R Under development (unstable) (2019-11-20 r77445) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> pkgname <- "BatchMap"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('BatchMap')
Loading required package: parallel
Loading required package: ggplot2
> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("add.marker")
> ### * add.marker
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: add.marker
> ### Title: Creates a new sequence by adding markers.
> ### Aliases: add.marker
> 
> ### ** Examples
> 
> data(example.out)
> twopt <- rf.2pts(example.out)
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
> all.mark <- make.seq(twopt,"all")
> groups <- group(all.mark)
   Selecting markers: 
	  group    1 
	   .............../usr/local/bin/../include/c++/v1/memory:1825:35: runtime error: nan is outside the range of representable values of type 'int'
    #0 0x7f1cb6c7e11c in void std::__1::allocator<int>::construct<int, double&>(int*, double&) /usr/local/bin/../include/c++/v1/memory:1825:35
    #1 0x7f1cb6c7e11c in void std::__1::allocator_traits<std::__1::allocator<int> >::__construct<int, double&>(std::__1::integral_constant<bool, true>, std::__1::allocator<int>&, int*, double&) /usr/local/bin/../include/c++/v1/memory:1717:18
    #2 0x7f1cb6c7e11c in void std::__1::allocator_traits<std::__1::allocator<int> >::construct<int, double&>(std::__1::allocator<int>&, int*, double&) /usr/local/bin/../include/c++/v1/memory:1560:14
    #3 0x7f1cb6c7dfa5 in void std::__1::allocator_traits<std::__1::allocator<int> >::__construct_range_forward<double*, int*>(std::__1::allocator<int>&, double*, double*, int*&) /usr/local/bin/../include/c++/v1/memory:1645:17
    #4 0x7f1cb6c7ddcb in std::__1::enable_if<__is_forward_iterator<double*>::value, void>::type std::__1::vector<int, std::__1::allocator<int> >::__construct_at_end<double*>(double*, double*, unsigned long) /usr/local/bin/../include/c++/v1/vector:1074:5
    #5 0x7f1cb6c7cbc3 in std::__1::vector<int, std::__1::allocator<int> >::vector<double*>(double*, std::__1::enable_if<(__is_forward_iterator<double*>::value) && (is_constructible<int, std::__1::iterator_traits<double*>::reference>::value), double*>::type) /usr/local/bin/../include/c++/v1/vector:1222:9
    #6 0x7f1cb6c75ef7 in est_rf_out(Rcpp::Vector<14, Rcpp::PreserveStorage>, int, Rcpp::Vector<14, Rcpp::PreserveStorage>, int, bool) /data/gannet/ripley/R/packages/tests-clang-SAN/BatchMap/src/twopts_out.cpp:110:24
    #7 0x7f1cb6c74799 in est_rf_out_wrap /data/gannet/ripley/R/packages/tests-clang-SAN/BatchMap/src/twopts_out.cpp:54:12
    #8 0x6f1dc5 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:606:17
    #9 0x73c02a in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1276:11
    #10 0x853590 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7053:14
    #11 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #12 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #13 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #14 0x85ccc1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7021:12
    #15 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #16 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #17 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #18 0x85ccc1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7021:12
    #19 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #20 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #21 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #22 0x83dd99 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:811:12
    #23 0x8aece7 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2919:8
    #24 0x83d51a in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12
    #25 0x975bb6 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2
    #26 0x97a130 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:314:11
    #27 0x979f15 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1113:5
    #28 0x4da35a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #29 0x7f1cc8130f42 in __libc_start_main (/lib64/libc.so.6+0x23f42)
    #30 0x43036d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43036d)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /usr/local/bin/../include/c++/v1/memory:1825:35 in 
