-03 2.871398e-04
  [501] 2.779980e-04 4.403715e-04 1.617966e-04 3.797401e-03 3.701848e-03
  [506] 9.879204e-03 2.024279e-05 1.078251e-03 1.423924e-02 2.260101e-02
  [511] 8.238360e-04 6.969499e-04 2.197483e-04 2.919602e-02 5.332298e-05
  [516] 1.156939e-05 1.387377e-03 6.658413e-04 6.198796e-04 8.156307e-04
  [521] 9.199432e-06 3.792131e-02 3.433047e-05 9.908522e-05 6.399611e-04
  [526] 1.145409e-04 9.503209e-04 8.849497e-05 6.568133e-03 7.176715e-03
  [531] 7.775891e-04 5.733521e-04 2.737411e-02 1.049792e-04 3.541105e-02
  [536] 5.348228e-04 6.814323e-04 2.674884e-04 7.502946e-04 1.216831e-05
  [541] 2.010010e-03 1.078880e-05 1.533479e-04 4.666079e-03 1.437666e-04
  [546] 4.217744e-04 3.783856e-04 3.041282e-04 1.781173e-05 1.060155e-03
  [551] 6.094068e-03 3.931456e-04 4.239660e-04 6.821728e-04 2.850029e-04
  [556] 1.187090e-04 5.461294e-04 1.017109e-03 1.772791e-03 2.008557e-03
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 [1466] 2.288827e-04 4.700951e-05 2.056872e-01 1.540923e-04 7.987645e-04
 [1471] 2.927670e-01 3.655034e-03 3.673194e-04 9.065498e-06 6.018245e-03
 [1476] 2.280034e-04 1.487589e-04 2.042812e-03 1.182841e-02 1.276891e-01
 [1481] 1.540923e-04 7.040572e-04 1.421615e-01 1.413301e-04 7.335228e-03
 [1486] 6.461052e-05 8.559894e-02 2.649783e-05 3.347461e-04 3.602649e-04
 [1491] 2.866598e-04 2.210468e-06 1.334608e-02 6.595769e-05 3.712023e-05
 [1496] 2.611117e-04 1.102541e-05 1.108495e-04 2.995946e-05 3.089575e-04
 [1501] 3.943518e-05 9.610055e-05 9.958438e-04 6.050126e-04 1.921541e-04
 [1506] 5.945922e-04 3.557910e-03 1.409370e-02 3.744923e-05 4.019794e-03
 [1511] 7.875064e-05 3.552183e-02 4.121692e-05 1.011751e-05 1.352021e-02
 [1516] 3.650666e-05 3.904506e-03 4.699973e-05 4.712721e-04 7.111805e-04
 [1521] 1.357951e-04 1.222332e-04 1.796608e-04 3.338717e-02 4.701816e-05
 [1526] 5.570287e-02 2.318688e-03 1.720680e-03 3.727829e-04 5.560211e-06
 [1531] 6.751663e-03 1.311665e-03 5.484877e-05 8.283384e-04 4.830358e-04
 [1536] 3.383458e-05 8.341196e-03 6.120968e-05 1.081082e-05 3.131801e-04
 [1541] 7.383130e-03 6.504001e-05 5.857038e-03 3.860063e-04 3.292419e-04
 [1546] 6.154166e-04 1.830590e-04 6.472051e-03 5.700252e-03 1.099982e-02
 [1551] 2.699771e-05 1.669260e-03 2.006094e-02 3.559724e-02 1.191882e-03
 [1556] 1.128979e-03 2.817590e-04 2.090539e-02 6.220569e-05 9.838158e-06
 [1561] 1.436232e-03 1.001398e-03 7.378435e-04 1.102037e-03 9.914880e-06
 [1566] 6.688963e-02 3.943518e-05 9.610055e-05 9.958438e-04 1.292608e-04
 [1571] 1.559567e-03 1.186792e-04 1.000521e-02 1.034630e-02 1.127897e-03
 [1576] 7.987645e-04 4.399829e-02 1.299148e-04 6.704977e-02 7.402394e-04
 [1581] 1.000554e-03 3.018291e-04 1.062123e-03 1.516802e-05 1.879472e-03
 [1586] 1.284905e-05 2.390310e-04 6.779416e-03 1.867611e-04 5.994422e-04
 [1591] 4.605369e-04 4.784101e-04 1.930729e-05 1.011309e-03 9.644584e-03
 [1596] 4.806122e-04 6.460804e-04 1.152360e-03 3.593017e-04 1.027878e-04
 [1601] 7.750532e-04 1.491597e-03 2.156309e-03 3.090795e-03 9.296605e-05
 [1606] 2.835558e-01 7.402394e-04 1.000554e-03 3.018291e-04 1.062123e-03
 [1611] 1.516802e-05 1.879472e-03 1.284905e-05 2.390310e-04 6.779416e-03
 [1616] 1.867611e-04 5.994422e-04 4.605369e-04 4.784101e-04 3.657869e-06
 [1621] 7.122454e-03 2.517908e-03 4.442321e-04 1.469433e-04 5.522676e-03
 [1626] 4.887884e-03 2.943110e-04 1.447790e-03 5.700252e-03 1.181733e-03
 [1631] 3.090795e-03 9.296605e-05 2.835558e-01 1.442625e-04 4.316814e-03
 [1636] 6.286251e-05 1.129926e-01 4.528204e-05 6.460804e-04 1.152360e-03
 [1641] 3.593017e-04 1.246738e-02 4.333326e-05 3.492126e-04 3.984551e-04
 [1646] 4.992036e-04 1.374669e-05 1.559567e-03 3.311137e-03 3.425079e-03
 [1651] 5.484877e-05 1.265187e-03 1.034934e-02 3.792446e-03 4.197230e-02
 [1656] 5.936719e-03 9.823103e-04 9.812801e-04 2.183737e-04 6.361754e-04
 [1661] 4.218907e-04 1.657477e-03 4.235806e-03 2.372640e-05 1.629527e-02
