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CHECK report for netresponse on tokay1

This page was generated on 2019-04-13 11:21:23 -0400 (Sat, 13 Apr 2019).

Package 1052/1649HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
netresponse 1.42.0
Leo Lahti
Snapshot Date: 2019-04-12 17:01:30 -0400 (Fri, 12 Apr 2019)
URL: https://git.bioconductor.org/packages/netresponse
Branch: RELEASE_3_8
Last Commit: e9b9422
Last Changed Date: 2018-10-30 11:41:46 -0400 (Tue, 30 Oct 2018)
malbec1 Linux (Ubuntu 16.04.6 LTS) / x86_64  OK  OK  WARNINGS UNNEEDED, same version exists in internal repository
tokay1 Windows Server 2012 R2 Standard / x64  OK  OK [ WARNINGS ] OK UNNEEDED, same version exists in internal repository
merida1 OS X 10.11.6 El Capitan / x86_64  OK  OK  WARNINGS  OK UNNEEDED, same version exists in internal repository

Summary

Package: netresponse
Version: 1.42.0
Command: C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:netresponse.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings netresponse_1.42.0.tar.gz
StartedAt: 2019-04-13 04:09:37 -0400 (Sat, 13 Apr 2019)
EndedAt: 2019-04-13 04:16:55 -0400 (Sat, 13 Apr 2019)
EllapsedTime: 438.0 seconds
RetCode: 0
Status:  WARNINGS  
CheckDir: netresponse.Rcheck
Warnings: 1

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:netresponse.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings netresponse_1.42.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.8-bioc/meat/netresponse.Rcheck'
* using R version 3.5.3 (2019-03-11)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'netresponse/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'netresponse' version '1.42.0'
* checking package namespace information ... OK
* checking package dependencies ...Warning: unable to access index for repository https://CRAN.R-project.org/src/contrib:
  cannot open URL 'https://CRAN.R-project.org/src/contrib/PACKAGES'
 OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'netresponse' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking compiled code ... NOTE
File 'netresponse/libs/i386/netresponse.dll':
  Found 'rand', possibly from 'rand' (C)
    Object: 'netresponse.o'
  Found 'srand', possibly from 'srand' (C)
    Object: 'netresponse.o'

Compiled code should not call entry points which might terminate R nor
write to stdout/stderr instead of to the console, nor use Fortran I/O
nor system RNGs.

See 'Writing portable packages' in the 'Writing R Extensions' manual.
* checking files in 'vignettes' ... WARNING
Files in the 'vignettes' directory but no files in 'inst/doc':
  'NetResponse.Rmd', 'NetResponse.md', 'TODO/TODO.Rmd',
    'fig/NetResponse2-1.png', 'fig/NetResponse2b-1.png',
    'fig/NetResponse3-1.png', 'fig/NetResponse4-1.png',
    'fig/NetResponse5-1.png', 'fig/NetResponse7-1.png',
    'fig/vdp-1.png', 'main.R', 'netresponse.bib', 'netresponse.pdf'
Package has no Sweave vignette sources and no VignetteBuilder field.
* checking examples ...
** running examples for arch 'i386' ... OK
Examples with CPU or elapsed time > 5s
                       user system elapsed
ICMg.combined.sampler 36.08   0.04   36.13
** running examples for arch 'x64' ... OK
Examples with CPU or elapsed time > 5s
                       user system elapsed
ICMg.combined.sampler 40.56   0.01   40.58
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  Running 'ICMg.test.R'
  Running 'bicmixture.R'
  Running 'mixture.model.test.R'
  Running 'mixture.model.test.multimodal.R'
  Running 'mixture.model.test.singlemode.R'
  Running 'timing.R'
  Running 'toydata2.R'
  Running 'validate.netresponse.R'
  Running 'validate.pca.basis.R'
  Running 'vdpmixture.R'
 OK
** running tests for arch 'x64' ...
  Running 'ICMg.test.R'
  Running 'bicmixture.R'
  Running 'mixture.model.test.R'
  Running 'mixture.model.test.multimodal.R'
  Running 'mixture.model.test.singlemode.R'
  Running 'timing.R'
  Running 'toydata2.R'
  Running 'validate.netresponse.R'
  Running 'validate.pca.basis.R'
  Running 'vdpmixture.R'
 OK
* checking PDF version of manual ... OK
* DONE

Status: 1 WARNING, 1 NOTE
See
  'C:/Users/biocbuild/bbs-3.8-bioc/meat/netresponse.Rcheck/00check.log'
for details.



Installation output

netresponse.Rcheck/00install.out

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##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O https://malbec1.bioconductor.org/BBS/3.8/bioc/src/contrib/netresponse_1.42.0.tar.gz && rm -rf netresponse.buildbin-libdir && mkdir netresponse.buildbin-libdir && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=netresponse.buildbin-libdir netresponse_1.42.0.tar.gz && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL netresponse_1.42.0.zip && rm netresponse_1.42.0.tar.gz netresponse_1.42.0.zip
###
##############################################################################
##############################################################################


