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Type 'q()' to quit R. > pkgname <- "layeranalyzer" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('layeranalyzer') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("anovalayered") > ### * anovalayered > > flush(stderr()); flush(stdout()) > > ### Name: anova.layered > ### Title: Analysis of Deviance for return object from 'layer.analyzer'. > ### Aliases: anova.layered > > ### ** Examples > > library(layeranalyzer) > > # Read a simulated dataset with 2 layers: > test.ex<-read.table("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt") > names(test.ex)<-c("time","value") > ts<-layer.data.series(time=test.ex$time, value=test.ex$value,name="X") > > # Calculate classic maximum likelihoods for an OU model and a 2 layered model. > # Test the number of layers. The dataset itself is simulated using 2 layers: > > > > cleanEx() > nameEx("comparelayered") > ### * comparelayered > > flush(stderr()); flush(stdout()) > > ### Name: compare.layered > ### Title: Perform comparison of several models for the same dataset(s). > ### Aliases: compare.layered > > ### ** Examples > > library(layeranalyzer) > > # Read a simulated dataset with 2 layers: > test.ex<-read.table("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt") > names(test.ex)<-c("time","value") > ts<-layer.data.series(time=test.ex$time, value=test.ex$value,name="X") > > # ML estimation for an OU model, low computer resources (including low pre-optimization MCMC) > # to speed things up > > > > > cleanEx() > nameEx("layeranalyzer-package") > ### * layeranalyzer-package > > flush(stderr()); flush(stdout()) > > ### Name: layeranalyzer > ### Title: Time Series Analysis Tool using Linear Layered SDEs > ### Aliases: layeranalyzer > ### Keywords: package > > ### ** Examples > > library(layeranalyzer) > > # Create a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, > # characteristic time=50 and sigma=0.2: > n=1000 > x=rep(rnorm(1),n) > for(i in 2:n) + x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1) > > # Create measurement set as a subsample of the process, plus > # noise with standard deviation 0.1 > t=sort(sample(1:n,300)) > y=(x+rnorm(length(x),0,0.1))[t] > > # Create the data series object from the time points and "measurements": > X=layer.data.series(time.points=t, value.points=y,name="X") > > # Specify layered structure. (In this case default structure, one layered=OU): > X.struct=layer.series.structure(X) > > # Perform Bayesian analysis (low number of MCMCs): > res<-layer.analyzer(X.struct, num.MCMC=40, burnin=400) > > # Show the results: > summary(res) Description: OU Coefficients: Mean Median Lower 95% Upper 95% mu_X -0.179781 -0.197132 -0.612322 0.307791 dt_X_1 35.690709 35.186388 21.332573 54.399653 stat_sdev_X_1 0.878059 0.863245 0.745646 1.079676 obs_sd_X 0.074014 0.076355 0.016495 0.119791 Model log-likelihood: -112.063 > > > > > cleanEx() > nameEx("layeranalyzer") > ### * layeranalyzer > > flush(stderr()); flush(stdout()) > > ### Name: layer.analyzer > ### Title: Time series analysis tool using linear layered SDEs. > ### Aliases: layer.analyzer > > ### ** Examples > > library(layeranalyzer) > # Simulated a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, > # characteristic time=50 and sigma=0.2: > n=1000 > x=rep(rnorm(1),n) > for(i in 2:n) + x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1) > > # Create measurement set as a subsample of the process, plus > # noise with standard deviation 0.1 > t=sort(sample(1:n,300)) > y=(x+rnorm(length(x),0,0.1))[t] > > # Create the data series object from the time points and "measurements": > X=layer.data.series(time.points=t, value.points=y,name="X") > > # Run with few resources (since this is a test example). > # PS: ML-based estimates (default is Bayesian) > res=layer.analyzer(layer.series.structure(X,numlayers=1), + num.MCMC=40,burnin=400, + do.maximum.likelihood=TRUE,maximum.likelihood.numstart=20) > > # Look at the results: > summary(res) Description: OU Coefficients: ML estimate Bayesian Lower 95% Bayesian Upper 95% mu_X -0.150304 -0.612322 0.307791 dt_X_1 31.981498 21.332573 54.399653 stat_sdev_X_1 0.806772 0.745646 1.079676 obs_sd_X 0.102706 0.016495 0.119791 Log-likelihood: -99.845 AIC : 207.691 AICc: 207.759 BIC : 222.506 > # PS: Uncertainty estimates comes from Bayesian analysis and will > # in this case not be very stable (since few resources). > > > > > cleanEx() > nameEx("layeranalyzertimeserieslist") > ### * layeranalyzertimeserieslist > > flush(stderr()); flush(stdout()) > > ### Name: layer.analyzer.timeseries.list > ### Title: Time series analysis tool using linear layered SDEs. > ### Aliases: layer.analyzer.timeseries.list > > ### ** Examples > > library(layeranalyzer) > # Simulated a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, > # characteristic time=50 and sigma=0.2: > n=1000 > x=rep(rnorm(1),n) > for(i in 2:n) + x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1) > > # Create measurement set as a subsample of the process, plus > # noise with standard deviation 0.1 > t=sort(sample(1:n,300)) > y=(x+rnorm(length(x),0,0.1))[t] > > # Create the data series object from the time points and "measurements": > X=layer.data.series(time.points=t, value.points=y,name="X") > > # Run with few resources (since this is a test example). > # PS: ML-based estimates (default is Bayesian) > res=layer.analyzer.timeseries.list(list(x1=layer.series.structure(X,numlayers=1)), + num.MCMC=40,burnin=400,layer.analyzer.mode="ML-from-MCMC", + do.maximum.likelihood=TRUE,maximum.likelihood.numstart=20) > > # Look at the results: > summary(res) Description: OU Coefficients: ML estimate Bayesian Lower 95% Bayesian Upper 95% mu_X -0.150304 -0.612322 0.307791 dt_X_1 31.981498 21.332573 54.399653 stat_sdev_X_1 0.806772 0.745646 1.079676 obs_sd_X 0.102706 0.016495 0.119791 Log-likelihood: -99.845 AIC : 207.691 AICc: 207.759 BIC : 222.506 > # PS: Uncertainty estimates comes from Bayesian analysis and will > # in this case not be very stable (since few resources). > > > > > cleanEx() > nameEx("layerconnections") > ### * layerconnections > > flush(stderr()); flush(stdout()) > > ### Name: layer.connections > ### Title: Alternative connection specification, taking a structure list. > ### Aliases: layer.connections > > ### ** Examples > > library(layeranalyzer) > > # Creates two 2-layered structures based on the two hare/lynx datasets. > # outputs the connection structure specified by the > # alternative conenction specification array c(0,1,2,4) > > hare.st=layer.series.structure(hare.norm,numlayers=2) > lynx.st=layer.series.structure(lynx.norm,numlayers=2) > > layer.connections(hare.st, lynx.st, conn=c(0,1,2,4)) $causal [,1] [,2] [,3] [1,] 1 1 2 [2,] 2 2 2 [3,] 2 2 1 [4,] 1 2 2 $corr [,1] [1,] 1 [2,] 1 [3,] 2 [4,] 2 attr(,"class") [1] "layer.connections" > > > > cleanEx() > nameEx("layerconnectionslist") > ### * layerconnectionslist > > flush(stderr()); flush(stdout()) > > ### Name: layer.connections.list > ### Title: Alternative connection specification, taking a structure list. > ### Aliases: layer.connections.list > > ### ** Examples > > library(layeranalyzer) > > # Creates two 2-layered structures based on the two hare/lynx datasets. > # outputs the connection structure specified by the > # alternative conenction specification array c(0,1,2,4) > > hare.st=layer.series.structure(hare.norm,numlayers=2) > lynx.st=layer.series.structure(lynx.norm,numlayers=2) > > layer.connections.list(list(hare.st, lynx.st), conn=c(0,1,2,4)) $causal [,1] [,2] [,3] [1,] 1 1 2 [2,] 2 2 2 [3,] 2 2 1 [4,] 1 2 2 $corr [,1] [1,] 1 [2,] 1 [3,] 2 [4,] 2 attr(,"class") [1] "layer.connections" > > > > cleanEx() > nameEx("layerdataseries") > ### * layerdataseries > > flush(stderr()); flush(stdout()) > > ### Name: layer.data.series > ### Title: Represents a measurement time series. > ### Aliases: layer.data.series > > ### ** Examples > > library(layeranalyzer) > > # Create a sample from the Ornstein-Uhlenbeck (OU) process with mu=0, > # characteristic time=50 and sigma=0.2: > n=1000 > x=rep(rnorm(1),n) > for(i in 2:n) + x[i]=exp(-1/50)*x[i-1]+0.2*rnorm(1) > > # Create measurement set as a subsample of the process, plus > # noise with standard deviation 0.1 > t=sort(sample(1:n,300)) > y=(x+rnorm(length(x),0,0.1))[t] > > # Create the data series object from the time points and "measurements": > X=layer.data.series(time.points=t, value.points=y, name="X") > > > > > cleanEx() > nameEx("layerloadprior") > ### * layerloadprior > > flush(stderr()); flush(stdout()) > > ### Name: layer.load.prior > ### Title: Reading prior specification for the layer.analyzer from a file. > ### Aliases: layer.load.prior > > ### ** Examples > > library(layeranalyzer) > pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt") > > > > cleanEx() > nameEx("layerpairs") > ### * layerpairs > > flush(stderr()); flush(stdout()) > > ### Name: layer.pairs > ### Title: Alternative connection specification, taking a structure list. > ### Aliases: layer.pairs > > ### ** Examples > > library(layeranalyzer) > > # Creates two 2-layered structures based on the two hare/lynx datasets. > # outputs the pairs and a description > > hare.st=layer.series.structure(hare.norm,numlayers=2) > lynx.st=layer.series.structure(lynx.norm,numlayers=2) > > layer.pairs(hare.st, lynx.st) $pairs comp1.series comp1.layer comp2.series comp2.layer pair1 1 1 2 1 pair2 1 1 2 2 pair3 1 2 2 1 pair4 1 2 2 2 $description [1] "Pair 1: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 1" [2] "Pair 2: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 2" [3] "Pair 3: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 1" [4] "Pair 4: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 2" attr(,"class") [1] "layer.pairs" > > > > cleanEx() > nameEx("layerpairslist") > ### * layerpairslist > > flush(stderr()); flush(stdout()) > > ### Name: layer.pairs.list > ### Title: Alternative connection specification, taking a structure list. > ### Aliases: layer.pairs.list > > ### ** Examples > > library(layeranalyzer) > > # Creates two 2-layered structures based on the two hare/lynx datasets. > # outputs the pairs and a description > > hare.st=layer.series.structure(hare.norm,numlayers=2) > lynx.st=layer.series.structure(lynx.norm,numlayers=2) > > layer.pairs.list(list(hare.st, lynx.st)) $pairs comp1.series comp1.layer comp2.series comp2.layer pair1 1 1 2 1 pair2 1 1 2 2 pair3 1 2 2 1 pair4 1 2 2 2 $description [1] "Pair 1: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 1" [2] "Pair 2: first process is series 1 (hare), layer 1, second process is series 1 (lynx), layer 2" [3] "Pair 3: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 1" [4] "Pair 4: first process is series 1 (hare), layer 2, second process is series 1 (lynx), layer 2" attr(,"class") [1] "layer.pairs" > > > > cleanEx() > nameEx("layerparamloglik") > ### * layerparamloglik > > flush(stderr()); flush(stdout()) > > ### Name: layer.param.loglik > ### Title: Returning log-likelihood for a set of parameter values. > ### Aliases: layer.param.loglik > > ### ** Examples > > # Define time series (time points, values and in this case also > # sample standard deviations and sample size, used for calculating > # standard errors for each measurement). > # install.packages("../www_docs/R/layeranalyzer_0.2.1.tar.gz",type="source") > library(layeranalyzer) > X=layer.data.series(time.points=malta$Time.Year, + value.points=malta$Mean..log.body.mass., + std.dev=sqrt(malta$Variance.calculated.from.the.data), + num.meas.per.value=malta$Sample.size,name="log.body.size") > > # Define process structure for analysis purposes: > ser=layer.series.structure(X,numlayers=1) > > # Perform Bayesian analysis: > res=layer.analyzer(ser,num.MCMC=100,burnin=1000) layeranalyzer.cpp:19254:12: runtime error: load of value 32520, which is not a valid value for type 'param_type' #0 0x7f082ad2c40d in layeranalyzer /data/localhost/ripley/R/packages/tests-clang-UBSAN/layeranalyzer/src/layeranalyzer.cpp:19254:12 #1 0x5575fdd0970b in R_doDotCall (/data/localhost/ripley/R/R-clang/bin/exec/R+0x9170b) #2 0x5575fdd09ffd in do_dotcall (/data/localhost/ripley/R/R-clang/bin/exec/R+0x91ffd) #3 0x5575fdd48b23 in bcEval_loop eval.c #4 0x5575fdd421cb in bcEval eval.c #5 0x5575fdd41984 