 [1666] 9.080097e-06 2.181708e-04 1.079351e-06 1.858439e-02 1.043024e-05
 [1671] 1.079569e-03 8.184107e-03 1.379339e-03 5.484877e-05 1.265187e-03
 [1676] 1.473855e-01 5.225645e-05 1.220959e-03 1.820978e-01 3.617297e-04
 [1681] 1.027878e-04 1.476487e-03 1.221515e-04 1.778644e-02 3.369421e-04
 [1686] 3.423423e-02 3.726978e-05 7.339252e-03 4.815683e-05 2.910395e-04
 [1691] 4.876929e-03 1.151859e-03 3.770416e-05 1.295446e-03 4.660789e-03
 [1696] 2.709259e-05 4.141071e-04 4.008589e-03 1.269080e-02 5.009154e-05
 [1701] 4.637855e-04 7.639639e-03 1.001980e-03 1.220916e-05 1.858363e-04
 [1706] 3.888629e-04 6.324787e-04 8.668170e-05 2.743468e-02 2.361662e-05
 [1711] 5.152960e-03 1.017805e-04 5.068761e-04 2.611117e-04 1.102541e-05
 [1716] 1.264323e-05 3.003103e-03 6.338023e-05 1.822902e-05 2.113617e-04
 [1721] 8.717605e-01 7.414640e-05 6.393799e-03 1.650094e-03 1.458561e-03
 [1726] 1.706936e-05 8.580801e-04 8.733892e-06 1.480561e-02 3.954435e-04
 [1731] 1.698549e-04 2.059798e-02 1.041532e-02 2.697726e-05 2.114200e-03
 [1736] 1.891471e-04 8.135753e-04 3.720275e-04 1.223122e-02 1.551456e-04
 [1741] 3.252439e-03 7.513891e-04 4.158649e-05 5.542657e-03 6.595769e-05
 [1746] 3.712023e-05 3.688359e-05 4.158397e-04 2.627158e-05 8.130329e-03
 [1751] 1.696598e-04 8.489979e-03 1.099877e-04 4.235569e-05 1.001398e-03
 [1756] 5.926490e-03 6.111836e-04 6.832842e-06 1.902645e-01 4.489287e-05
 [1761] 3.416236e-05 1.790233e-04 2.288827e-04 4.700951e-05 2.056872e-01
 [1766] 8.118222e-04 6.397928e-03 5.549563e-03 1.016357e-04 7.123885e-04
 [1771] 1.753606e-04 4.693587e-04 1.505621e-05 1.399129e-03 6.428097e-06
 [1776] 2.666791e-04 9.537737e-05 1.283083e-04 1.858238e-04 1.876893e-05
 [1781] 1.284905e-05 9.914880e-06 2.522650e-02 5.225645e-05 1.594486e-02
 [1786] 8.232594e-03 3.712530e-03 3.263200e-04 1.663695e-04 1.780028e-05
 [1791] 5.660286e-05 7.914150e-04 4.848875e-03 3.172953e-05 4.036814e-02
 [1796] 6.952996e-05 6.811217e-04 4.412889e-05 9.157045e-03 1.294051e-05
 [1801] 4.441429e-03 3.428661e-05 2.989220e-02 8.235823e-05 5.490737e-03
 [1806] 4.914250e-05 2.567499e-04 7.999703e-02 2.362253e-05 1.027878e-04
 [1811] 2.573001e-04 1.510033e-03 2.395761e-02 7.115642e-05 1.031032e-02
 [1816] 5.700252e-03 1.099982e-02 2.111981e-04 1.698549e-04 2.839146e-01
 [1821] 4.981541e-04 5.639972e-05 5.791363e-05 3.790504e-04 5.321443e-03
 [1826] 7.115642e-05 8.023299e-03 3.935284e-03 3.423423e-02 3.726978e-05
 [1831] 1.957547e-03 2.310687e-03 2.970135e-03 2.663118e-03 1.223047e-04
 [1836] 2.167466e-04 3.895518e-04 2.588952e-04 8.811150e-05 2.740550e-04
 [1841] 6.925038e-05 1.782851e-03 1.021048e-04 1.471673e-03 9.537737e-05
 [1846] 2.740586e-05 2.048167e-04 3.914805e-04 2.709259e-05 3.559712e-03
 [1851] 5.956717e-05 1.783705e-02 6.081048e-03 9.864137e-04 4.730726e-03
 [1856] 8.733892e-06 1.870389e-03 8.456046e-04 4.442321e-04 8.023299e-03
 [1861] 3.935284e-03 3.423423e-02 3.726978e-05 3.941533e-03 3.583428e-03
 [1866] 9.622517e-04 2.053214e-03 1.628900e-05 6.528161e-03 1.103593e-03
 [1871] 4.194427e-05 3.720275e-04 5.676966e-03 7.237487e-03 7.761269e-04
 [1876] 4.549871e-05 1.820745e-02 3.394365e-04 1.814365e-04 3.104040e-03
 [1881] 9.296605e-05 1.037467e-01 1.384206e-04 1.698549e-04 1.230374e-03
 [1886] 5.482147e-04 3.418767e-05 7.131524e-04 4.218907e-04 4.326749e-02
 [1891] 7.046101e-04 8.322254e-04 5.298647e-03 7.128548e-05 1.679661e-04
 [1896] 3.006721e-04 1.655102e-02 5.943608e-05 9.879702e-05 1.665627e-04
 [1901] 4.158397e-04 2.627158e-05 9.969264e-04 8.589652e-05 5.655524e-02
 [1906] 8.772202e-04 6.130679e-05 1.300207e-04 2.334959e-03 7.904984e-04
 [1911] 2.377634e-06 3.835121e-02 4.489287e-05 3.416236e-05 1.790233e-04
 [1916] 2.709320e-03 3.657869e-06 4.482033e-02 1.270217e-02 1.293560e-02
 [1921] 1.422839e-04 9.812801e-04 1.116919e-03 7.983585e-04 1.456599e-05
 [1926] 2.693786e-03 4.501987e-04 9.411355e-06 1.133727e-04 1.203347e-01