  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100 1030k  100 1030k    0     0  23.0M      0 --:--:-- --:--:-- --:--:-- 25.1M

install for i386

* installing *source* package 'netresponse' ...
** libs
C:/Rtools/mingw_32/bin/gcc  -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O3 -Wall  -std=gnu99 -mtune=generic -c netresponse.c -o netresponse.o
netresponse.c: In function 'mHPpost':
netresponse.c:264:15: warning: unused variable 'prior_fields' [-Wunused-variable]
   const char *prior_fields[]={"Mumu","S2mu",
               ^
netresponse.c: In function 'vdp_mk_hp_posterior':
netresponse.c:210:3: warning: 'U_hat_table' may be used uninitialized in this function [-Wmaybe-uninitialized]
   update_centroids(datalen, ncentroids, dim1, dim2,
   ^
netresponse.c:210:3: warning: 'data2_int' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c: In function 'mLogLambda':
netresponse.c:713:3: warning: 'U_p' may be used uninitialized in this function [-Wmaybe-uninitialized]
   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde, 
   ^
netresponse.c:713:3: warning: 'KsiBeta' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'KsiAlpha' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'BetaKsi' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'AlphaKsi' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'Mutilde' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'Mubar' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'S2mu' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'Mumu' may be used uninitialized in this function [-Wmaybe-uninitialized]
C:/Rtools/mingw_32/bin/gcc -shared -s -static-libgcc -o netresponse.dll tmp.def netresponse.o -LC:/extsoft/lib/i386 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/netresponse.buildbin-libdir/netresponse/libs/i386
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'netresponse'
    finding HTML links ... done
    ICMg.combined.sampler                   html  
    ICMg.get.comp.memberships               html  
    ICMg.links.sampler                      html  
    NetResponseModel-class                  html  
    P.S                                     html  
    P.Sr                                    html  
    P.r.s                                   html  
    P.rS                                    html  
    P.rs.joint                              html  
    P.rs.joint.individual                   html  
    P.s.individual                          html  
    P.s.r                                   html  
    PlotMixture                             html  
    PlotMixtureBivariate                    html  
    PlotMixtureMultivariate                 html  
    PlotMixtureMultivariate.deprecated      html  
    PlotMixtureUnivariate                   html  
    add.ellipse                             html  
    bic.mixture                             html  
    bic.mixture.multivariate                html  
    bic.mixture.univariate                  html  
    bic.select.best.mode                    html  
    centerData                              html  
    check.matrix                            html  
    check.network                           html  
    continuous.responses                    html  
    detect.responses                        html  
    dna                                     html  
    enrichment.list.factor                  html  
    enrichment.list.factor.minimal          html  
    factor.responses                        html  
    factor.responses.minimal                html  
    filter.netw                             html  
    filter.network                          html  
    find.similar.features                   html  
    generate.toydata                        html  
    get.dat-NetResponseModel-method         html  
    get.mis                                 html  
    get.model.parameters                    html  
    get.subnets-NetResponseModel-method     html  
    getqofz-NetResponseModel-method         html  
    independent.models                      html  
    list.responses.continuous.multi         html  
    list.responses.continuous.single        html  
    list.responses.factor                   html  
    list.responses.factor.minimal           html  
    list.significant.responses              html  
    listify.groupings                       html  
    mixture.model                           html  
    model.stats                             html  
    netresponse-package                     html  
    order.responses                         html  
    osmo                                    html  
    pick.model.pairs                        html  
    pick.model.parameters                   html  
    plotPCA                                 html  
    plot_associations                       html  
    plot_data                               html  
    plot_expression                         html  
    plot_matrix                             html  
    plot_response                           html  
    plot_responses                          html  
    plot_scale                              html  
    plot_subnet                             html  
    read.sif                                html  
    remove.negative.edges                   html  
    response.enrichment                     html  
    response2sample                         html  
    sample2response                         html  
    set.breaks                              html  
    toydata                                 html  
    update.model.pair                       html  
    vdp.mixt                                html  
    vectorize.groupings                     html  
    write.netresponse.results               html  
** building package indices
** installing vignettes
** testing if installed package can be loaded
In R CMD INSTALL

install for x64

* installing *source* package 'netresponse' ...
** libs
C:/Rtools/mingw_64/bin/gcc  -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -std=gnu99 -mtune=generic -c netresponse.c -o netresponse.o
netresponse.c: In function 'mHPpost':
netresponse.c:264:15: warning: unused variable 'prior_fields' [-Wunused-variable]
   const char *prior_fields[]={"Mumu","S2mu",
               ^
netresponse.c: In function 'mLogLambda':
netresponse.c:713:3: warning: 'U_p' may be used uninitialized in this function [-Wmaybe-uninitialized]
   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde, 
   ^
netresponse.c:713:3: warning: 'KsiBeta' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'KsiAlpha' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'BetaKsi' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'AlphaKsi' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'Mutilde' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'Mubar' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'S2mu' may be used uninitialized in this function [-Wmaybe-uninitialized]
netresponse.c:713:3: warning: 'Mumu' may be used uninitialized in this function [-Wmaybe-uninitialized]
C:/Rtools/mingw_64/bin/gcc -shared -s -static-libgcc -o netresponse.dll tmp.def netresponse.o -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/netresponse.buildbin-libdir/netresponse/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'netresponse' as netresponse_1.42.0.zip
* DONE (netresponse)
In R CMD INSTALL
In R CMD INSTALL
* installing to library 'C:/Users/biocbuild/bbs-3.8-bioc/R/library'
package 'netresponse' successfully unpacked and MD5 sums checked
In R CMD INSTALL