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9984) #6 0x5575fdd59a58 in R_execClosure eval.c #7 0x5575fdd58f4a in applyClosure_core eval.c #8 0x5575fdd42896 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xca896) #9 0x5575fdd41dd7 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9dd7) #10 0x5575fdd5eda7 in do_set (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe6da7) #11 0x5575fdd41baf in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9baf) #12 0x5575fdd8ef27 in Rf_ReplIteration (/data/localhost/ripley/R/R-clang/bin/exec/R+0x116f27) #13 0x5575fdd90a2e in run_Rmainloop (/data/localhost/ripley/R/R-clang/bin/exec/R+0x118a2e) #14 0x5575fdd90a9a in Rf_mainloop (/data/localhost/ripley/R/R-clang/bin/exec/R+0x118a9a) #15 0x5575fdc79db7 in main (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1db7) #16 0x7f083280a680 in __libc_start_call_main (/lib64/libc.so.6+0x3680) (BuildId: 5bd941be836f538fe5e10eff508f7f5dd94905a6) #17 0x7f083280a797 in __libc_start_main@GLIBC_2.2.5 (/lib64/libc.so.6+0x3797) (BuildId: 5bd941be836f538fe5e10eff508f7f5dd94905a6) #18 0x5575fdc79cd4 in _start (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1cd4) SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior layeranalyzer.cpp:19254:12 > > # Run with debug info: > layer.param.loglik(res, silent.mode=FALSE) 0.001 10 prior_obs1=0.001 prior_obs2=10 num_layers=1 0.001 10 mu in (-10.000, 10.000), m = 0.000 s = 5.102), dt in ( 0.001,1000.000), lm= 0.000 ls= 3.524), sigma in ( 0.001,100.000), lm= -1.151 ls= 2.937), stat_sdev in ( 0.001, 10.000), lm= -2.303 ls= 2.350), lin in (-10.000, 10.000), m = 0.000 s = 5.102), beta in (-10.000, 10.000), m = 0.000 s = 5.102), init in (-100.000,100.000), m = 0.000 s = 51.020), obs in ( 0.001, 10.000), lm= -2.303 ls= 2.350). d.falloff in ( 0.000,1000000.000), lm= 0.000 ls= 7.049). is_log=0. 0 extseries.size=0 numpar=3 1 mu_log.body.size 2 dt_log.body.size_1 3 stat_sdev_log.body.size_1 ********************************* Starting loglik treatment ********************************* currparams=(2.41324 2032.41 0.600547 ) loglik started start reading parameters start useext end useext, start init treatment start correlations start feedback timer stuff starting numpar=3 1 mu_log.body.size 2 dt_log.body.size_1 3 stat_sdev_log.body.size_1 useext checking starting correlation2 starting filling in matrices eigenvector stuff m vector stuff i=0 m_prev[i]=2.41324 pull=0.000492026 m_after[i]=0.00118738 m=(0.00118738 ) dosmooth=0 me=0x557621c2e2b8 meas_smooth=0 meas_tot=0x557621c2e2b8 me[0].tm=1996 omega/Lambda matrix stuff Making P, Q, x matrices Making residual and prior expectancy matrices Making smoothing matrices Correlation3 starting loglik init2 loglik init3 Sigma matrix starting Finding V*Sigma^2 Finding V*Sigma^2*V' Initialize x,u,P,F num_states=1 num_series=1 mean time 0:2005.18 is_complex=0 num_states=1 W initialized W,u initialized meantime filled meantime=2005.18 Fill W, initialize x, u t00=1996 first_init_time=-1e+07 meantime=2005.18 W_r=(2.41324 ) Fill u useext2 starting sigma^2=(0.000354905 ) Omega_r=(0.000354905 ) A=(-0.000492026 ) lambda_r=(-0.000492026 ) V_r=(1 ) Vinv_r=(1 ) k=0 u_k=(2.41324 ) x_k_prev=(2.41324 ) Q_k=( 0.3606565365 ) F_k=( 0.0000000000 ) x_k_now[prev]=(0 ) P_k_now[prev]=( 0.0000000000 ) x_k_prev=(2.41324 ) P_k_prev=( 0.3606565365 ) s= site=0 numsites=1 index=0 k=0 y_k=0.164032 S_k=0.363503 y_k/sqrt(S_k)=0.272066 k=0 meas=2.57728 x_k_prev[0]=2.41324 y_k=0.164032 S_k= 0.363503 ll= -0.449965 P_k_prev=0.360657 u_k=2.41324 k=1 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.57591 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.57599 ) P_k_now[prev]=( 0.0028243171 ) x_k_prev=(2.57591 ) P_k_prev=( 0.0031762697 ) s= site=0 numsites=1 index=0 k=1 y_k=-0.0510384 S_k=0.00435055 y_k/sqrt(S_k)=-0.773794 k=1 meas=2.52487 x_k_prev[0]=2.57591 y_k=-0.0510384 S_k= 0.00435055 ll= 1.05045 P_k_prev=0.00317627 u_k=0.00118709 k=2 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.53859 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.53865 ) P_k_now[prev]=( 0.0008573219 ) x_k_prev=(2.53859 ) P_k_prev=( 0.0012112092 ) s= site=0 numsites=1 index=0 k=2 y_k=-0.0472003 S_k=0.00283255 y_k/sqrt(S_k)=-0.886862 k=2 meas=2.49139 x_k_prev[0]=2.53859 y_k=-0.0472003 S_k= 0.00283255 ll= 2.67153 P_k_prev=0.00121121 u_k=0.00118709 k=3 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.51835 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.5184 ) P_k_now[prev]=( 0.0006932922 ) x_k_prev=(2.51835 ) P_k_prev=( 0.0010473408 ) s= site=0 numsites=1 index=0 k=3 y_k=-0.122384 S_k=0.00206263 y_k/sqrt(S_k)=-2.69471 k=3 meas=2.39597 x_k_prev[0]=2.51835 y_k=-0.122384 S_k= 0.00206263 ll= 1.21374 P_k_prev=0.00104734 u_k=0.00118709 k=4 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.45619 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.45621 ) P_k_now[prev]=( 0.0005155332 ) x_k_prev=(2.45619 ) P_k_prev=( 0.0008697567 ) s= site=0 numsites=1 index=0 k=4 y_k=-0.106726 S_k=0.00128868 y_k/sqrt(S_k)=-2.97302 k=4 meas=2.34946 x_k_prev[0]=2.45619 y_k=-0.106726 S_k= 0.00128868 ll= -0.797536 P_k_prev=0.000869757 u_k=0.00118709 k=5 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.38417 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.38416 ) P_k_now[prev]=( 0.0002827394 ) x_k_prev=(2.38417 ) P_k_prev=( 0.0006371918 ) s= site=0 numsites=1 index=0 k=5 y_k=-0.0231986 S_k=0.00148283 y_k/sqrt(S_k)=-0.602444 k=5 meas=2.36097 x_k_prev[0]=2.38417 y_k=-0.0231986 S_k= 0.00148283 ll= 1.35896 P_k_prev=0.000637192 u_k=0.00118709 k=6 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.37422 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.3742 ) P_k_now[prev]=( 0.0003633814 ) x_k_prev=(2.37422 ) P_k_prev=( 0.0007177545 ) s= site=0 numsites=1 index=0 k=6 y_k=-0.062093 S_k=0.000906907 y_k/sqrt(S_k)=-2.06187 k=6 meas=2.31213 x_k_prev[0]=2.37422 y_k=-0.062093 S_k= 0.000906907 ll= 1.8171 P_k_prev=0.000717755 u_k=0.00118709 k=7 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.998525 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00355951 after i=0 W_r=0.00355951 u_k=(0.00355951 ) x_k_prev=(2.32521 ) Q_k=( 0.0010631452 ) F_k=( 0.9985250096 ) x_k_now[prev]=(2.32508 ) P_k_now[prev]=( 0.0001497014 ) x_k_prev=(2.32521 ) P_k_prev=( 0.0012124052 ) s= site=0 numsites=1 index=0 k=7 y_k=-0.0114067 S_k=0.00138638 y_k/sqrt(S_k)=-0.306352 k=7 meas=2.3138 x_k_prev[0]=2.32521 y_k=-0.0114067 S_k= 0.00138638 ll= 4.14177 P_k_prev=0.00121241 u_k=0.00355951 k=8 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.31528 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.31523 ) P_k_now[prev]=( 0.0001521418 ) x_k_prev=(2.31528 ) P_k_prev=( 0.0005067227 ) s= site=0 numsites=1 index=0 k=8 y_k=0.0387434 S_k=0.000835229 y_k/sqrt(S_k)=1.34059 k=8 meas=2.35403 x_k_prev[0]=2.31528 y_k=0.0387434 S_k= 0.000835229 ll= 5.86814 P_k_prev=0.000506723 u_k=0.00118709 k=9 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.33882 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.33879 ) P_k_now[prev]=( 0.0001993006 ) x_k_prev=(2.33882 ) P_k_prev=( 0.0005538351 ) s= site=0 numsites=1 index=0 k=9 y_k=0.0811406 S_k=0.00263868 y_k/sqrt(S_k)=1.57959 k=9 meas=2.41996 x_k_prev[0]=2.33882 y_k=0.0811406 S_k= 0.00263868 ll= 6.67039 P_k_prev=0.000553835 u_k=0.00118709 k=10 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.35588 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.35586 ) P_k_now[prev]=( 0.0004375903 ) x_k_prev=(2.35588 ) P_k_prev=( 0.0007918904 ) s= site=0 numsites=1 index=0 k=10 y_k=0.00263976 S_k=0.00148108 y_k/sqrt(S_k)=0.0685924 k=10 meas=2.35852 x_k_prev[0]=2.35588 y_k=0.00263976 S_k= 0.00148108 ll= 9.00659 P_k_prev=0.00079189 u_k=0.00118709 k=11 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.35732 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.35729 ) P_k_now[prev]=( 0.0003684889 ) x_k_prev=(2.35732 ) P_k_prev=( 0.0007228570 ) s= site=0 numsites=1 index=0 k=11 y_k=0.00656383 S_k=0.00118614 y_k/sqrt(S_k)=0.190585 k=11 meas=2.36389 x_k_prev[0]=2.35732 y_k=0.00656383 S_k= 0.00118614 ll= 11.438 P_k_prev=0.000722857 u_k=0.00118709 k=12 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.36135 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.36132 ) P_k_now[prev]=( 0.0002823342 ) x_k_prev=(2.36135 ) P_k_prev=( 0.0006367870 ) s= site=0 numsites=1 index=0 k=12 y_k=-0.0183908 S_k=0.00097064 y_k/sqrt(S_k)=-0.590299 k=12 meas=2.34296 x_k_prev[0]=2.36135 y_k=-0.0183908 S_k= 0.00097064 ll= 13.8136 P_k_prev=0.000636787 u_k=0.00118709 k=13 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.34931 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.34928 ) P_k_now[prev]=( 0.0002190237 ) x_k_prev=(2.34931 ) P_k_prev=( 0.0005735388 ) s= site=0 numsites=1 index=0 k=13 y_k=0.062433 S_k=0.00142915 y_k/sqrt(S_k)=1.65149 k=13 meas=2.41175 x_k_prev[0]=2.34931 y_k=0.062433 S_k= 0.00142915 ll= 14.8063 P_k_prev=0.000573539 u_k=0.00118709 k=14 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.37439 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.37437 ) P_k_now[prev]=( 0.0003433698 ) x_k_prev=(2.37439 ) P_k_prev=( 0.0006977626 ) s= site=0 numsites=1 index=0 k=14 y_k=-0.0519964 S_k=0.0029132 y_k/sqrt(S_k)=-0.963359 k=14 meas=2.32239 x_k_prev[0]=2.37439 y_k=-0.0519964 S_k= 0.0029132 ll= 16.3426 P_k_prev=0.000697763 u_k=0.00118709 k=15 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.36196 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.36193 ) P_k_now[prev]=( 0.0005306359 ) x_k_prev=(2.36196 ) P_k_prev=( 0.0008848445 ) s= site=0 numsites=1 index=0 k=15 y_k=-0.00364153 S_k=0.00160062 y_k/sqrt(S_k)=-0.0910206 k=15 meas=2.35832 x_k_prev[0]=2.36196 y_k=-0.00364153 S_k= 0.00160062 ll= 18.6382 P_k_prev=0.000884845 u_k=0.00118709 k=16 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.35997 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.35995 ) P_k_now[prev]=( 0.0003956913 ) x_k_prev=(2.35997 ) P_k_prev=( 0.0007500326 ) s= site=0 numsites=1 index=0 k=16 y_k=0.00273234 S_k=0.00126284 y_k/sqrt(S_k)=0.0768882 k=16 meas=2.36271 x_k_prev[0]=2.35997 y_k=0.00273234 S_k= 0.00126284 ll= 21.0535 P_k_prev=0.000750033 u_k=0.00118709 loglik started start reading parameters start useext end useext, start init treatment start correlations start feedback timer stuff starting numpar=3 1 mu_log.body.size 2 dt_log.body.size_1 3 stat_sdev_log.body.size_1 useext checking starting correlation2 starting filling in matrices eigenvector stuff m vector stuff i=0 m_prev[i]=2.41324 pull=0.000492026 m_after[i]=0.00118738 m=(0.00118738 ) dosmooth=0 me=0x557621c2e2b8 meas_smooth=0 meas_tot=0x557621c2e2b8 me[0].tm=1996 omega/Lambda matrix stuff Making P, Q, x matrices Making residual and prior expectancy matrices Making smoothing matrices Correlation3 starting loglik init2 loglik init3 Sigma matrix starting Finding V*Sigma^2 Finding V*Sigma^2*V' Initialize x,u,P,F num_states=1 num_series=1 mean time 0:2005.18 is_complex=0 num_states=1 W initialized W,u initialized meantime filled meantime=2005.18 Fill W, initialize x, u t00=1996 first_init_time=-1e+07 meantime=2005.18 W_r=(2.41324 ) Fill u useext2 starting sigma^2=(0.000354905 ) Omega_r=(0.000354905 ) A=(-0.000492026 ) lambda_r=(-0.000492026 ) V_r=(1 ) Vinv_r=(1 ) k=0 u_k=(2.41324 ) x_k_prev=(2.41324 ) Q_k=( 0.3606565365 ) F_k=( 0.0000000000 ) x_k_now[prev]=(0 ) P_k_now[prev]=( 0.0000000000 ) x_k_prev=(2.41324 ) P_k_prev=( 0.3606565365 ) s= site=0 numsites=1 index=0 k=0 y_k=0.164032 S_k=0.363503 y_k/sqrt(S_k)=0.272066 k=0 meas=2.57728 x_k_prev[0]=2.41324 y_k=0.164032 S_k= 0.363503 ll= -0.449965 P_k_prev=0.360657 u_k=2.41324 k=1 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.57591 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.57599 ) P_k_now[prev]=( 0.0028243171 ) x_k_prev=(2.57591 ) P_k_prev=( 0.0031762697 ) s= site=0 numsites=1 index=0 k=1 y_k=-0.0510384 S_k=0.00435055 y_k/sqrt(S_k)=-0.773794 k=1 meas=2.52487 x_k_prev[0]=2.57591 y_k=-0.0510384 S_k= 0.00435055 ll= 1.05045 P_k_prev=0.00317627 u_k=0.00118709 k=2 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.53859 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.53865 ) P_k_now[prev]=( 0.0008573219 ) x_k_prev=(2.53859 ) P_k_prev=( 0.0012112092 ) s= site=0 numsites=1 index=0 k=2 y_k=-0.0472003 S_k=0.00283255 y_k/sqrt(S_k)=-0.886862 k=2 meas=2.49139 