 [1931] 1.048650e-04 1.183719e-03 2.964850e-05 2.852007e-04 9.914880e-06
 [1936] 1.906433e-01 7.414640e-05 6.393799e-03 1.009096e-03 3.734345e-03
 [1941] 3.014828e-05 1.119830e-04 4.489287e-05 2.213123e-05 5.875492e-03
 [1946] 1.797855e-04 5.046288e-06 1.088843e-04 9.989619e-05 2.932936e-04
 [1951] 1.004672e-03 6.082242e-02 3.828718e-04 2.517176e-03 3.428661e-05
 [1956] 5.899992e-03 1.097617e-04 9.610055e-05 9.958438e-04 7.819876e-04
 [1961] 1.085283e-02 3.726978e-05 5.189477e-04 1.300207e-04 6.195945e-03
 [1966] 3.745093e-03 4.788279e-04 1.585159e-04 7.582564e-03 1.732784e-03
 [1971] 1.184737e-03 4.528204e-05 8.418696e-03 3.996188e-04 1.181733e-03
 [1976] 3.090795e-03 3.502096e-04 7.172559e-03 3.872205e-04 4.915523e-04
 [1981] 2.655405e-03 7.786374e-03 1.853892e-04 3.149983e-05 1.495640e-03
 [1986] 6.195945e-03 3.745093e-03 4.788279e-04 1.585159e-04 1.669260e-03
 [1991] 2.006094e-02 3.193621e-03 7.115642e-05 1.031032e-02 5.700252e-03
 [1996] 1.181733e-03 3.090795e-03 9.296605e-05 4.811667e-02 5.189599e-04
 [2001] 1.000554e-03 3.142859e-04 5.158718e-02 2.362253e-05 1.027878e-04
 [2006] 2.573001e-04 1.510033e-03 2.395761e-02 4.788279e-04 1.585159e-04
 [2011] 1.669260e-03 2.006094e-02 3.193621e-03 7.115642e-05 2.934675e-02
 [2016] 8.263161e-03 7.160831e-04 7.691461e-04 3.394365e-04 8.977636e-03
 [2021] 4.787928e-04 1.311758e-02 3.369421e-04 3.423423e-02 3.726978e-05
 [2026] 3.941533e-03 1.152360e-03 2.305354e-03 1.423467e-03 3.407348e-04
 [2031] 2.199800e-05 8.319033e-04 2.588952e-04 8.811150e-05 4.295977e-04
 [2036] 9.052033e-05 2.043622e-03 3.256369e-04 3.175743e-04 8.233823e-04
 [2041] 2.686516e-05 2.048167e-04 3.914805e-04 8.603122e-05 1.447790e-03
 [2046] 8.599567e-05 3.941533e-03 3.820967e-04 1.448519e-03 1.283784e-03
 [2051] 7.832793e-05 2.727852e-05 1.986478e-03 4.806122e-04 3.389935e-03
 [2056] 4.528204e-05 8.418696e-03 3.996188e-04 4.660237e-03 6.889927e-03
 [2061] 4.280019e-04 6.534468e-02 1.857486e-05 2.255753e-03 7.329092e-04
 [2066] 5.014480e-05 3.174916e-04 3.915720e-04 7.224898e-04 2.993931e-04
 [2071] 1.717911e-03 6.952996e-05 6.811217e-04 4.412889e-05 9.157045e-03
 [2076] 1.294051e-05 1.529273e-02 9.808634e-05 1.638978e-03 2.785547e-04
 [2081] 1.621751e-03 8.042176e-05 2.114200e-03 1.891471e-04 8.135753e-04
 [2086] 4.877608e-03 1.041532e-02 1.392275e-04 3.740888e-02 5.303998e-04
 [2091] 5.023393e-04 3.207349e-04 1.631988e-04 1.718294e-04 6.977632e-06
 [2096] 1.023701e-03 7.514979e-04 2.277662e-04 9.733591e-05 5.298647e-03
 [2101] 2.702609e-02 1.490500e-02 3.425081e-04 3.052959e-03 1.617356e-02
 [2106] 7.549627e-04 2.914268e-02 4.280019e-04 3.941533e-03 3.583428e-03
 [2111] 9.622517e-04 3.168935e-04 2.065064e-04 1.571542e-04 1.792244e-04
 [2116] 2.932936e-04 2.276885e-03 3.209133e-03 2.030788e-02 1.452309e-01
 [2121] 1.285106e-03 1.058401e-03 6.081792e-03 7.542857e-03 4.174024e-04
 [2126] 1.322159e-04 5.068761e-04 2.611117e-04 1.102541e-05 4.693587e-04
 [2131] 1.505621e-05 1.908349e-05 1.716701e-04 5.600865e-05 1.889620e-03
 [2136] 1.618287e-03 2.654460e-03 5.237553e-06 7.652968e-02 6.595769e-05
 [2141] 1.645727e-04 7.671252e-05 1.102541e-05 2.390220e-05 1.411857e-03
 [2146] 5.235913e-05 6.772047e-05 1.804411e-04 1.446131e-02 9.424754e-04
 [2151] 2.075702e-04 2.375762e-03 1.272380e-05 1.085509e-04 3.247398e-04
 [2156] 8.703813e-05 9.274858e-03 4.266335e-05 1.311838e-05 4.442458e-03
 [2161] 1.762753e-05 3.537078e-05 3.431109e-03 6.619891e-03 7.146492e-03
 [2166] 4.488039e-05 3.198868e-03 1.925481e-04 2.046253e-02 2.251098e-03
 [2171] 1.106033e-04 1.017805e-04 1.631988e-04 1.850280e-05 7.842219e-05
 [2176] 1.370334e-04 1.926954e-03 8.669991e-05 1.918532e-04 1.442771e-03
 [2181] 1.027433e-04 6.629343e-03 1.270217e-02 1.293560e-02 2.649783e-05
 [2186] 1.664263e-02 2.199800e-05 3.322068e-04 3.014828e-05 4.845152e-03
 [2191] 1.963029e-02 6.163212e-03 7.311957e-04 2.242081e-03 5.459256e-04