Tests output

netresponse.Rcheck/tests_i386/bicmixture.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> # 1. vdp.mixt: moodien loytyminen eri dimensiolla, naytemaarilla ja komponenteilla
> #   -> ainakin nopea check
> 
> #######################################################################
> 
> # Generate random data from five Gaussians. 
> # Detect modes with vdp-gm. 
> # Plot data points and detected clusters with variance ellipses
> 
> #######################################################################
> 
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> #source("˜/Rpackages/netresponse/netresponse/R/detect.responses.R")
> #source("˜/Rpackages/netresponse/netresponse/R/internals.R")
> #source("˜/Rpackages/netresponse/netresponse/R/vdp.mixt.R")
> #dyn.load("/home/tuli/Rpackages/netresponse/netresponse/src/netresponse.so")
> 
> #########  Generate DATA #############################################
> 
> # Generate Nc components from normal-inverseGamma prior
> 
> set.seed(12346)
> 
> dd <- 3   # Dimensionality of data
> Nc <- 5   # Number of components
> Ns <- 200 # Number of data points
> sd0 <- 3  # component spread
> rgam.shape = 2 # parameters for Gamma distribution 
> rgam.scale = 2 # parameters for Gamma distribution to define precisions
> 
> 
> # Generate means and variances (covariance diagonals) for the components 
> component.means <- matrix(rnorm(Nc*dd, mean = 0, sd = sd0), nrow = Nc, ncol = dd)
> component.vars <- matrix(1/rgamma(Nc*dd, shape = rgam.shape, scale = rgam.scale), 
+ 	                 nrow = Nc, ncol = dd)
> component.sds <- sqrt(component.vars)
> 
> 
> # Size for each component -> sample randomly for each data point from uniform distr.
> # i.e. cluster assignments
> sample2comp <- sample.int(Nc, Ns, replace = TRUE)
> 
> D <- array(NA, dim = c(Ns, dd))
> for (i in 1:Ns)  {
+     # component identity of this sample
+     ci <- sample2comp[[i]]
+     cm <- component.means[ci,]
+     csd <- component.sds[ci,]
+     D[i,] <- rnorm(dd, mean = cm, sd = csd)
+ }
> 
> 
> ######################################################################
> 
> # Fit mixture model
> out <- mixture.model(D, mixture.method = "bic")
> 
> # FIXME rowmeans(qofz) is constant but not 1
> #qofz <- P.r.s(t(D), list(mu = out$mu, sd = out$sd, w = out$w), log = FALSE)
> 
> ############################################################
> 
> # Compare input data and results
> 
> ord.out <- order(out$mu[,1])
> ord.in <- order(component.means[,1])
> 
> means.out <- out$mu[ord.out,]
> means.in <- component.means[ord.in,]
> 
> # Cluster stds and variances
> sds.out <- out$sd[ord.out,]
> sds.in  <- sqrt(component.vars[ord.in,])
> 
> # -----------------------------------------------------------
> 
> vars.out <- sds.out^2
> vars.in <- sds.in^2
> 
> # Check correspondence between input and output
> if (length(means.in) == length(means.out)) {
+    cm <- cor(as.vector(means.in), as.vector(means.out))
+    csd <- cor(as.vector(sds.in), as.vector(sds.out))
+ }
> 
> # Plot results (assuming 2D)
> 
> ran <- range(c(as.vector(means.in - 2*vars.in), 
+                as.vector(means.in + 2*vars.in), 
+ 	       as.vector(means.out + 2*vars.out), 
+ 	       as.vector(means.out - 2*vars.out)))
> 
> plot(D, pch = 20, main = paste("Cor.means:", round(cm,3), "/ Cor.sds:", round(csd,3)), xlim = ran, ylim = ran) 
> for (ci in 1:nrow(means.out))  { add.ellipse(centroid = means.out[ci,], covmat = diag(vars.out[ci,]), col = "red") }
> for (ci in 1:nrow(means.in))  { add.ellipse(centroid = means.in[ci,], covmat = diag(vars.in[ci,]), col = "blue") }
> 
> ######################################################
> 
> #for (ci in 1:nrow(means.out))  {
> #    points(means.out[ci,1], means.out[ci,2], col = "red", pch = 19)
> #    el <- ellipse(matrix(c(vars.out[ci,1],0,0,vars.out[ci,2]),2), centre = means.out[ci,])
> #    lines(el, col = "red") 						  
> #}
> 
> #for (ci in 1:nrow(means.in))  {
> #    points(means.in[ci,1], means.in[ci,2], col = "blue", pch = 19)
> #    el <- ellipse(matrix(c(vars.in[ci,1],0,0,vars.in[ci,2]),2), centre = means.in[ci,])
> #    lines(el, col = "blue") 						  
> #}
> 
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   2.90    0.21    3.09 