twopts_out.cpp:162:10: runtime error: nan is outside the range of representable values of type 'int'
    #0 0x7f1cb6c7a2f6 in est_rf_out(Rcpp::Vector<14, Rcpp::PreserveStorage>, int, Rcpp::Vector<14, Rcpp::PreserveStorage>, int, bool) /data/gannet/ripley/R/packages/tests-clang-SAN/BatchMap/src/twopts_out.cpp:162:10
    #1 0x7f1cb6c74799 in est_rf_out_wrap /data/gannet/ripley/R/packages/tests-clang-SAN/BatchMap/src/twopts_out.cpp:54:12
    #2 0x6f1dc5 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:606:17
    #3 0x73c02a in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1276:11
    #4 0x853590 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7053:14
    #5 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #6 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #7 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #8 0x85ccc1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7021:12
    #9 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #10 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #11 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #12 0x85ccc1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7021:12
    #13 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #14 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #15 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #16 0x83dd99 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:811:12
    #17 0x8aece7 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2919:8
    #18 0x83d51a in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12
    #19 0x975bb6 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2
    #20 0x97a130 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:314:11
    #21 0x979f15 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1113:5
    #22 0x4da35a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #23 0x7f1cc8130f42 in __libc_start_main (/lib64/libc.so.6+0x23f42)
    #24 0x43036d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43036d)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior twopts_out.cpp:162:10 in 
twopts_out.cpp:162:29: runtime error: nan is outside the range of representable values of type 'int'
    #0 0x7f1cb6c7a34b in est_rf_out(Rcpp::Vector<14, Rcpp::PreserveStorage>, int, Rcpp::Vector<14, Rcpp::PreserveStorage>, int, bool) /data/gannet/ripley/R/packages/tests-clang-SAN/BatchMap/src/twopts_out.cpp:162:29
    #1 0x7f1cb6c74799 in est_rf_out_wrap /data/gannet/ripley/R/packages/tests-clang-SAN/BatchMap/src/twopts_out.cpp:54:12
    #2 0x6f1dc5 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:606:17
    #3 0x73c02a in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1276:11
    #4 0x853590 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7053:14
    #5 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #6 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #7 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #8 0x85ccc1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7021:12
    #9 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #10 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #11 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #12 0x85ccc1 in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7021:12
    #13 0x83cee3 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:688:8
    #14 0x8a11c3 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c
    #15 0x89e50f in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1778:16
    #16 0x83dd99 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:811:12
    #17 0x8aece7 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2919:8
    #18 0x83d51a in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:763:12
    #19 0x975bb6 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264:2
    #20 0x97a130 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:314:11
    #21 0x979f15 in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1113:5
    #22 0x4da35a in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29:5
    #23 0x7f1cc8130f42 in __libc_start_main (/lib64/libc.so.6+0x23f42)
    #24 0x43036d in _start (/data/gannet/ripley/R/R-clang-SAN/bin/exec/R+0x43036d)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior twopts_out.cpp:162:29 in 