 [2196] 4.031165e-05 1.850280e-05 1.677008e-05 1.184612e-04 1.232237e-02
 [2201] 1.294408e-02 2.073011e-04 1.249302e-02 1.212007e-02 1.082760e-04
 [2206] 5.127826e-06 6.280632e-02 5.525582e-05 2.839233e-04 5.857038e-03
 [2211] 1.564056e-03 9.603462e-04 6.610976e-05 4.220870e-04 9.449451e-04
 [2216] 1.775770e-04 3.908289e-04 2.030514e-03 1.412878e-01 7.444873e-04
 [2221] 3.065486e-04 5.091569e-03 1.367936e-03 4.134977e-04 2.372073e-05
 [2226] 2.688876e-03 1.938812e-02 4.743994e-03 2.885967e-04 1.473729e-03
 [2231] 1.624186e-03 2.850786e-05 9.784905e-05 8.342216e-04 1.825339e-04
 [2236] 3.493816e-04 7.685114e-06 7.914150e-04 4.848875e-03 2.711800e-04
 [2241] 7.135441e-04 3.181194e-05 6.090930e-03 1.796901e-04 7.867426e-04
 [2246] 2.535660e-04 5.422196e-03 2.111929e-04 1.570088e-03 6.595769e-05
 [2251] 1.207300e-05 1.850280e-05 7.842219e-05 7.696242e-05 2.042812e-03
 [2256] 1.298243e-04 3.137757e-01 2.195807e-04 5.023393e-04 3.207349e-04
 [2261] 1.631988e-04 1.718294e-04 2.995946e-05 3.089575e-04 5.570153e-05
 [2266] 7.586532e-03 2.552212e-04 6.127447e-05 1.220617e-03 2.994158e-04
 [2271] 2.569359e-04 1.382326e-01 2.213670e-05 2.727881e-04 3.138759e-04
 [2276] 1.109875e-02 8.654151e-05 5.125526e-04 1.507060e-02 6.965707e-05
 [2281] 6.314937e-05 1.032530e-05 8.820117e-03 6.001893e-04 1.010065e-04
 [2286] 2.538706e-03 3.527368e-01 4.617025e-04 1.792244e-04 5.091569e-03
 [2291] 1.367936e-03 4.458582e-03 3.380366e-04 3.065486e-04 3.713675e-05
 [2296] 3.666433e-04 2.809596e-04 1.931551e-03 3.149983e-05 1.378977e-04
 [2301] 1.772708e-02 3.153809e-04 1.394299e-04 3.838047e-02 2.572505e-03
 [2306] 3.742413e-04 1.876949e-04 3.383458e-05 1.176826e-02 1.812080e-03
 [2311] 1.294408e-02 2.734153e-04 3.273337e-03 1.641798e-03 5.406297e-04
 [2316] 2.342184e-04 4.501987e-04 4.549871e-05 2.078831e-04 1.390629e-04
 [2321] 1.112371e-04 1.537658e-03 2.982136e-04 8.654102e-03 3.377196e-03
 [2326] 4.096589e-03 4.888184e-04 2.453490e-04 8.812240e-06 3.786980e-04
 [2331] 1.546799e-04 1.496747e-04 1.710120e-04 3.707160e-04 8.866560e-04
 [2336] 1.621751e-03 3.909589e-04 2.663118e-03 1.223047e-04 2.167466e-04
 [2341] 3.895518e-04 1.057795e-03 5.301590e-04 2.505703e-04 1.322159e-04
 [2346] 1.631988e-04 4.823815e-04 1.207300e-05 1.718294e-04 6.977632e-06
 [2351] 1.023701e-03 7.514979e-04 5.958181e-04 2.759670e-04 2.759840e-03
 [2356] 5.988163e-03 6.902608e-05 1.019191e-03 5.003205e-04 1.244234e-03
 [2361] 2.345103e-05 1.451449e-03 7.020107e-02 4.442321e-04 1.469433e-04
 [2366] 6.716529e-02 9.754669e-03 3.673194e-04 1.716701e-04 8.003925e-04
 [2371] 9.657855e-05 2.259496e-05 2.181708e-04 6.077537e-05 1.001239e-03
 [2376] 2.074218e-05 4.635516e-04 1.878605e-03 7.115642e-05 1.081181e-02
 [2381] 1.317250e-03 8.016318e-04 1.012876e-04 1.546799e-04 5.525582e-05
 [2386] 2.044305e-03 3.475489e-04 4.468343e-04 4.790042e-04 1.272380e-05
 [2391] 9.032278e-05 6.333255e-03 1.479253e-04 1.596067e-04 2.185422e-05
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[17971] 3.875851e-03 5.830882e-06 7.477665e-04 8.346178e-04 1.210590e-04
[17976] 7.772782e-05 1.528894e-05 7.416578e-06 1.929326e-04 2.745763e-05
[17981] 1.523082e-05 5.139229e-04 7.589657e-06 2.207684e-03 1.847146e-03
[17986] 1.201674e-03 4.220238e-05 1.136720e-04 1.589379e-06 2.541660e-04
[17991] 2.174864e-05 1.878178e-05 3.563608e-05 1.581868e-04 4.951033e-04
[17996] 4.593940e-04 8.911193e-05 3.917743e-03 3.123786e-05 4.091528e-05
[18001] 1.098866e-04 4.652232e-04 1.711516e-04 4.684963e-05 3.836488e-05
[18006] 3.282048e-05 1.567900e-04 1.870151e-06 4.313755e-05 1.749474e-06
[18011] 1.611315e-04 1.445210e-04 9.298936e-06 1.757864e-04 4.812314e-04
[18016] 9.098281e-04 1.968065e-05 2.449345e-04 2.897230e-04 1.058372e-04
[18021] 6.004189e-07 7.206986e-04 2.897794e-02 2.218083e-05 6.354588e-05
[18026] 3.287733e-02 3.725002e-03 1.644822e-04 1.874413e-04 1.567108e-04