netresponse.Rcheck/tests_x64/bicmixture.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> # 1. vdp.mixt: moodien loytyminen eri dimensiolla, naytemaarilla ja komponenteilla
> #   -> ainakin nopea check
> 
> #######################################################################
> 
> # Generate random data from five Gaussians. 
> # Detect modes with vdp-gm. 
> # Plot data points and detected clusters with variance ellipses
> 
> #######################################################################
> 
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> #source("˜/Rpackages/netresponse/netresponse/R/detect.responses.R")
> #source("˜/Rpackages/netresponse/netresponse/R/internals.R")
> #source("˜/Rpackages/netresponse/netresponse/R/vdp.mixt.R")
> #dyn.load("/home/tuli/Rpackages/netresponse/netresponse/src/netresponse.so")
> 
> #########  Generate DATA #############################################
> 
> # Generate Nc components from normal-inverseGamma prior
> 
> set.seed(12346)
> 
> dd <- 3   # Dimensionality of data
> Nc <- 5   # Number of components
> Ns <- 200 # Number of data points
> sd0 <- 3  # component spread
> rgam.shape = 2 # parameters for Gamma distribution 
> rgam.scale = 2 # parameters for Gamma distribution to define precisions
> 
> 
> # Generate means and variances (covariance diagonals) for the components 
> component.means <- matrix(rnorm(Nc*dd, mean = 0, sd = sd0), nrow = Nc, ncol = dd)
> component.vars <- matrix(1/rgamma(Nc*dd, shape = rgam.shape, scale = rgam.scale), 
+ 	                 nrow = Nc, ncol = dd)
> component.sds <- sqrt(component.vars)
> 
> 
> # Size for each component -> sample randomly for each data point from uniform distr.
> # i.e. cluster assignments
> sample2comp <- sample.int(Nc, Ns, replace = TRUE)
> 
> D <- array(NA, dim = c(Ns, dd))
> for (i in 1:Ns)  {
+     # component identity of this sample
+     ci <- sample2comp[[i]]
+     cm <- component.means[ci,]
+     csd <- component.sds[ci,]
+     D[i,] <- rnorm(dd, mean = cm, sd = csd)
+ }
> 
> 
> ######################################################################
> 
> # Fit mixture model
> out <- mixture.model(D, mixture.method = "bic")
> 
> # FIXME rowmeans(qofz) is constant but not 1
> #qofz <- P.r.s(t(D), list(mu = out$mu, sd = out$sd, w = out$w), log = FALSE)
> 
> ############################################################
> 
> # Compare input data and results
> 
> ord.out <- order(out$mu[,1])
> ord.in <- order(component.means[,1])
> 
> means.out <- out$mu[ord.out,]
> means.in <- component.means[ord.in,]
> 
> # Cluster stds and variances
> sds.out <- out$sd[ord.out,]
> sds.in  <- sqrt(component.vars[ord.in,])
> 
> # -----------------------------------------------------------
> 
> vars.out <- sds.out^2
> vars.in <- sds.in^2
> 
> # Check correspondence between input and output
> if (length(means.in) == length(means.out)) {
+    cm <- cor(as.vector(means.in), as.vector(means.out))
+    csd <- cor(as.vector(sds.in), as.vector(sds.out))
+ }
> 
> # Plot results (assuming 2D)
> 
> ran <- range(c(as.vector(means.in - 2*vars.in), 
+                as.vector(means.in + 2*vars.in), 
+ 	       as.vector(means.out + 2*vars.out), 
+ 	       as.vector(means.out - 2*vars.out)))
> 
> plot(D, pch = 20, main = paste("Cor.means:", round(cm,3), "/ Cor.sds:", round(csd,3)), xlim = ran, ylim = ran) 
> for (ci in 1:nrow(means.out))  { add.ellipse(centroid = means.out[ci,], covmat = diag(vars.out[ci,]), col = "red") }
> for (ci in 1:nrow(means.in))  { add.ellipse(centroid = means.in[ci,], covmat = diag(vars.in[ci,]), col = "blue") }
> 
> ######################################################
> 
> #for (ci in 1:nrow(means.out))  {
> #    points(means.out[ci,1], means.out[ci,2], col = "red", pch = 19)
> #    el <- ellipse(matrix(c(vars.out[ci,1],0,0,vars.out[ci,2]),2), centre = means.out[ci,])
> #    lines(el, col = "red") 						  
> #}
> 
> #for (ci in 1:nrow(means.in))  {
> #    points(means.in[ci,1], means.in[ci,2], col = "blue", pch = 19)
> #    el <- ellipse(matrix(c(vars.in[ci,1],0,0,vars.in[ci,2]),2), centre = means.in[ci,])
> #    lines(el, col = "blue") 						  
> #}
> 
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   3.32    0.21    3.53 

netresponse.Rcheck/tests_i386/ICMg.test.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> # Test script for the ICMg method
> 
> # Load the package
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> data(osmo) # Load data
> 
> # Set parameters
> C.boost = 1
> alpha = 10
> beta = 0.01
> B.num = 10
> B.size = 10
> S.num = 10  
> S.size = 10
> C = 24
> pm0 = 0
> V0 = 1               
> V = 0.1
> 
> # Run combined ICMg sampler
> res = ICMg.combined.sampler(osmo$ppi, osmo$exp, C, alpha, beta, pm0, V0, V, B.num, B.size, S.num, S.size, C.boost) 
Sampling ICMg2...

nodes:10250links:1711observations:133components:24alpha:10beta:0.01

Sampling200iterationcs

Burnin iterations:100

I: 0

n(z):428423409412457415426422439403450384413462441428417459435430468415385429

m(z):816584747873626979656880667266737470745875676672

I:10

convL:-0.479717908656148n(z):4274292014332213793596003753452185267238336312294302276276278383461470403

convN:-0.0117711166301349m(z):516417565247979352561857089526850285446779043162112

I:20

convL:-0.406763710315724n(z):3514652404362143823026283223861833314238320277247268190193296510644781413

convN:-0.00322514240194744m(z):50601851514310984545113385109537155295351788748180108

I:30

convL:-0.389780632565301n(z):3044392733952333753006333794281727364235296299239281173173240585717803359

convN:-0.00229485826437178m(z):50601851514310984545113285109537155295351788748181108

I:40

convL:-0.365378593141548n(z):3044592604062443752886973225021695362273271297190261163191227597709814343

convN:-0.00308859428192431m(z):50541851514310985545513487109547155295251788748178108

I:50

convL:-0.359183900385927n(z):2704192724182353743036673485411757374312248313184239173166200645733769290

convN:-0.0041348498360967m(z):50551849514310989545113486111537155295351788748178108

I:60

convL:-0.344605245317887n(z):3034062854102313592685922815931784362333238338179239172158180682765786306

convN:-0.00661535743116072m(z):50551848514310890545113286111537254295252778848181108