	  group    2 
	   ..........
	  group    3 
	   .....
> (LG1 <- make.seq(groups,1))

Number of markers: 15
Markers in the sequence:
M1 M2 M3 M5 M6 M10 M11 M12 M14 M15 M17 M25 M26 M28 M30

Parameters not estimated.

> (LG.aug<-add.marker(LG1, c(4,7)))

Number of markers: 17
Markers in the sequence:
M1 M2 M3 M5 M6 M10 M11 M12 M14 M15 M17 M25 M26 M28 M30 M4 M7

Parameters not estimated.

> 
> 
> 
> 
> cleanEx()
> nameEx("combine.onemap")
> ### * combine.onemap
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: combine.onemap
> ### Title: Combine OneMap datasets
> ### Aliases: combine.onemap
> ### Keywords: IO
> 
> ### ** Examples
> 
> 
>   ## Not run: 
> ##D     combined_data <- combine.onemap(onemap_data1, onemap_data2)
> ##D   
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("compare")
> ### * compare
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: compare
> ### Title: Compare all possible orders (exhaustive search) for a given
> ###   sequence of markers
> ### Aliases: compare
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   #outcrossing example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   markers <- make.seq(twopt,c(12,14,15,26,28))
> ##D   (markers.comp <- compare(markers))
> ##D   (markers.comp <- compare(markers,verbose=TRUE))
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> 
> cleanEx()
> nameEx("create.data.bins")
> ### * create.data.bins
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: create.data.bins
> ### Title: New dataset based on bins
> ### Aliases: create.data.bins
> ### Keywords: bins dimension reduction
> 
> ### ** Examples
> 
>  ## Not run: 
> ##D   load(url("https://github.com/mmollina/data/raw/master/fake_big_data_f2.RData"))
> ##D   fake.big.data.f2
> ##D   (bins <- find.bins(fake.big.data.f2, exact=FALSE))
> ##D   (new.data <- create.data.bins(fake.big.data.f2, bins))
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("draw.map")
> ### * draw.map
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: draw.map
> ### Title: Draw a genetic map
> ### Aliases: draw.map
> ### Keywords: rqtl
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D  #outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   lg<-group(make.seq(twopt, "all"))
> ##D   maps<-vector("list", lg$n.groups)
> ##D   for(i in 1:lg$n.groups)
> ##D      maps[[i]]<- make.seq(order.seq(input.seq= make.seq(lg,i),twopt.alg =
> ##D    "rcd"), "force")
> ##D   draw.map(maps, grid=TRUE)
> ##D   draw.map(maps, grid=TRUE, horizontal=TRUE)
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("drop.marker")
> ### * drop.marker
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: drop.marker
> ### Title: Creates a new sequence by dropping markers.
> ### Aliases: drop.marker
> 
> ### ** Examples
> 
> data(example.out)
> twopt <- rf.2pts(example.out)
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
> all.mark <- make.seq(twopt,"all")
> groups <- group(all.mark)
   Selecting markers: 
	  group    1 
	   ...............
	  group    2 
	   ..........
	  group    3 
	   .....
> (LG1 <- make.seq(groups,1))

Number of markers: 15
Markers in the sequence:
M1 M2 M3 M5 M6 M10 M11 M12 M14 M15 M17 M25 M26 M28 M30

Parameters not estimated.

> (LG.aug<-drop.marker(LG1, c(10,14)))

Number of markers: 13
Markers in the sequence:
M1 M2 M3 M5 M6 M11 M12 M15 M17 M25 M26 M28 M30

Parameters not estimated.

> 
> 
> 
> 
> cleanEx()
> nameEx("example.out")
> ### * example.out
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: example.out
> ### Title: Data from a full-sib family derived from two outbred parents
> ### Aliases: example.out
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(example.out)
> 
> # perform two-point analyses
> twopts <- rf.2pts(example.out)
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
> twopts
  This is an object of class 'rf.2pts'

  Criteria: LOD = 3 , Maximum recombination fraction = 0.5 

  This object is too complex to print
  Type 'print(object,mrk1=marker,mrk2=marker)' to see the analysis for two markers
    mrk1 and mrk2 can be the names or numbers of both markers
> 
> 
> 
> cleanEx()
> nameEx("find.bins")
> ### * find.bins
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: find.bins
> ### Title: Allocate markers into bins
> ### Aliases: find.bins
> ### Keywords: bins dimension reduction
> 
> ### ** Examples
> 
>  ## Not run: 
> ##D   load(url("https://github.com/mmollina/data/raw/master/fake_big_data_f2.RData"))
> ##D   fake.big.data.f2
> ##D   (bins<-find.bins(fake.big.data.f2, exact=FALSE))
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("group")
> ### * group
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: group
> ### Title: Assign markers to linkage groups
> ### Aliases: group
> ### Keywords: misc
> 
> ### ** Examples
> 
> 
>   data(example.out)
>   twopts <- rf.2pts(example.out)
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
> 
>   all.data <- make.seq(twopts,"all")
>   link_gr <- group(all.data)
   Selecting markers: 
	  group    1 
	   ...............
	  group    2 
	   ..........
	  group    3 
	   .....
>   link_gr
  This is an object of class 'group'
  It was generated from the object "all.data"