[18031] 1.905942e-05 3.933704e-06 4.747367e-05 1.234223e-05 6.178220e-04
[18036] 2.870049e-06 1.805087e-04 1.364325e-04 3.425795e-06 3.861541e-03
[18041] 4.680873e-04 3.100960e-04 4.735901e-05 2.174864e-05 3.963697e-06
[18046] 3.974359e-04 1.368293e-04 1.159695e-04 7.537101e-05 1.028520e-06
[18051] 2.857121e-05 3.239323e-03 6.399187e-06 3.085384e-05 8.570042e-06
[18056] 4.227221e-04 1.072314e-02 5.748719e-03 9.041196e-05 1.355521e-05
[18061] 5.354220e-04 2.343684e-06 8.797467e-04 4.482381e-05 8.909151e-05
[18066] 1.234716e-02 2.227426e-03 1.474782e-05 2.489061e-05 4.787364e-04
[18071] 3.738940e-05 1.433029e-06 7.488237e-02 8.059531e-06 9.364176e-05
[18076] 4.771961e-05 1.975922e-04 6.332700e-07 9.795231e-04 2.690699e-03
[18081] 2.919670e-04 2.674691e-06 3.390090e-06 4.200831e-04 5.250162e-04
[18086] 5.734000e-06 3.239790e-03 8.871789e-06 7.846328e-05 3.635877e-04
[18091] 2.218083e-05 1.007540e-03 4.812314e-04 7.841024e-05 2.660015e-04
[18096] 2.950672e-04 1.794748e-07 6.898633e-04 2.162647e-03 2.465591e-05
[18101] 2.723150e-05 6.529443e-02 4.872546e-04 2.513508e-05 1.408025e-04
[18106] 7.315851e-04 9.435979e-04 3.279273e-06 1.408882e-03 8.559534e-05
[18111] 1.684480e-03 4.916840e-06 2.855903e-03 1.373387e-03 8.322870e-07
[18116] 2.248275e-03 2.152673e-06 9.577466e-04 1.813073e-05 1.013995e-04
[18121] 6.810615e-05 1.473773e-02 9.041196e-05 3.293914e-04 1.359210e-02
[18126] 1.385327e-07 1.981539e-04 1.354109e-04 2.020593e-06 3.084524e-06
[18131] 5.453072e-04 7.079760e-06 7.637955e-05 3.280997e-04 2.914848e-02
[18136] 1.183722e-05 1.063355e-05 3.752829e-04 8.130616e-03 1.364850e-03
[18141] 1.770538e-04 6.250709e-05 8.336786e-06 3.329335e-06 2.447944e-05
[18146] 4.098446e-04 1.015554e-04 2.585949e-04 3.912741e-04 5.473304e-02
[18151] 7.967150e-05 1.958942e-05 7.926215e-04 1.252014e-06 9.766169e-03
[18156] 6.939054e-07 5.305156e-04 9.231695e-03 1.417532e-05 9.090252e-05
[18161] 2.637804e-03 8.322174e-05 1.424529e-03 6.810615e-05 1.877788e-03
[18166] 2.483252e-04 1.847146e-03 1.201674e-03 5.194808e-07 4.340830e-05
[18171] 3.111479e-06 6.827458e-02 7.399163e-06 1.781473e-04 2.874321e-01
[18176] 1.070899e-04 1.644822e-04 3.035187e-06 1.257541e-03 3.412659e-05
[18181] 1.168197e-05 6.486524e-04 2.061176e-03 6.046059e-02 7.399163e-06
[18186] 1.085552e-04 9.604731e-02 3.212991e-05 4.234471e-03 2.182541e-05
[18191] 3.646849e-02 2.938589e-06 9.859468e-05 8.668330e-05 1.685215e-04
[18196] 2.000087e-07 9.105675e-03 1.247311e-05 1.595839e-05 2.026531e-04
[18201] 1.123645e-06 2.191069e-05 1.443867e-05 9.025890e-05 2.178383e-06
[18206] 4.015618e-06 3.251001e-04 4.558787e-05 4.153002e-05 1.942752e-04
[18211] 1.582648e-03 1.258279e-03 6.873683e-06 1.728245e-03 2.720082e-05
[18216] 2.596983e-02 7.700770e-06 1.190864e-06 3.025980e-03 8.286712e-06
[18221] 5.262750e-04 1.593101e-05 8.257798e-05 6.525499e-04 1.245533e-05
[18226] 2.352328e-05 5.328489e-05 5.963623e-03 7.098978e-06 7.147068e-02
[18231] 7.739724e-04 1.924865e-04 1.416637e-04 4.774070e-07 1.128857e-02
[18236] 1.439970e-03 6.287128e-06 5.577491e-04 4.038402e-04 6.778907e-06
[18241] 4.642472e-02 1.386777e-05 1.453598e-06 2.926107e-04 2.696316e-03
[18246] 5.734000e-06 3.239790e-03 8.871789e-06 1.523082e-05 1.331982e-04
[18251] 8.036852e-06 2.988747e-03 1.227640e-03 1.877788e-03 3.914921e-06
[18256] 4.735727e-04 8.455563e-03 2.776432e-04 1.265071e-04 7.694807e-04
[18261] 1.836260e-04 1.124756e-02 2.138033e-05 1.433029e-06 2.718559e-03
[18266] 3.003953e-04 7.216574e-04 4.137787e-04 2.343684e-06 3.966666e-02
[18271] 2.178383e-06 4.015618e-06 3.251001e-04 1.278662e-05 1.589499e-04
[18276] 1.193933e-05 1.580009e-03 5.001494e-04 5.708006e-05 1.781473e-04
[18281] 2.715373e-02 1.682018e-06 2.860433e-02 1.884873e-04 4.317019e-05
[18286] 1.434257e-04 2.065861e-03 1.966118e-06 8.197473e-04 2.472486e-06
[18291] 3.750045e-05 2.529721e-03 3.538038e-05 1.924747e-04 5.018139e-05