I:70

convL:-0.339078056881019n(z):2754093023802833762955602665301845384347231286163211173165179725787806272

convN:-0.00259344384824696m(z):50551848514310990565113386111537155295351798748179105

I:80

convL:-0.336332710738166n(z):2764402653952613872995302745641837386353215258174208182172175704759853283

convN:-0.00173073221252916m(z):50551848514310890565113286111566955295351778848181105

I:90

convL:-0.336633169144404n(z):2384282723812573762895033095601875365364258303184183178168169721691902276

convN:-0.00201111017183236m(z):50551848514310889565113286112577055295251778748181105

I:100

convL:-0.322991537402716n(z):2414252593852693642875093265641887363363274295188200183161163713705859267

convN:-Infm(z):51551848514310889565113386112576955295351778748179105

Sample iterations:100

I:110

convL:-0.330477874570725n(z):2634292563572923802815382826101884324388244269185179184157190713731868246

convN:-0.00355515946143753m(z):50551850514310989565113586112576955295350778748176105

I:120

convL:-0.333714052523403n(z):2484432623652673672835392876311870332358244270193177191150169688717903296

convN:-0.00257048251549619m(z):50551848514310890565113587111566955295350778948178104

I:130

convL:-0.326060924247017n(z):2474282903632623542635533286291821354376211299178186204148191653742880290

convN:-0.00610920779468357m(z):50541848514310989565013787111567055295251778948177104

I:140

convL:-0.339958144061511n(z):2924092813762473672874812946201833371386257289172184180156192676773865262

convN:-0.00293351506623642m(z):50551849514310889565013387112577055295251778848179104

I:150

convL:-0.328002575021972n(z):2874272773612423752864952985981836354400234298185176173157197708758856272

convN:-0.00337633320961183m(z):50551848514310889565013387112577055295251778848180104

I:160

convL:-0.333115779884941n(z):2854322693552573552855003156381858372381266323177150171158195655705902246

convN:-0.00172190771297211m(z):50551848514310889565013387112577055295251778748180105

I:170

convL:-0.316770574187423n(z):2904202633612373652564752896021905351429220324201157158158205702725873284

convN:-0.00145932449500817m(z):50551848514310890565013187112577055295251778848181104

I:180

convL:-0.33443895149239n(z):2854012493602243612694862806011918404388218308223163171170181743738835274

convN:-0.00275308647133559m(z):50551848514310889565013287112567055295251788848181104

I:190

convL:-0.314346400944757n(z):2943912523622153562674602946411894399412190293203175183165181729726898270

convN:-0.00347474884735854m(z):50541848514310889565113386112576955295350778748182105

I:200

convL:-0.319898323112704n(z):3114072613752513502594312956001918449364233293192178187164194683723852280

convN:-0.00371309078773136m(z):50541848514310890565113487111577055295251778748179105

DONE

> 
> # Compute component membership probabilities for the data points
> res$comp.memb <- ICMg.get.comp.memberships(osmo$ppi, res)
> 
> # Compute (hard) clustering for nodes
> res$clustering <- apply(res$comp.memb, 2, which.max)
> 
> proc.time()
   user  system elapsed 
   8.28    0.20    8.46 

netresponse.Rcheck/tests_x64/ICMg.test.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> # Test script for the ICMg method
> 
> # Load the package
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> data(osmo) # Load data
> 
> # Set parameters
> C.boost = 1
> alpha = 10
> beta = 0.01
> B.num = 10
> B.size = 10
> S.num = 10  
> S.size = 10
> C = 24
> pm0 = 0
> V0 = 1               
> V = 0.1
> 
> # Run combined ICMg sampler
> res = ICMg.combined.sampler(osmo$ppi, osmo$exp, C, alpha, beta, pm0, V0, V, B.num, B.size, S.num, S.size, C.boost) 
Sampling ICMg2...

nodes:10250links:1711observations:133components:24alpha:10beta:0.01

Sampling200iterationcs

Burnin iterations:100

I: 0

n(z):425411440422451429459455401465433410428452408423393407430442408416432410

m(z):856469805670756365684176708078617386867663796681

I:10

convL:-0.51961830426294n(z):1964121983891550245972591523283533389225247466337326230370383351347338349

convN:-0.00598026027401599m(z):451076773146351009662939380373855895547597375617649

I:20

convL:-0.415523877877975n(z):2163872283811614196988889482299388449222252453312324198390363346322263288

convN:-0.0087144922532075m(z):46866678145359811157919182394958895555637273477154

I:30

convL:-0.379350476960332n(z):19441924042115772101005992564308399483270249400274330186368275324271270221

convN:-0.0036592544727111m(z):478166761503510511655938981385060875557637073407153

I:40

convL:-0.370677986010068n(z):180499204398154119510681282489309354440285242402245352142378222290274252207

convN:-0.00324183574496793m(z):478266761503510512360919181385159805555587174446653

I:50

convL:-0.362620199677817n(z):175530214378154720010331410479297324436268259382267348150386211280239247190

convN:-0.00433564901491265m(z):477866711503511012560929182385459805552597173456751

I:60

convL:-0.351865485692638n(z):165534214346161321410041396454301305479272275388262357146383211263256242170

convN:-0.00209075684111098m(z):477966711493511012560919181395559805553587174446751

I:70

convL:-0.352577189513199n(z):16252522135116052359981468444283318436297234355262376146363208286265264148

convN:-0.00295280290168015m(z):477966711513511012560919181385459805552597174446751