  Criteria used to assign markers to groups:
    LOD = 3 , Maximum recombination fraction = 0.5 

  No. markers:            30 
  No. groups:             3 
  No. linked markers:     30 
  No. unlinked markers:   0 

  Printing groups:
  Group 1 : 15 markers
    M1 M2 M3 M5 M6 M10 M11 M12 M14 M15 M17 M25 M26 M28 M30 

  Group 2 : 10 markers
    M4 M9 M16 M19 M20 M21 M23 M24 M27 M29 

  Group 3 : 5 markers
    M7 M8 M13 M18 M22 
>   print(link_gr, details=FALSE) #omit the names of the markers
  This is an object of class 'group'
  It was generated from the object "all.data"

  Criteria used to assign markers to groups:
    LOD = 3 , Maximum recombination fraction = 0.5 

  No. markers:            30 
  No. groups:             3 
  No. linked markers:     30 
  No. unlinked markers:   0 

  Printing groups:
  Group 1 : 15 markers
    M1 M2 M3 M5 M6 M10 M11 M12 M14 M15 M17 M25 M26 M28 M30 

  Group 2 : 10 markers
    M4 M9 M16 M19 M20 M21 M23 M24 M27 M29 

  Group 3 : 5 markers
    M7 M8 M13 M18 M22 
> 
> 
> 
> 
> cleanEx()
> nameEx("make.seq")
> ### * make.seq
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: make.seq
> ### Title: Create a sequence of markers
> ### Aliases: make.seq
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D 
> ##D   all.mark <- make.seq(twopt,"all")
> ##D   all.mark <- make.seq(twopt,1:30) # same as above, for this data set
> ##D   groups <- group(all.mark)
> ##D   LG1 <- make.seq(groups,1)
> ##D   LG1.ord <- order.seq(LG1)
> ##D   (LG1.final <- make.seq(LG1.ord)) # safe order
> ##D   (LG1.final.all <- make.seq(LG1.ord,"force")) # forced order
> ##D 
> ##D   markers <- make.seq(twopt,c(2,3,12,14))
> ##D   markers.comp <- compare(markers)
> ##D   (base.map <- make.seq(markers.comp))
> ##D   base.map <- make.seq(markers.comp,1,1) # same as above
> ##D   (extend.map <- try.seq(base.map,30))
> ##D   (base.map <- make.seq(extend.map,5)) # fifth position is the best
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("map")
> ### * map
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: map
> ### Title: Construct the linkage map for a sequence of markers
> ### Aliases: map
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
>   data(example.out)
>   twopt <- rf.2pts(example.out)
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
> 
>   markers <- make.seq(twopt,c(30,12,3,14,2)) # correct phases
>   map(markers)

Printing map:

Markers           Position           Parent 1       Parent 2

30 M30                0.00           a |  | b       a |  | b 
12 M12                1.00           b |  | a       c |  | a 
 3 M3                20.57           o |  | a       o |  | o 
14 M14               33.63           a |  | o       b |  | o 
 2 M2                41.70           o |  | o       o |  | a 

5 markers            log-likelihood: -320.9012 

> 
>   markers <- make.seq(twopt,c(30,12,3,14,2),phase=c(4,1,4,3)) # incorrect phases
>   map(markers)

Printing map:

Markers           Position           Parent 1       Parent 2

30 M30                0.00           a |  | b       a |  | b 
12 M12                1.00           b |  | a       c |  | a 
 3 M3                20.57           o |  | a       o |  | o 
14 M14               33.63           a |  | o       b |  | o 
 2 M2               379.02           o |  | o       a |  | o 