[18296] 1.975922e-04 6.332700e-07 3.264436e-04 4.437897e-03 6.326488e-05
[18301] 1.050689e-04 4.678786e-04 6.813523e-05 1.723725e-05 2.643592e-04
[18306] 1.867481e-04 9.059764e-04 6.075622e-04 6.618546e-06 1.356823e-01
[18311] 1.884873e-04 4.317019e-05 1.434257e-04 2.065861e-03 1.966118e-06
[18316] 8.197473e-04 2.472486e-06 3.750045e-05 2.529721e-03 3.538038e-05
[18321] 1.924747e-04 5.018139e-05 1.975922e-04 1.581219e-07 4.314635e-03
[18326] 7.197039e-04 2.218083e-05 6.354588e-05 2.152382e-03 1.535954e-03
[18331] 6.518543e-05 4.172569e-04 1.227640e-03 2.690525e-04 6.075622e-04
[18336] 6.618546e-06 1.356823e-01 2.284112e-05 8.728552e-05 2.199089e-05
[18341] 4.461803e-02 4.698212e-06 1.050689e-04 4.678786e-04 6.813523e-05
[18346] 4.017555e-03 1.613387e-05 5.218818e-05 1.215372e-04 7.510229e-05
[18351] 1.082505e-06 1.589499e-04 3.805757e-04 1.017478e-03 6.287128e-06
[18356] 2.900351e-03 4.511044e-04 1.549340e-04 1.164515e-02 3.196653e-04
[18361] 2.134341e-04 3.310151e-04 2.958827e-05 3.706227e-04 1.165127e-04
[18366] 1.052899e-03 1.515077e-04 3.734815e-06 6.906009e-03 1.117806e-06
[18371] 4.747367e-05 9.192468e-08 9.766169e-03 6.939054e-07 5.865911e-05
[18376] 2.856587e-03 1.735053e-05 6.287128e-06 2.900351e-03 6.177995e-03
[18381] 1.742052e-06 3.193014e-04 1.135265e-01 6.468192e-06 1.723725e-05
[18386] 4.582960e-04 1.150616e-05 9.774069e-03 6.431326e-05 5.401832e-03
[18391] 3.514419e-06 6.845550e-03 7.589811e-06 7.143857e-05 2.216541e-03
[18396] 1.407655e-05 2.784433e-06 4.166430e-04 4.535315e-03 1.604729e-06
[18401] 1.010178e-04 1.308067e-03 2.537684e-03 3.074234e-05 1.068806e-04
[18406] 2.836901e-03 3.046824e-04 1.123966e-06 5.538577e-05 7.445837e-05
[18411] 1.876470e-04 1.559797e-05 1.165451e-02 7.119245e-07 2.149719e-03
[18416] 2.399930e-05 2.759584e-04 2.026531e-04 1.123645e-06 2.210722e-06
[18421] 1.167025e-02 8.035503e-06 1.689065e-06 4.365643e-04 3.613227e-02
[18426] 4.867228e-06 1.120228e-03 6.489397e-04 2.876579e-04 3.174062e-06
[18431] 8.024593e-04 4.940740e-07 5.982490e-03 1.613786e-04 3.076012e-05
[18436] 1.426241e-02 3.547727e-03 5.854444e-07 1.735575e-03 4.837575e-05
[18441] 2.101379e-04 9.643731e-05 2.600572e-03 2.308895e-06 2.999046e-03
[18446] 2.085678e-04 2.592253e-06 2.997847e-03 1.247311e-05 1.595839e-05
[18451] 9.988391e-06 1.073187e-04 4.260067e-06 3.028100e-03 9.881105e-05
[18456] 3.595467e-03 2.411558e-05 5.280883e-05 3.003953e-04 5.317464e-03
[18461] 7.119848e-04 1.622565e-06 1.299760e-01 4.066028e-05 6.287128e-06
[18466] 4.140899e-05 4.340830e-05 3.111479e-06 6.827458e-02 6.659485e-05
[18471] 1.388130e-03 2.664474e-03 2.844818e-05 3.472759e-04 2.948783e-05
[18476] 9.608086e-05 3.151348e-06 1.494359e-04 9.579629e-07 3.212178e-05
[18481] 5.143987e-05 1.097985e-04 4.574240e-05 2.033782e-06 2.472486e-06
[18486] 2.343684e-06 1.446173e-02 1.742052e-06 3.614384e-03 4.216214e-03
[18491] 6.988340e-04 1.252932e-04 1.930180e-04 8.059531e-06 1.536711e-05
[18496] 2.053463e-04 2.467165e-03 4.867228e-06 2.841838e-02 1.315915e-05
[18501] 2.026597e-04 1.167444e-05 5.100241e-03 1.251923e-06 1.851921e-03
[18506] 8.407180e-06 2.483337e-02 1.266188e-05 2.182240e-04 9.933350e-06
[18511] 5.508836e-05 3.146492e-02 3.592419e-07 1.723725e-05 6.346580e-05
[18516] 1.737950e-04 8.621606e-03 3.425795e-06 3.549102e-03 1.227640e-03
[18521] 1.877788e-03 2.998861e-05 3.076012e-05 2.016962e-01 1.692904e-05
[18526] 1.068111e-05 9.998082e-06 4.568095e-05 1.721720e-03 3.425795e-06
[18531] 3.372812e-03 9.515532e-04 5.401832e-03 3.514419e-06 3.503690e-03
[18536] 8.280148e-04 8.227058e-04 3.917743e-03 3.123786e-05 4.091528e-05
[18541] 1.098866e-04 6.534075e-05 2.043515e-05 4.945989e-05 7.792645e-05
[18546] 4.039677e-04 2.035820e-05 5.797027e-04 5.143987e-05 9.458158e-06
[18551] 4.819586e-05 3.594787e-04 1.604729e-06 8.269219e-04 7.399163e-06
[18556] 2.499448e-02 3.280430e-03 1.550900e-05 2.489570e-03 4.940740e-07