I:80

convL:-0.351884102075756n(z):16953123834416312119631437438283271499307233350272343131392211281271278166

convN:-0.00336196593039861m(z):477966711503510912560938882395460825552587173446751

I:90

convL:-0.333345780691125n(z):17556923230816002259551516482286259534310227348288350141384183224249257148

convN:-0.00404606571143788m(z):477866711533510512560928884385460825552597174446751

I:100

convL:-0.343704824570221n(z):18154522233616042519711499476293241584300212319293329140400208213252227154

convN:-0.00211776764947067m(z):477966711543510412560928983395459825553587173446751

Sample iterations:100

I:110

convL:-0.350984385232619n(z):19655422933615872359371556457291229571335216317276324132393197228242262150

convN:-0.00153963557683921m(z):477966711543510412560928883385460825553597173446751

I:120

convL:-0.346266343921852n(z):15758522933215742469211559442308253621325245299295345132382186185243230156

convN:-0.00458013591165896m(z):477766711553510412460928884395560825553587173446751

I:130

convL:-0.31989196357795n(z):16765723833515712469071555456281258608311236263253346150397178207238233159

convN:-0.00318674968754439m(z):477966711563510412360928883395460825553587173446751

I:140

convL:-0.32277350326581n(z):13762322932815562399031576464294238609325252293278366137401176203232228163

convN:-0.0026845625517416m(z):477966711573510112460928884385460825553597173446751

I:150

convL:-0.332532148577022n(z):15662922831015682359401567456274266589310237283286377132386183217238238145

convN:-0.00148193001216185m(z):477966711583510012560928983395459825553587173446751

I:160

convL:-0.332053291234978n(z):13760623331216002099381570430282264599330263300275338147389191215224244154

convN:-0.00183466394534414m(z):47796672158359912560928983385459825553597173446751

I:170

convL:-0.34385256359576n(z):13261122831816072108871588441265251631312281273271342141392223207242269128

convN:-0.00227314073978873m(z):477966711563510112660928983395458825553597173446751

I:180

convL:-0.335850728069077n(z):13964025031215752199631560417287256644291253263291325143381188172246294141

convN:-0.00145624610002483m(z):477966711563510012660928984395458825553597173446751

I:190

convL:-0.326389431267356n(z):14461722733215561889481643410280239608310273265297300163387201177258282145

convN:-0.00243844599937679m(z):46796671158349912660928883395459825553607273446751

I:200

convL:-0.325372542729545n(z):13061122933015002099631679414277252619290252272293301149391220188251286144

convN:-0.00115320799415236m(z):477966711563510112660928983385459825553617173446551

DONE

> 
> # Compute component membership probabilities for the data points
> res$comp.memb <- ICMg.get.comp.memberships(osmo$ppi, res)
> 
> # Compute (hard) clustering for nodes
> res$clustering <- apply(res$comp.memb, 2, which.max)
> 
> proc.time()
   user  system elapsed 
   7.35    0.20    7.54 

netresponse.Rcheck/tests_i386/mixture.model.test.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> # Validate mixture models
> 
> # Generate random data from five Gaussians. 
> # Detect modes 
> # Plot data points and detected clusters 
> 
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> #fs <- list.files("˜/Rpackages/netresponse/netresponse/R/", full.names = TRUE); for (f in fs) {source(f)}; dyn.load("/home/tuli/Rpackages/netresponse/netresponse/src/netresponse.so")
> 
> #########  Generate DATA #######################
> 
> res <- generate.toydata()
> D <- res$data
> component.means <- res$means
> component.sds   <- res$sds
> sample2comp     <- res$sample2comp
> 
> ######################################################################
> 
> par(mfrow = c(2,1))
> 
> for (mm in c("vdp", "bic")) {
+ 
+   # Fit nonparametric Gaussian mixture model
+   #source("˜/Rpackages/netresponse/netresponse/R/vdp.mixt.R")
+   out <- mixture.model(D, mixture.method = mm, max.responses = 10, pca.basis = FALSE)
+ 
+   ############################################################
+ 
+   # Compare input data and results
+ 
+   ord.out <- order(out$mu[,1])
+   ord.in <- order(component.means[,1])
+ 
+   means.out <- out$mu[ord.out,]
+   means.in <- component.means[ord.in,]
+ 
+   # Cluster stds and variances
+   sds.out <- out$sd[ord.out,]
+   vars.out <- sds.out^2
+ 
+   sds.in  <- component.sds[ord.in,]
+   vars.in <- sds.in^2
+ 
+   # Check correspondence between input and output
+   if (length(means.in) == length(means.out)) {
+     cm <- cor(as.vector(means.in), as.vector(means.out))
+     csd <- cor(as.vector(sds.in), as.vector(sds.out))
+   }
+ 
+   # Plot results (assuming 2D)
+   ran <- range(c(as.vector(means.in - 2*vars.in), 
+                as.vector(means.in + 2*vars.in), 
+ 	       as.vector(means.out + 2*vars.out), 
+ 	       as.vector(means.out - 2*vars.out)))
+ 
+   real.modes <- sample2comp
+   obs.modes <- apply(out$qofz, 1, which.max)
+ 
+   # plot(D, pch = 20, main = paste(mm, "/ cor.means:", round(cm,6), "/ Cor.sds:", round(csd,6)), xlim = ran, ylim = ran) 
+   plot(D, pch = real.modes, col = obs.modes, main = paste(mm, "/ cor.means:", round(cm,6), "/ Cor.sds:", round(csd,6)), xlim = ran, ylim = ran) 
+   for (ci in 1:nrow(means.out))  { add.ellipse(centroid = means.out[ci,], covmat = diag(vars.out[ci,]), col = "red") }
+   for (ci in 1:nrow(means.in))  { add.ellipse(centroid = means.in[ci,], covmat = diag(vars.in[ci,]), col = "blue") }
+ 
+ }
> 
> 
> proc.time()
   user  system elapsed 
   2.39    0.23    2.61 