5 markers            log-likelihood: -362.3389 

> 
> 
> 
> 
> cleanEx()
> nameEx("map_func")
> ### * map_func
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: map_func
> ### Title: Mapping functions Haldane and Kosambi
> ### Aliases: map_func haldane kosambi
> ### Keywords: arith
> 
> ### ** Examples
> 
>   # little difference for small recombination fractions
>   haldane(0.05)
[1] 5.268026
>   kosambi(0.05)
[1] 5.016767
> 
>   # greater difference as recombination fraction increases
>   haldane(0.35)
[1] 60.19864
>   kosambi(0.35)
[1] 43.36503
> 
> 
> 
> cleanEx()
> nameEx("marker.type")
> ### * marker.type
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: marker.type
> ### Title: Informs the segregation patterns of markers
> ### Aliases: marker.type
> ### Keywords: manip utilities
> 
> ### ** Examples
> 
> 
>   data(example.out)
>   twopts <- rf.2pts(example.out)
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
>   markers.ex <- make.seq(twopts,c(3,6,8,12,16,25))
>   marker.type(markers.ex) # segregation type for some markers
  Marker3(M3) has typeD1.13

  Marker6(M6) has typeB3.7

  Marker8(M8) has typeB3.7

  Marker12(M12) has typeA.2

  Marker16(M16) has typeD2.17

  Marker25(M25) has typeB2.6

> 
> 
> 
> 
> 
> cleanEx()
> nameEx("order.seq")
> ### * order.seq
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: order.seq
> ### Title: Search for the best order of markers combining compare and
> ###   try.seq functions
> ### Aliases: order.seq
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   #outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   all.mark <- make.seq(twopt,"all")
> ##D   groups <- group(all.mark)
> ##D   LG2 <- make.seq(groups,2)
> ##D   LG2.ord <- order.seq(LG2,touchdown=TRUE)
> ##D   LG2.ord
> ##D   make.seq(LG2.ord) # get safe sequence
> ##D   make.seq(LG2.ord,"force") # get forced sequence
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("pick.batch.sizes")
> ### * pick.batch.sizes
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pick.batch.sizes
> ### Title: Picking optimal batch size values
> ### Aliases: pick.batch.sizes
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   LG <- structure(list(seq.num = seq(1,800)), class = "sequence")
> ##D   batchsize <- pick.batch.sizes(LG, 50, 19)
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("plot.by.segreg.type")
> ### * plot.by.segreg.type
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.by.segreg.type
> ### Title: Draw a graphic showing the number of markers of each segregation
> ###   pattern.
> ### Aliases: plot.by.segreg.type
> 
> ### ** Examples
> 
> data(example.out) #Outcrossing data
> plot.by.segreg.type(example.out)
> plot.by.segreg.type(example.out, subcateg=FALSE)
> 
> # You can store the graphic in an object, then save it.
> # For details, see the help of ggplot2's function ggsave()
> data(example.out) #Outcrossing data
> g <- plot.by.segreg.type(example.out)
> ggsave("SegregationTypes.jpg", g, width=7, height=4, dpi=600)
> 
> 
> 
> 
> cleanEx()
> nameEx("plot.onemap")
> ### * plot.onemap
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.onemap
> ### Title: Draw a graphic of raw data for any OneMap population
> ### Aliases: plot.onemap
> 
> ### ** Examples
> 
> data(example.out) # Loads a fake full-sib dataset installed with onemap
> plot(example.out) # This will show you the graph for all markers
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
> plot(example.out, all=FALSE) # This will show you the graph splitted for marker types
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
> 
> # You can store the graphic in an object, then save it.
> # For details, see the help of ggplot2's function ggsave()
> g <- plot(example.out, all=FALSE)
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
No id variables; using all as measure variables
> ggsave("MyRawData_out.jpg", g, width=9, height=4, dpi=600)
> 
> 
> 
> 
> cleanEx()
> nameEx("plot.onemap.segreg.test")
> ### * plot.onemap.segreg.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.onemap.segreg.test
> ### Title: Plot p-values for chi-square tests of expected segregation
> ### Aliases: plot.onemap.segreg.test
> 
> ### ** Examples
> 
> data(example.out) # load OneMap's fake dataset for an outcrossing population
> Out.seg <- test.segregation(example.out) # Applies chi-square tests
> print(Out.seg) # Shows the results
   Marker      H0 Chi-square      p-value % genot.
1      M1   1:2:1       1.76 4.147829e-01      100
2      M2     1:1       0.04 8.414806e-01      100
3      M3     1:1       0.36 5.485062e-01      100
4      M4 1:1:1:1       2.64 4.505201e-01      100
5      M5     1:1       1.96 1.615133e-01      100
6      M6   1:2:1       1.52 4.676664e-01      100
7      M7     1:1       0.16 6.891565e-01      100
8      M8   1:2:1       0.86 6.505091e-01      100
9      M9     1:1       0.04 8.414806e-01      100