[18561] 8.445195e-04 1.057019e-04 2.218083e-05 3.372812e-03 9.515532e-04
[18566] 5.401832e-03 3.514419e-06 2.837129e-03 1.350929e-03 3.341804e-04
[18571] 5.791805e-04 3.504806e-06 4.124864e-03 3.960855e-04 4.052202e-06
[18576] 9.643731e-05 1.235062e-03 2.472666e-04 2.251628e-04 7.416578e-06
[18581] 9.734555e-03 4.967980e-05 2.327211e-05 2.094754e-03 6.618546e-06
[18586] 5.665943e-02 9.685611e-05 3.076012e-05 3.340659e-04 1.165953e-04
[18591] 1.644899e-06 3.646120e-04 1.165127e-04 2.018317e-02 2.383646e-06
[18596] 2.397051e-04 1.131383e-03 7.621667e-06 4.792008e-05 8.735216e-05
[18601] 7.409219e-03 6.571782e-06 3.891973e-05 4.753021e-05 1.073187e-04
[18606] 4.260067e-06 4.419678e-04 2.723150e-05 2.738704e-02 2.283855e-04
[18611] 8.071687e-05 2.655910e-05 6.766246e-04 7.868880e-04 3.194488e-07
[18616] 2.258427e-02 4.066028e-05 6.287128e-06 4.140899e-05 4.627332e-04
[18621] 1.581219e-07 2.638271e-02 4.931209e-03 5.853247e-05 2.621096e-05
[18626] 3.310151e-04 2.037682e-04 5.584662e-04 1.600837e-06 1.758650e-03
[18631] 1.528894e-05 9.634096e-07 4.912655e-05 7.723325e-02 1.067611e-05
[18636] 3.252277e-04 5.138041e-06 8.895486e-05 2.343684e-06 1.249198e-01
[18641] 4.867228e-06 1.120228e-03 6.614573e-04 4.011299e-03 2.017417e-05
[18646] 1.716190e-05 4.066028e-05 7.416578e-06 2.309776e-03 8.738386e-05
[18651] 6.524595e-07 2.147818e-05 2.448109e-05 5.855794e-05 6.365050e-05
[18656] 3.729774e-02 5.381232e-05 7.066109e-04 8.407180e-06 2.877570e-03
[18661] 9.346765e-06 4.015618e-06 3.251001e-04 1.499959e-04 2.388357e-03
[18666] 3.514419e-06 4.144973e-04 2.655910e-05 1.925690e-03 7.493275e-04
[18671] 1.395644e-05 2.771706e-05 2.351588e-03 4.872546e-04 5.806052e-05
[18676] 4.698212e-06 1.053377e-03 6.810615e-05 2.690525e-04 6.075622e-04
[18681] 1.854324e-05 2.251805e-03 9.186937e-05 5.217798e-05 5.184933e-04
[18686] 6.652865e-04 1.634190e-06 8.105512e-06 4.333519e-04 1.925690e-03
[18691] 7.493275e-04 1.395644e-05 2.771706e-05 4.735727e-04 8.455563e-03
[18696] 2.427283e-05 3.425795e-06 3.549102e-03 1.227640e-03 2.690525e-04
[18701] 6.075622e-04 6.618546e-06 1.770965e-02 6.383463e-05 4.317019e-05
[18706] 7.329090e-05 2.702321e-02 3.592419e-07 1.723725e-05 6.346580e-05
[18711] 1.737950e-04 8.621606e-03 1.395644e-05 2.771706e-05 4.735727e-04
[18716] 8.455563e-03 2.427283e-05 3.425795e-06 8.809760e-03 1.655765e-03
[18721] 8.911193e-05 2.926107e-04 4.967980e-05 1.041220e-03 1.088341e-04
[18726] 7.061179e-04 6.431326e-05 5.401832e-03 3.514419e-06 2.837129e-03
[18731] 4.678786e-04 5.507672e-04 3.992991e-04 1.384277e-04 2.796395e-06
[18736] 2.945280e-04 6.534075e-05 2.043515e-05 9.145547e-05 3.738940e-05
[18741] 6.812614e-04 1.530453e-04 7.933000e-05 7.964328e-04 7.283861e-06
[18746] 4.819586e-05 3.594787e-04 1.300603e-05 4.172569e-04 1.239860e-05
[18751] 2.837129e-03 1.067494e-04 2.921547e-04 4.413987e-04 6.403860e-06
[18756] 2.094457e-06 7.009836e-04 6.326488e-05 2.723498e-04 4.698212e-06
[18761] 1.053377e-03 6.810615e-05 2.353667e-03 2.588705e-03 5.095818e-05
[18766] 5.395724e-02 4.230799e-06 7.458285e-04 3.079393e-04 6.359060e-06
[18771] 6.671572e-05 6.512072e-05 8.292098e-05 7.524026e-05 3.114349e-05
[18776] 1.315915e-05 2.026597e-04 1.167444e-05 5.100241e-03 1.251923e-06
[18781] 7.187084e-03 2.390373e-05 9.135586e-04 1.514877e-04 4.593940e-04
[18786] 1.757607e-05 1.735575e-03 4.837575e-05 2.101379e-04 2.394917e-03
[18791] 3.547727e-03 2.425897e-06

>   
> 
> 
> 
> 
> cleanEx()
> nameEx("numericalDNA")
> ### * numericalDNA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: numericalDNA
> ### Title: Conversion of nucleotides to numerical vectors
> ### Aliases: numericalDNA
> 
> ### ** Examples
> 
> require("MEET")
> background<-c(0.25, 0.25, 0.25, 0.25)
> numericA<-numericalDNA(background)
> 
> 
> 
> cleanEx()
> nameEx("probability")
> ### * probability