netresponse.Rcheck/tests_x64/mixture.model.test.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> # Validate mixture models
> 
> # Generate random data from five Gaussians. 
> # Detect modes 
> # Plot data points and detected clusters 
> 
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> #fs <- list.files("˜/Rpackages/netresponse/netresponse/R/", full.names = TRUE); for (f in fs) {source(f)}; dyn.load("/home/tuli/Rpackages/netresponse/netresponse/src/netresponse.so")
> 
> #########  Generate DATA #######################
> 
> res <- generate.toydata()
> D <- res$data
> component.means <- res$means
> component.sds   <- res$sds
> sample2comp     <- res$sample2comp
> 
> ######################################################################
> 
> par(mfrow = c(2,1))
> 
> for (mm in c("vdp", "bic")) {
+ 
+   # Fit nonparametric Gaussian mixture model
+   #source("˜/Rpackages/netresponse/netresponse/R/vdp.mixt.R")
+   out <- mixture.model(D, mixture.method = mm, max.responses = 10, pca.basis = FALSE)
+ 
+   ############################################################
+ 
+   # Compare input data and results
+ 
+   ord.out <- order(out$mu[,1])
+   ord.in <- order(component.means[,1])
+ 
+   means.out <- out$mu[ord.out,]
+   means.in <- component.means[ord.in,]
+ 
+   # Cluster stds and variances
+   sds.out <- out$sd[ord.out,]
+   vars.out <- sds.out^2
+ 
+   sds.in  <- component.sds[ord.in,]
+   vars.in <- sds.in^2
+ 
+   # Check correspondence between input and output
+   if (length(means.in) == length(means.out)) {
+     cm <- cor(as.vector(means.in), as.vector(means.out))
+     csd <- cor(as.vector(sds.in), as.vector(sds.out))
+   }
+ 
+   # Plot results (assuming 2D)
+   ran <- range(c(as.vector(means.in - 2*vars.in), 
+                as.vector(means.in + 2*vars.in), 
+ 	       as.vector(means.out + 2*vars.out), 
+ 	       as.vector(means.out - 2*vars.out)))
+ 
+   real.modes <- sample2comp
+   obs.modes <- apply(out$qofz, 1, which.max)
+ 
+   # plot(D, pch = 20, main = paste(mm, "/ cor.means:", round(cm,6), "/ Cor.sds:", round(csd,6)), xlim = ran, ylim = ran) 
+   plot(D, pch = real.modes, col = obs.modes, main = paste(mm, "/ cor.means:", round(cm,6), "/ Cor.sds:", round(csd,6)), xlim = ran, ylim = ran) 
+   for (ci in 1:nrow(means.out))  { add.ellipse(centroid = means.out[ci,], covmat = diag(vars.out[ci,]), col = "red") }
+   for (ci in 1:nrow(means.in))  { add.ellipse(centroid = means.in[ci,], covmat = diag(vars.in[ci,]), col = "blue") }
+ 
+ }
> 
> 
> proc.time()
   user  system elapsed 
   2.60    0.15    2.76 

netresponse.Rcheck/tests_i386/mixture.model.test.multimodal.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> # Three MODES
> 
> # set.seed(34884)
> set.seed(3488400)
> 
> Ns <- 200
> Nd <- 2
> 
> D3 <- rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd), 
+       	    matrix(rnorm(Ns*Nd, mean = 3), ncol = Nd),
+       	    cbind(rnorm(Ns, mean = -3), rnorm(Ns, mean = 3))
+ 	    )
> 
> #X11()
> par(mfrow = c(2,2))
> for (mm in c("vdp", "bic")) {
+   for (pp in c(FALSE, TRUE)) {
+ 
+     # Fit nonparametric Gaussian mixture model
+     out <- mixture.model(D3, mixture.method = mm, pca.basis = pp)
+     plot(D3, col = apply(out$qofz, 1, which.max), main = paste(mm, "/ pca:",  pp)) 
+ 
+   }
+ }
> 
> # VDP is less sensitive than BIC in detecting Gaussian modes (more
> # separation between the clusters needed)
> 
> # pca.basis option is less important for sensitive detection but
> # it will help to avoid overfitting to unimodal features that
> # are not parallel to the axes (unimodal distribution often becomes
> # splitted in two or more clusters in these cases)
> 
> 
> proc.time()
   user  system elapsed 
   6.62    0.32    6.92 

netresponse.Rcheck/tests_x64/mixture.model.test.multimodal.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> # Three MODES
> 
> # set.seed(34884)
> set.seed(3488400)
> 
> Ns <- 200
> Nd <- 2
> 
> D3 <- rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd), 
+       	    matrix(rnorm(Ns*Nd, mean = 3), ncol = Nd),
+       	    cbind(rnorm(Ns, mean = -3), rnorm(Ns, mean = 3))
+ 	    )
> 
> #X11()
> par(mfrow = c(2,2))
> for (mm in c("vdp", "bic")) {
+   for (pp in c(FALSE, TRUE)) {
+ 
+     # Fit nonparametric Gaussian mixture model
+     out <- mixture.model(D3, mixture.method = mm, pca.basis = pp)
+     plot(D3, col = apply(out$qofz, 1, which.max), main = paste(mm, "/ pca:",  pp)) 
+ 
+   }
+ }
> 
> # VDP is less sensitive than BIC in detecting Gaussian modes (more
> # separation between the clusters needed)
> 
> # pca.basis option is less important for sensitive detection but
> # it will help to avoid overfitting to unimodal features that
> # are not parallel to the axes (unimodal distribution often becomes
> # splitted in two or more clusters in these cases)
> 
> 
> proc.time()
   user  system elapsed 
   4.29    0.18    4.46 