10    M10     1:1       0.36 5.485062e-01      100
11    M11     1:1       0.16 6.891565e-01      100
12    M12 1:1:1:1       6.48 9.045460e-02      100
13    M13     3:1       0.00 1.000000e+00      100
14    M14 1:1:1:1       0.40 9.402425e-01      100
15    M15 1:1:1:1       2.24 5.241127e-01      100
16    M16     1:1       1.44 2.301393e-01      100
17    M17   1:2:1      35.58 1.878889e-08      100
18    M18 1:1:1:1       1.44 6.961859e-01      100
19    M19   1:2:1      37.98 5.659105e-09      100
20    M20 1:1:1:1       4.88 1.807980e-01      100
21    M21     1:1       1.44 2.301393e-01      100
22    M22     1:1       1.00 3.173105e-01      100
23    M23     3:1       0.48 4.884223e-01      100
24    M24   1:2:1       1.50 4.723666e-01      100
25    M25   1:2:1      35.04 2.461278e-08      100
26    M26 1:1:1:1       1.52 6.776621e-01      100
27    M27     1:1       1.00 3.173105e-01      100
28    M28 1:1:1:1       1.20 7.530043e-01      100
29    M29     1:1       0.00 1.000000e+00      100
30    M30   1:2:1       3.42 1.808658e-01      100
> plot(Out.seg) # Plot the graph, ordering the p-values
> plot(Out.seg, order=FALSE) # Plot the graph showing the results keeping the order in the dataset
> # You can store the graphic in an object, then save it.
> # For details, see the help of ggplot2's function ggsave()
> g <- plot(Out.seg)
> ggsave("SegregationTests.jpg", g, width=7, height=5, dpi=600)
> 
> 
> 
> 
> cleanEx()
> nameEx("rcd")
> ### * rcd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rcd
> ### Title: Rapid Chain Delineation
> ### Aliases: rcd
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   #outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   all.mark <- make.seq(twopt,"all")
> ##D   groups <- group(all.mark)
> ##D   LG1 <- make.seq(groups,1)
> ##D   LG1.rcd <- rcd(LG1)
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("read.mapmaker")
> ### * read.mapmaker
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.mapmaker
> ### Title: Read data from a Mapmaker raw file
> ### Aliases: read.mapmaker
> ### Keywords: IO
> 
> ### ** Examples
> 
> 
>   ## Not run: 
> ##D     map_data <-read.mapmaker(dir="work_directory",file="data_file.txt")
> ##D   
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("read.onemap")
> ### * read.onemap
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.onemap
> ### Title: Read data from all types of progenies supported by OneMap
> ### Aliases: read.onemap
> ### Keywords: IO
> 
> ### ** Examples
> 
> 
>   ## Not run: 
> ##D     outcr_data <- read.onemap(dir="work_directory", file="data_file.txt", cross="outcross")
> ##D   
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("read.outcross")
> ### * read.outcross
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.outcross
> ### Title: Read data from a full-sib progeny (outcrossing populations)
> ### Aliases: read.outcross
> ### Keywords: IO
> 
> ### ** Examples
> 
> 
>   ## Not run: 
> ##D     outcr_data <-
> ##D read.outcross(dir="work_directory",file="data_file.txt")
> ##D   
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("read.outcross2")
> ### * read.outcross2
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.outcross2
> ### Title: Read data from a full-sib progeny (outcrossing populations)
> ### Aliases: read.outcross2
> ### Keywords: IO
> 
> ### ** Examples
> 
> ## Not run: 
> ##D     outcr_data <-
> ##D read.outcross2("data_file.txt")
> ##D   
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("record")
> ### * record
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: record
> ### Title: Recombination Counting and Ordering
> ### Aliases: record
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   ##outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   all.mark <- make.seq(twopt,"all")
> ##D   groups <- group(all.mark)
> ##D   LG1 <- make.seq(groups,1)
> ##D   LG1.rec <- record(LG1)
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("record.parallel")
> ### * record.parallel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: record.parallel
> ### Title: Recombination Counting and Ordering
> ### Aliases: record.parallel
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   ##outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   all.mark <- make.seq(twopt,"all")
> ##D   groups <- group(all.mark)
> ##D   LG1 <- make.seq(groups,1)
> ##D   LG1.rec <- record(LG1)
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("rf.2pts")
> ### * rf.2pts
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rf.2pts
> ### Title: Two-point analysis between genetic markers
> ### Aliases: rf.2pts
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
>   data(example.out)
> 
>   twopts <- rf.2pts(example.out,LOD=3,max.rf=0.5) # perform two-point analyses
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
>   twopts
  This is an object of class 'rf.2pts'