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: probability
> ### Title: Probability
> ### Aliases: probability
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> data(BackgroundOrganism)
> wind<-TranscriptionFactor
> probability(wind,Prob)
        1    2    3    4 5 6 7 8    9  10   11   12
[1,] 0.00 0.00 0.10 0.95 0 0 0 0 0.00 0.1 0.25 0.20
[2,] 0.45 0.15 0.45 0.05 1 1 0 1 0.90 0.3 0.40 0.25
[3,] 0.45 0.60 0.40 0.00 0 0 0 0 0.05 0.5 0.15 0.25
[4,] 0.10 0.25 0.05 0.00 0 0 1 0 0.05 0.1 0.20 0.30
> 
> 
> 
> cleanEx()
> nameEx("probability.couple")
> ### * probability.couple
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: probability.couple
> ### Title: Background joint probability
> ### Aliases: probability.couple
> 
> ### ** Examples
> 
> require("MEET")
> data(BackgroundOrganism)
> probability.couple(Prob)
 [1] 0.1024 0.1024 0.0576 0.0576 0.1024 0.1024 0.0576 0.0576 0.0576 0.0576
[11] 0.0324 0.0324 0.0576 0.0576 0.0324 0.0324
> 
> 
> 
> cleanEx()
> nameEx("pvalue")
> ### * pvalue
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pvalue
> ### Title: P value
> ### Aliases: pvalue
> 
> ### ** Examples
> 
> require("MEET")
> dist<-rnorm(10000,0,1)
> pvalue(a=3.298,dist)
[1] 4e-04
> 
> 
> cleanEx()
> nameEx("readMEME")
> ### * readMEME
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: readMEME
> ### Title: Read MEME motifs and consensus sequences
> ### Aliases: readMEME
> 
> ### ** Examples
> 
> resultat<-"/memeout/meme.txt"
> num_motif=2
> readMEME(resultat, num_motif)
[1] "null"
[1] "null"
[1] FALSE FALSE
[[1]]
NULL

[[2]]
NULL

[[3]]
[1] FALSE FALSE

> 
> 
> 
> cleanEx()
> nameEx("redundancy")
> ### * redundancy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: redundancy
> ### Title: To calculate redundancy
> ### Aliases: redundancy
> 
> ### ** Examples
> 
> require("MEET")
> data(BackgroundOrganism)
> data(iicc)
> q<-iicc$q
> Factortrans<-iicc$Transcriptionfactor
> HXmax<-iicc$HXmax
> correction<-correction.entropy(q,p=nrow(Factortrans),long=1,iicc)
> Herror<-slot(correction,"Herror")
> pmX<-probability(Factortrans,Prob)
> HX<-switch(iicc$classentropy,"Shannon"=entropy.Shannon(pmX),"Renyi"=entropy.Renyi(pmX,q))
> test<-redundancy(HX,HXmax,Herror,finite=nrow(Factortrans),correction=TRUE)
> 
> 
> cleanEx()
> nameEx("writeMEME")
> ### * writeMEME
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: writeMEME
> ### Title: Write a training matrix in a MEME/MAST format
> ### Aliases: writeMEME
> 
> ### ** Examples
> 
> require("MEET")
> data(TranscriptionFactor)
> matriu<-TranscriptionFactor
> writeMEME(matriu, m=1)
      [,1] [,2] [,3] [,4]
 [1,]    0    9    2    9
 [2,]    0   12    5    3
 [3,]    2    8    1    9
 [4,]   19    0    0    1
 [5,]    0    0    0   20
 [6,]    0    0    0   20
 [7,]    0    0   20    0
 [8,]    0    0    0   20
 [9,]    0    1    1   18
[10,]    2   10    2    6
[11,]    5    3    4    8
[12,]    4    5    6    5
[1] 1
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  653.212 2.93 659.286 0.007 0.017 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')
==21311== 
==21311== HEAP SUMMARY:
==21311==     in use at exit: 338,963,252 bytes in 75,453 blocks
==21311==   total heap usage: 646,607 allocs, 571,154 frees, 3,494,988,713 bytes allocated
==21311== 
==21311== LEAK SUMMARY:
==21311==    definitely lost: 0 bytes in 0 blocks
==21311==    indirectly lost: 0 bytes in 0 blocks
==21311==      possibly lost: 0 bytes in 0 blocks
==21311==    still reachable: 338,963,252 bytes in 75,453 blocks
==21311==         suppressed: 0 bytes in 0 blocks
==21311== Reachable blocks (those to which a pointer was found) are not shown.
==21311== To see them, rerun with: --leak-check=full --show-leak-kinds=all
==21311== 
==21311== For lists of detected and suppressed errors, rerun with: -s
==21311== ERROR SUMMARY: 53850 errors from 4 contexts (suppressed: 0 from 0)