x_k_prev[0]=2.53859 y_k=-0.0472003 S_k= 0.00283255 ll= 2.67153 P_k_prev=0.00121121 u_k=0.00118709 k=3 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.51835 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.5184 ) P_k_now[prev]=( 0.0006932922 ) x_k_prev=(2.51835 ) P_k_prev=( 0.0010473408 ) s= site=0 numsites=1 index=0 k=3 y_k=-0.122384 S_k=0.00206263 y_k/sqrt(S_k)=-2.69471 k=3 meas=2.39597 x_k_prev[0]=2.51835 y_k=-0.122384 S_k= 0.00206263 ll= 1.21374 P_k_prev=0.00104734 u_k=0.00118709 k=4 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.45619 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.45621 ) P_k_now[prev]=( 0.0005155332 ) x_k_prev=(2.45619 ) P_k_prev=( 0.0008697567 ) s= site=0 numsites=1 index=0 k=4 y_k=-0.106726 S_k=0.00128868 y_k/sqrt(S_k)=-2.97302 k=4 meas=2.34946 x_k_prev[0]=2.45619 y_k=-0.106726 S_k= 0.00128868 ll= -0.797536 P_k_prev=0.000869757 u_k=0.00118709 k=5 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.38417 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.38416 ) P_k_now[prev]=( 0.0002827394 ) x_k_prev=(2.38417 ) P_k_prev=( 0.0006371918 ) s= site=0 numsites=1 index=0 k=5 y_k=-0.0231986 S_k=0.00148283 y_k/sqrt(S_k)=-0.602444 k=5 meas=2.36097 x_k_prev[0]=2.38417 y_k=-0.0231986 S_k= 0.00148283 ll= 1.35896 P_k_prev=0.000637192 u_k=0.00118709 k=6 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.37422 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.3742 ) P_k_now[prev]=( 0.0003633814 ) x_k_prev=(2.37422 ) P_k_prev=( 0.0007177545 ) s= site=0 numsites=1 index=0 k=6 y_k=-0.062093 S_k=0.000906907 y_k/sqrt(S_k)=-2.06187 k=6 meas=2.31213 x_k_prev[0]=2.37422 y_k=-0.062093 S_k= 0.000906907 ll= 1.8171 P_k_prev=0.000717755 u_k=0.00118709 k=7 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.998525 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00355951 after i=0 W_r=0.00355951 u_k=(0.00355951 ) x_k_prev=(2.32521 ) Q_k=( 0.0010631452 ) F_k=( 0.9985250096 ) x_k_now[prev]=(2.32508 ) P_k_now[prev]=( 0.0001497014 ) x_k_prev=(2.32521 ) P_k_prev=( 0.0012124052 ) s= site=0 numsites=1 index=0 k=7 y_k=-0.0114067 S_k=0.00138638 y_k/sqrt(S_k)=-0.306352 k=7 meas=2.3138 x_k_prev[0]=2.32521 y_k=-0.0114067 S_k= 0.00138638 ll= 4.14177 P_k_prev=0.00121241 u_k=0.00355951 k=8 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.31528 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.31523 ) P_k_now[prev]=( 0.0001521418 ) x_k_prev=(2.31528 ) P_k_prev=( 0.0005067227 ) s= site=0 numsites=1 index=0 k=8 y_k=0.0387434 S_k=0.000835229 y_k/sqrt(S_k)=1.34059 k=8 meas=2.35403 x_k_prev[0]=2.31528 y_k=0.0387434 S_k= 0.000835229 ll= 5.86814 P_k_prev=0.000506723 u_k=0.00118709 k=9 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.33882 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.33879 ) P_k_now[prev]=( 0.0001993006 ) x_k_prev=(2.33882 ) P_k_prev=( 0.0005538351 ) s= site=0 numsites=1 index=0 k=9 y_k=0.0811406 S_k=0.00263868 y_k/sqrt(S_k)=1.57959 k=9 meas=2.41996 x_k_prev[0]=2.33882 y_k=0.0811406 S_k= 0.00263868 ll= 6.67039 P_k_prev=0.000553835 u_k=0.00118709 k=10 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.35588 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.35586 ) P_k_now[prev]=( 0.0004375903 ) x_k_prev=(2.35588 ) P_k_prev=( 0.0007918904 ) s= site=0 numsites=1 index=0 k=10 y_k=0.00263976 S_k=0.00148108 y_k/sqrt(S_k)=0.0685924 k=10 meas=2.35852 x_k_prev[0]=2.35588 y_k=0.00263976 S_k= 0.00148108 ll= 9.00659 P_k_prev=0.00079189 u_k=0.00118709 k=11 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.35732 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.35729 ) P_k_now[prev]=( 0.0003684889 ) x_k_prev=(2.35732 ) P_k_prev=( 0.0007228570 ) s= site=0 numsites=1 index=0 k=11 y_k=0.00656383 S_k=0.00118614 y_k/sqrt(S_k)=0.190585 k=11 meas=2.36389 x_k_prev[0]=2.35732 y_k=0.00656383 S_k= 0.00118614 ll= 11.438 P_k_prev=0.000722857 u_k=0.00118709 k=12 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.36135 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.36132 ) P_k_now[prev]=( 0.0002823342 ) x_k_prev=(2.36135 ) P_k_prev=( 0.0006367870 ) s= site=0 numsites=1 index=0 k=12 y_k=-0.0183908 S_k=0.00097064 y_k/sqrt(S_k)=-0.590299 k=12 meas=2.34296 x_k_prev[0]=2.36135 y_k=-0.0183908 S_k= 0.00097064 ll= 13.8136 P_k_prev=0.000636787 u_k=0.00118709 k=13 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.34931 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.34928 ) P_k_now[prev]=( 0.0002190237 ) x_k_prev=(2.34931 ) P_k_prev=( 0.0005735388 ) s= site=0 numsites=1 index=0 k=13 y_k=0.062433 S_k=0.00142915 y_k/sqrt(S_k)=1.65149 k=13 meas=2.41175 x_k_prev[0]=2.34931 y_k=0.062433 S_k= 0.00142915 ll= 14.8063 P_k_prev=0.000573539 u_k=0.00118709 k=14 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.37439 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.37437 ) P_k_now[prev]=( 0.0003433698 ) x_k_prev=(2.37439 ) P_k_prev=( 0.0006977626 ) s= site=0 numsites=1 index=0 k=14 y_k=-0.0519964 S_k=0.0029132 y_k/sqrt(S_k)=-0.963359 k=14 meas=2.32239 x_k_prev[0]=2.37439 y_k=-0.0519964 S_k= 0.0029132 ll= 16.3426 P_k_prev=0.000697763 u_k=0.00118709 k=15 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.36196 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.36193 ) P_k_now[prev]=( 0.0005306359 ) x_k_prev=(2.36196 ) P_k_prev=( 0.0008848445 ) s= site=0 numsites=1 index=0 k=15 y_k=-0.00364153 S_k=0.00160062 y_k/sqrt(S_k)=-0.0910206 k=15 meas=2.35832 x_k_prev[0]=2.36196 y_k=-0.00364153 S_k= 0.00160062 ll= 18.6382 P_k_prev=0.000884845 u_k=0.00118709 k=16 start i=0 W_r=0 b=0 i=0 lambda_r=-0.000492026 exp(ee)=0.999508 V_r=1 m=0.00118738 B=0 m/B i=0 W_r=0.00118709 after i=0 W_r=0.00118709 u_k=(0.00118709 ) x_k_prev=(2.35997 ) Q_k=( 0.0003547305 ) F_k=( 0.9995080946 ) x_k_now[prev]=(2.35995 ) P_k_now[prev]=( 0.0003956913 ) x_k_prev=(2.35997 ) P_k_prev=( 0.0007500326 ) s= site=0 numsites=1 index=0 k=16 y_k=0.00273234 S_k=0.00126284 y_k/sqrt(S_k)=0.0768882 k=16 meas=2.36271 x_k_prev[0]=2.35997 y_k=0.00273234 S_k= 0.00126284 ll= 21.0535 P_k_prev=0.000750033 u_k=0.00118709 [1] 21.05351 > > > > > cleanEx() > nameEx("layerparamlogliks") > ### * layerparamlogliks > > flush(stderr()); flush(stdout()) > > ### Name: layer.param.logliks > ### Title: Returning log-likelihoods for a set of parameter value sets. > ### Aliases: layer.param.logliks > > ### ** Examples > > # Define time series (time points, values and in this case also > # sample standard deviations and sample size, used for calculating > # standard errors for each measurement). > library(layeranalyzer) > X=layer.data.series(time.points=malta$Time.Year, + value.points=malta$Mean..log.body.mass., + std.dev=sqrt(malta$Variance.calculated.from.the.data), + num.meas.per.value=malta$Sample.size,name="log.body.size") > > # Define process structure for analysis purposes: > ser=layer.series.structure(X,numlayers=1) > > # Perform Bayesian analysis, short run: > res=layer.analyzer(ser,num.MCMC=100,burnin=1000) > > # Check one specific parameter set, then the same without > # mu (expected value), dt (characteristic time) and > # stat-sdev (stationary standaard deviation) specified. (Unspecified > # parameters are optimized over.) > sets=rbind(c(2.4, 300, 0.04), c(NA, 300, 0.04), c(2.4, NA, 0.04), c(2.4,300,NA)) > lls=layer.param.logliks(res, sets, silent.mode=FALSE) 0.001 10 prior_obs1=0.001 prior_obs2=10 num_layers=1 0.001 10 mu in (-10.000, 10.000), m = 0.000 s = 5.102), dt in ( 0.001,1000.000), lm= 0.000 ls= 3.524), sigma in ( 0.001,100.000), lm= -1.151 ls= 2.937), stat_sdev in ( 0.001, 10.000), lm= -2.303 ls= 2.350), lin in (-10.000, 10.000), m = 0.000 s = 5.102), beta in (-10.000, 10.000), m = 0.000 s = 5.102), init in (-100.000,100.000), m = 0.000 s = 51.020), obs in ( 0.001, 10.000), lm= -2.303 ls= 2.350). d.falloff in ( 0.000,1000000.000), lm= 0.000 ls= 7.049). is_log=0. 0 extseries.size=0 numpar=3 1 mu_log.body.size 2 dt_log.body.size_1 3 stat_sdev_log.body.size_1 ********************************* Starting loglik treatment ********************************* currparams=(2.4 300 0.04 ) currparams=(-1e+07 300 0.04 ) Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly currparams=(2.4 -1e+07 0.04 ) Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly currparams=(2.4 300 -1e+07 ) Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Warning in (function (par, fn, gr = NULL, ..., method = c("Nelder-Mead", : one-dimensional optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly > > > > > cleanEx() > nameEx("layerpredictestimate") > ### * layerpredictestimate > > flush(stderr()); flush(stdout()) > > ### Name: layer.predict.estimate > ### Title: Prediction based on previously performed analysis using single > ### point estimate for the parameter set > ### Aliases: layer.predict.estimate > > ### ** Examples > > library(layeranalyzer) > > # Define time series (time points, values and in this case also > # sample standard deviations and sample size, used for calculating > # standard errors for each measurement). > X=layer.data.series(time.points=malta$Time.Year, + value.points=malta$Mean..log.body.mass., + std.dev=sqrt(malta$Variance.calculated.from.the.data), + num.meas.per.value=malta$Sample.size,name="log.body.size") > > # Define process structure for analysis purposes: > ser=layer.series.structure(X,numlayers=1) > > # Perform Bayesian analysis: > res=layer.analyzer(ser,mcmc=TRUE,num.MCMC=200,burnin=800) > > # Now make up some new values much later: > t2=2100:2140 > v2=rnorm(length(t2),2+0.1*sin(2*pi*t2/50),0.02) > X2=layer.data.series(time.points=t2, + value.points=v2, std.dev=rep(0.02,length(t2)), + num.meas.per.value=rep(1,length(t2)), + name="log.body.size") > > # Perform predictions (old data not conditioned on): > p3=layer.predict.estimate(X2, analysis=res, smoothing.time.diff=0.25, + smoothing.start=1900,smoothing.end=2300) > > # Fetch the results > t3=p3$process.time.points > m3=p3$process.mean[1,] > l3=p3$process.lower95[1,] > u3=p3$process.upper95[1,] > > # Plot the results > plot(t3,m3,type="l",ylim=c(min(l3),max(u3))) > points(t2,v2) > lines(t3,l3,col="red") > lines(t3,u3,col="red") > > > > > > > cleanEx() > nameEx("layerpredictmcmc") > ### * layerpredictmcmc > > flush(stderr()); flush(stdout()) > > ### Name: layer.predict.mcmc > ### Title: Prediction based on previously performed MCMC analysis > ### Aliases: layer.predict.mcmc > > ### ** Examples > > library(layeranalyzer) > > # Define time series (time points, values and in this case also > # sample standard deviations and sample size, used for calculating > # standard errors for each measurement). > X<-layer.data.series(time.points=malta$Time.Year, + value.points=malta$Mean..log.body.mass., + std.dev=sqrt(malta$Variance.calculated.from.the.data), + num.meas.per.value=malta$Sample.size,name="log.body.size") > > # Define process structure for analysis purposes: > ser<-layer.series.structure(X,numlayers=1) > > # Perform analysis and make sure MCMC samples are returned: > > > > > cleanEx() > nameEx("layerpredictmcmclist") > ### * layerpredictmcmclist > > flush(stderr()); flush(stdout()) > > ### Name: layer.predict.mcmc.list > ### Title: Prediction based on previously performed MCMC analysis > ### Aliases: layer.predict.mcmc.list > > ### ** Examples > > library(layeranalyzer) > > # Define time series (time points, values and in this case also > # sample standard deviations and sample size, used for calculating > # standard errors for each measurement). > X<-layer.data.series(time.points=malta$Time.Year, + value.points=malta$Mean..log.body.mass., + std.dev=sqrt(malta$Variance.calculated.from.the.data), + num.meas.per.value=malta$Sample.size,name="log.body.size") > > # Define process structure for analysis purposes: > ser<-layer.series.structure(X,numlayers=1) > > # Perform analysis and make sure MCMC samples are returned: > > > > > cleanEx() > nameEx("layerprior") > ### * layerprior > > flush(stderr()); flush(stdout()) > > ### Name: layer.prior > ### Title: Specifying a prior distribution for the layer.analyzer. > ### Aliases: layer.prior > > ### ** Examples > > library(layeranalyzer) > > pr=layer.prior(mu=c(-10,10),dt=c(0.001,1000),sigma=c(0.01,10), + lin=c(-1,1),beta=c(-1,1),init=c(-10,10), + obs=c(0.01,1),islog=0) > > > > cleanEx() > nameEx("layerseriesstructure") > ### * layerseriesstructure > > flush(stderr()); flush(stdout()) > > ### Name: layer.series.structure > ### Title: Represents a measurement time series. > ### Aliases: layer.series.structure > > ### ** Examples > > library(layeranalyzer) > > # Will look at the structure of a single simulated > # time series (simulated using a 2-layered model) > > # Read a simulated dataset with 2 layers: > test.ex=read.table("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt") > names(test.ex)<-c("time","value") > ts<-layer.data.series(time=test.ex$time, value=test.ex$value,name="X") > > # Look at 2 models, where the only difference is the structure > # specified in 'layer.series.structure': > > # ML estimates for an OU model: > > # ML estimates for an 2-layered model model: > > # PS: The models can then be compared, see 'compare.layered'. > > > > cleanEx() > nameEx("layerstandardlogprior") > ### * layerstandardlogprior > > flush(stderr()); flush(stdout()) > > ### Name: layer.standard.log.prior > ### Title: Specifying a suggestion for prior distribution, for data that > ### will be log-transformed. > ### Aliases: layer.standard.log.prior > > ### ** Examples > > library(layeranalyzer) > > # Output the hyperparameters of the standard prior for > # log-transformed measurements > layer.standard.log.prior $mu [1] -10 10 $dt [1] 1e-03 1e+03 $sigma [1] 1e-04 1e+02 $stat.sdev [1] 1e-03 1e+01 $init [1] -100 100 $lin [1] -10 10 $beta [1] -10 10 $dist.corr.falloff [1] 1e-06 1e+06 $obs [1] 1e-03 1e+01 $islog [1] 1 attr(,"class") [1] "layer.prior" > > > > cleanEx() > nameEx("layerstandardprior") > ### * layerstandardprior > > flush(stderr()); flush(stdout()) > > ### Name: layer.standard.prior > ### Title: Specifying a default prior distribution for the layer.analyzer. > ### Aliases: layer.standard.prior > > ### ** Examples > > library(layeranalyzer) > > # Output the hyperparameters of the standard (default) prior > layer.standard.prior $mu [1] -10 10 $dt [1] 1e-03 1e+03 $sigma [1] 1e-03 1e+02 $stat.sdev [1] 1e-03 1e+01 $init [1] -100 100 $lin [1] -10 10 $beta [1] -10 10 $dist.corr.falloff [1] 1e-06 1e+06 $obs [1] 1e-03 1e+01 $islog [1] 0 attr(,"class") [1] "layer.prior" > > > > cleanEx() > nameEx("layerstructname") > ### * layerstructname > > flush(stderr()); flush(stdout()) > > ### Name: layer.struct.name > ### Title: Generate a description of the process structure of a time > ### series. > ### Aliases: layer.struct.name > > ### ** Examples > > library(layeranalyzer) > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt", + name="X") > > # Insert the data intro a structure specificatiion > # with the default structure (OU): > struct1=layer.series.structure(X) > layer.struct.name(struct1) [1] "OU" > # Output should be "OU" > > # Insert the data into a structure with 2 layers, where the > # lowest layer has no pull (i.e. Brownian motion, here also called > # "Random Walk" which is abbreviated to "RW"). Note that when the > # lowest layer is Brownian, the processes are nonstationary and > # need initial value treatment (init.0=TRUE). > # The top layer is specified to have no stochasticity > # (i.e. deterministic given the layer below it). > struct2=layer.series.structure(X,numlayers=2, no.pull=TRUE, no.sigma=1, + init.0=TRUE) > layer.struct.name(struct2) [1] "2-layered: Layer 2: RW , Layer 1: deterministic tracking" > # Output should be "2-layered: Layer 2: RW , Layer 1: deterministic tracking" > > > > > cleanEx() > nameEx("layerwideprior") > ### * layerwideprior > > flush(stderr()); flush(stdout()) > > ### Name: layer.wide.prior > ### Title: Specifying a suggestion for a wide (little information) prior > ### distribution. > ### Aliases: layer.wide.prior > > ### ** Examples > > library(layeranalyzer) > > # Output the hyperparameters of a prior distribution that is assumes > # very little (little information): > layer.wide.prior $mu [1] -10000 10000 $dt [1] 1e-06 1e+06 $sigma [1] 1e-03 1e+03 $stat.sdev [1] 1e-05 1e+04 $init [1] -1e+05 1e+05 $lin [1] -1000 1000 $beta [1] -1000 1000 $dist.corr.falloff [1] 1e-20 1e+20 $obs [1] 1e-05 1e+03 $islog [1] 0 attr(,"class") [1] "layer.prior" > > > > cleanEx() > nameEx("logliklayered") > ### * logliklayered > > flush(stderr()); flush(stdout()) > > ### Name: logLik.layered > ### Title: Return maximum likelihood on the logarithmic scale for return > ### object from 'layer.analyzer'. > ### Aliases: logLik.layered > > ### ** Examples > > library(layeranalyzer) > pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt") > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt", + name="X") > > # Calculate classic maximum likelihoods for an OU model, a 2 layered model > # and a 3 layered model. Look at log-likelihoods: > > > > cleanEx() > nameEx("lratepr") > ### * lratepr > > flush(stderr()); flush(stdout()) > > ### Name: lrate.pr > ### Title: A "layer.prior" object used for diversifications rate analysis. > ### Aliases: lrate.pr > > ### ** Examples > > library(layeranalyzer) > > # Output the hyperparameters of a prior distribution we have used > # for diversification rate datasets: > lrate.pr $mu [1] -20 20 $dt [1] 1e-03 1e+03 $sigma [1] 1e-02 1e+02 $init [1] -20 20 $lin [1] -10 10 $beta [1] -10 10 $islog [1] 0 attr(,"class") [1] "layer.prior" > > > > cleanEx() > nameEx("ncombinations") > ### * ncombinations > > flush(stderr()); flush(stdout()) > > ### Name: n.combinations > ### Title: Returns all ways of combining 'n' objects going form 0 to > ### . > ### Aliases: n.combinations > > ### ** Examples > > library(layeranalyzer) > > # List the set of combinations up to (but not including) 2 > # of 3 elements (used by the model traversal algorithms): > n.combinations(3,2) [,1] [,2] [,3] [1,] 0 0 0 [2,] 0 0 1 [3,] 0 1 0 [4,] 0 1 1 [5,] 1 0 0 [6,] 1 0 1 [7,] 1 1 0 [8,] 1 1 1 > # This should be the output: > # [,1] [,2] [,3] > #[1,] 0 0 0 > #[2,] 0 0 1 > #[3,] 0 1 0 > #[4,] 0 1 1 > #[5,] 1 0 0 > #[6,] 1 0 1 > #[7,] 1 1 0 > #[8,] 1 1 1 > > > > > cleanEx() > nameEx("nobslayered") > ### * nobslayered > > flush(stderr()); flush(stdout()) > > ### Name: nobs.layered > ### Title: Return number of observations for return object from > ### 'layer.analyzer'. > ### Aliases: nobs.layered > > ### ** Examples > > library(layeranalyzer) > # Read prior and data: > pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt") > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt", + name="X") > > # Do a very short analysis, jsut to get a "layered" object we can run > # "nobs" on. > res1<-layer.analyzer(layer.series.structure(X,numlayers=1,prior=pr), + num.MCMC=40,burnin=400,do.model.likelihood=FALSE) > nobs(res1) [1] 400 > # Output should be: 400. > > > > cleanEx() > nameEx("printlayerseriesstructure") > ### * printlayerseriesstructure > > flush(stderr()); flush(stdout()) > > ### Name: print.layer.series.structure > ### Title: Outputs the description of a 'layer'series'structure' object. > ### Aliases: print.layer.series.structure > > ### ** Examples > > library(layeranalyzer) > # Read an example dataset: > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt", + "X") > > # Make a structure specification that happens to coincide with the way > # the data was simulated, namely as 2 layers with OU as the lowest layer: > struct=layer.series.structure(X,numlayers=2) > > # Show a description of the structure: > print.layer.series.structure(struct) Timeseries: X, structure: 2-layered: Layer 2: OU , Layer 1: OU-like tracking > # Should output: > # Timeseries: X, structure: > # 2-layered: Layer 2: OU , Layer 1: OU-like tracking > > > > > cleanEx() > nameEx("printsummarylayered") > ### * printsummarylayered > > flush(stderr()); flush(stdout()) > > ### Name: print.summary.layered > ### Title: Summarizes the return object from 'layer.analyzer'. > ### Aliases: print.summary.layered > > ### ** Examples > > library(layeranalyzer) > > # Read prior and data: > pr<-layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt") > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_1layer.txt", + "X") > > # Calculate Bayesian model likelihood for an OU model. > # (Devotes a large amount of computer resources in the form of MCMC > # samples, in order to get a stable results). > > > > > cleanEx() > nameEx("readlayerdataseries") > ### * readlayerdataseries > > flush(stderr()); flush(stdout()) > > ### Name: read.layer.data.series > ### Title: Read a series representing a measurement time series. > ### Aliases: read.layer.data.series > > ### ** Examples > > library(layeranalyzer) > > # Read a simulated 2 layer data set from an external web site, call it "Xseries": > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_2layer.txt", + name="X") > > > > > > cleanEx() > nameEx("stepwiseconnectionslayered") > ### * stepwiseconnectionslayered > > flush(stderr()); flush(stdout()) > > ### Name: stepwise.connections.layered > ### Title: Time series analysis tool using linear layered SDEs. > ### Aliases: stepwise.connections.layered > > ### ** Examples > > library(layeranalyzer) > > # Read data: > X1<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_cause.txt", + name="cause") > X2<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_effect.txt", + name="effect") > > # Set structure: > X1struct=layer.series.structure(X1, numlayers=1) > X2struct=layer.series.structure(X2, numlayers=1) > > # Perform traversal of analyses. (PS: this will take some time!) > > # PS: Since only the model of the last iteration is available, > # model comparison is not a priority and the method simply returns > # the best model according to the model selection criterion. > > # Show the estiamtes of the best model: > # Highlight of summary: > # Causal links: > # cause->effect > # As it should be. > > > > > > cleanEx() > nameEx("summarylayered") > ### * summarylayered > > flush(stderr()); flush(stdout()) > > ### Name: summary.layered > ### Title: Summarizes the return object from 'layer.analyzer'. > ### Aliases: summary.layered > > ### ** Examples > > library(layeranalyzer) > > # Read prior and dataset: > pr=layer.load.prior("https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_prior.txt") > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_1layer.txt", + "X") > > # Calculate Bayesian model likelihood for an OU model. > # (PS: The MCMC is restricted, to save time in the example). > res1<-layer.analyzer(layer.series.structure(X,numlayers=1,prior=pr), + num.MCMC=60,burnin=600) > summary(res1) Description: OU Coefficients: Mean Median Lower 95% Upper 95% mu_X 1.702512 1.716915 0.075254 3.424567 dt_X_1 12.510531 11.721703 4.087112 23.321316 stat_sdev_X_1 1.957960 1.967744 1.132939 2.828860 obs_sd_X 0.087047 0.084585 0.044445 0.141681 Model log-likelihood: -136.032 > > # PS: There will be moderately-sized numeric variation in the estimates > # due to the randomness of the MCMC algorithm. A longer MCMC (or > # multiple of them) would yield more stable results. > > > > cleanEx() > nameEx("traverseconnections") > ### * traverseconnections > > flush(stderr()); flush(stdout()) > > ### Name: traverse.connections.layered > ### Title: Time series analysis tool using linear layered SDEs. > ### Aliases: traverse.connections.layered > > ### ** Examples > > library(layeranalyzer) > > # Read data: > X1<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_cause.txt", + name="cause") > X2<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_effect.txt", + name="effect") > > # Set structure: > X1struct<-layer.series.structure(X1, numlayers=1) > X2struct<-layer.series.structure(X2, numlayers=1) > > # Perform traversal of analyses (but note that this will take some time): > > # Compare analyses: > > > > cleanEx() > nameEx("traversestandalone") > ### * traversestandalone > > flush(stderr()); flush(stdout()) > > ### Name: traverse.standalone.layered > ### Title: Model traversal tool for standalone time series. > ### Aliases: traverse.standalone.layered > > ### ** Examples > > library(layeranalyzer) > > # Specify the prior distribution by giving 95% credibility bands > # for the parameter types: > p<-layer.prior(mu=c(-5,5),init=c(-100,100),dt=c(0.5,200),sigma=c(0.01,2),obs=c(0.01,1)) > > # Read the data, a simulated OU (1-layered) process: > X<-read.layer.data.series( + "https://github.com/trondreitan/layeranalyzer/raw/master/doc/test_1layer.txt", + "X") > > # Traverse the possible standalone models up to 2 layers: > > > > > ### *