  Criteria: LOD = 3 , Maximum recombination fraction = 0.5 

  This object is too complex to print
  Type 'print(object,mrk1=marker,mrk2=marker)' to see the analysis for two markers
    mrk1 and mrk2 can be the names or numbers of both markers
> 
>   print(twopts,c("M1","M2")) # detailed results for markers 1 and 2
  Results of the 2-point analysis for markers:M1andM2

  Criteria: LOD = 3, Maximum recombination fraction = 0.5


          rf      LOD
CC 0.1818151 4.185012
CR 0.8181849 4.185012
RC 0.1818151 4.185012
RR 0.8181849 4.185012
> 
> 
> 
> 
> cleanEx()
> nameEx("ripple.seq")
> ### * ripple.seq
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ripple.seq
> ### Title: Compares and displays plausible alternative orders for a given
> ###   linkage group
> ### Aliases: ripple.seq
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D  #Outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   markers <- make.seq(twopt,c(27,16,20,4,19,21,23,9,24,29))
> ##D   markers.map <- map(markers)
> ##D   ripple.seq(markers.map)
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("seeded.map")
> ### * seeded.map
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: seeded.map
> ### Title: Construct the linkage map for a sequence of markers after
> ###   seeding phases
> ### Aliases: seeded.map
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
>   data(example.out)
>   twopt <- rf.2pts(example.out)
Computing 435 recombination fractions:

0%                                    100%
[----------------------------------------]
[########################################]
> 
>   markers <- make.seq(twopt,c(30,12,3,14,2))
>   seeded.map(markers, seeds = c(4,2))

Printing map:

Markers           Position           Parent 1       Parent 2

30 M30                0.00           a |  | b       a |  | b 
12 M12                1.00           b |  | a       c |  | a 
 3 M3                20.57           o |  | a       o |  | o 
14 M14               33.63           a |  | o       b |  | o 
 2 M2                41.70           o |  | o       o |  | a 

5 markers            log-likelihood: -320.9012 

> 
> 
> 
> 
> 
> cleanEx()
> nameEx("seriation")
> ### * seriation
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: seriation
> ### Title: Seriation
> ### Aliases: seriation
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   ##outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   all.mark <- make.seq(twopt,"all")
> ##D   groups <- group(all.mark)
> ##D   LG3 <- make.seq(groups,3)
> ##D   LG3.ser <- seriation(LG3)
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("test.segregation")
> ### * test.segregation
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: test.segregation
> ### Title: test.segregation
> ### Aliases: test.segregation
> 
> ### ** Examples
> 
> data(example.out) # Loads a fake outcross dataset installed with onemap
> Chi <- test.segregation(example.out) # Performs the chi-square test for all markers
> print(Chi) # Shows the results
   Marker      H0 Chi-square      p-value % genot.
1      M1   1:2:1       1.76 4.147829e-01      100
2      M2     1:1       0.04 8.414806e-01      100
3      M3     1:1       0.36 5.485062e-01      100
4      M4 1:1:1:1       2.64 4.505201e-01      100
5      M5     1:1       1.96 1.615133e-01      100
6      M6   1:2:1       1.52 4.676664e-01      100
7      M7     1:1       0.16 6.891565e-01      100
8      M8   1:2:1       0.86 6.505091e-01      100
9      M9     1:1       0.04 8.414806e-01      100
10    M10     1:1       0.36 5.485062e-01      100
11    M11     1:1       0.16 6.891565e-01      100
12    M12 1:1:1:1       6.48 9.045460e-02      100
13    M13     3:1       0.00 1.000000e+00      100
14    M14 1:1:1:1       0.40 9.402425e-01      100
15    M15 1:1:1:1       2.24 5.241127e-01      100
16    M16     1:1       1.44 2.301393e-01      100
17    M17   1:2:1      35.58 1.878889e-08      100
18    M18 1:1:1:1       1.44 6.961859e-01      100
19    M19   1:2:1      37.98 5.659105e-09      100
20    M20 1:1:1:1       4.88 1.807980e-01      100
21    M21     1:1       1.44 2.301393e-01      100
22    M22     1:1       1.00 3.173105e-01      100
23    M23     3:1       0.48 4.884223e-01      100
24    M24   1:2:1       1.50 4.723666e-01      100
25    M25   1:2:1      35.04 2.461278e-08      100
26    M26 1:1:1:1       1.52 6.776621e-01      100
27    M27     1:1       1.00 3.173105e-01      100
28    M28 1:1:1:1       1.20 7.530043e-01      100
29    M29     1:1       0.00 1.000000e+00      100
30    M30   1:2:1       3.42 1.808658e-01      100
> 
> 
> 
> 
> cleanEx()
> nameEx("try.seq")
> ### * try.seq
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: try.seq
> ### Title: Try to map a marker into every possible position between markers
> ###   in a given map
> ### Aliases: try.seq
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   #outcrossing example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   markers <- make.seq(twopt,c(2,3,12,14))
> ##D   markers.comp <- compare(markers)
> ##D   base.map <- make.seq(markers.comp,1)
> ##D 
> ##D   extend.map <- try.seq(base.map,30)
> ##D   extend.map
> ##D   print(extend.map,5) # best position
> ##D   print(extend.map,4) # second best position
> ##D 
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("ug")
> ### * ug
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ug
> ### Title: Unidirectional Growth
> ### Aliases: ug
> ### Keywords: utilities
> 
> ### ** Examples
> 
> 
> ## Not run: 
> ##D   #outcross example
> ##D   data(example.out)
> ##D   twopt <- rf.2pts(example.out)
> ##D   all.mark <- make.seq(twopt,"all")
> ##D   groups <- group(all.mark)
> ##D   LG1 <- make.seq(groups,1)
> ##D   LG1.ug <- ug(LG1)
> ## End(Not run)
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  26.975 0.679 29.013 0.005 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')