netresponse.Rcheck/tests_i386/mixture.model.test.singlemode.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> 
> skip <- FALSE
> 
> if (!skip) {
+ 
+ library(netresponse)
+ 
+ # SINGLE MODE
+ 
+ # Produce test data that has full covariance
+ # It is expected that
+ # pca.basis = FALSE splits Gaussian with full covariance into two modes
+ # pca.basis = TRUE should detect just a single mode
+ 
+ Ns <- 200
+ Nd <- 2
+ k <- 1.5
+ 
+ D2 <- matrix(rnorm(Ns*Nd), ncol = Nd) %*% rbind(c(1,k), c(k,1))
+ 
+ par(mfrow = c(2,2))
+ for (mm in c("vdp", "bic")) {
+   for (pp in c(FALSE, TRUE)) {
+ 
+     # Fit nonparametric Gaussian mixture model
+     out <- mixture.model(D2, mixture.method = mm, pca.basis = pp)
+     plot(D2, col = apply(out$qofz, 1, which.max), main = paste("mm:" , mm, "/ pp:",  pp)) 
+ 
+   }
+ }
+ 
+ }
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> proc.time()
   user  system elapsed 
   2.42    0.20    2.61 

netresponse.Rcheck/tests_x64/mixture.model.test.singlemode.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> 
> skip <- FALSE
> 
> if (!skip) {
+ 
+ library(netresponse)
+ 
+ # SINGLE MODE
+ 
+ # Produce test data that has full covariance
+ # It is expected that
+ # pca.basis = FALSE splits Gaussian with full covariance into two modes
+ # pca.basis = TRUE should detect just a single mode
+ 
+ Ns <- 200
+ Nd <- 2
+ k <- 1.5
+ 
+ D2 <- matrix(rnorm(Ns*Nd), ncol = Nd) %*% rbind(c(1,k), c(k,1))
+ 
+ par(mfrow = c(2,2))
+ for (mm in c("vdp", "bic")) {
+   for (pp in c(FALSE, TRUE)) {
+ 
+     # Fit nonparametric Gaussian mixture model
+     out <- mixture.model(D2, mixture.method = mm, pca.basis = pp)
+     plot(D2, col = apply(out$qofz, 1, which.max), main = paste("mm:" , mm, "/ pp:",  pp)) 
+ 
+   }
+ }
+ 
+ }
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> proc.time()
   user  system elapsed 
   2.60    0.14    2.75 

netresponse.Rcheck/tests_i386/timing.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> 
> # Play with different options and check their effect on  running times for bic and vdp 
> 
> skip <- TRUE
> 
> if (!skip) {
+ 
+   Ns <- 100
+   Nd <- 2
+ 
+   set.seed(3488400)
+ 
+   D <- cbind(
+ 
+      	rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd), 
+        	      matrix(rnorm(Ns*Nd, mean = 2), ncol = Nd),
+       	      cbind(rnorm(Ns, mean = -1), rnorm(Ns, mean = 3))
+  	    ), 
+ 
+      	rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd), 
+        	      matrix(rnorm(Ns*Nd, mean = 2), ncol = Nd),
+       	      cbind(rnorm(Ns, mean = -1), rnorm(Ns, mean = 3))
+  	    )
+ 	    )
+ 
+   rownames(D) <- paste("R", 1:nrow(D), sep = "-")
+   colnames(D) <- paste("C", 1:ncol(D), sep = "-")
+ 
+   ts <- c()
+   for (mm in c("bic", "vdp")) {
+ 
+ 
+     # NOTE: no PCA basis needed with mixture.method = "bic"
+     tt <- system.time(detect.responses(D, verbose = TRUE, max.responses = 5, 
+ 	   		       mixture.method = mm, information.criterion = "BIC", 
+ 			       merging.threshold = 0, bic.threshold = 0, pca.basis = TRUE))
+ 
+     print(paste(mm, ":", round(tt[["elapsed"]], 3)))
+     ts[[mm]] <- tt[["elapsed"]]
+   }
+ 
+    print(paste(names(ts)[[1]], "/", names(ts)[[2]], ": ", round(ts[[1]]/ts[[2]], 3)))
+ 
+ }
> 
> # -> VDP is much faster when sample sizes increase 
> # 1000 samples -> 25-fold speedup with VDP
> 
> 
> 
> proc.time()
   user  system elapsed 
   0.15    0.03    0.17 

netresponse.Rcheck/tests_x64/timing.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> 
> # Play with different options and check their effect on  running times for bic and vdp 
> 
> skip <- TRUE
> 
> if (!skip) {
+ 
+   Ns <- 100
+   Nd <- 2
+ 
+   set.seed(3488400)
+ 
+   D <- cbind(
+ 
+      	rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd), 
+        	      matrix(rnorm(Ns*Nd, mean = 2), ncol = Nd),
+       	      cbind(rnorm(Ns, mean = -1), rnorm(Ns, mean = 3))
+  	    ), 
+ 
+      	rbind(matrix(rnorm(Ns*Nd, mean = 0), ncol = Nd), 
+        	      matrix(rnorm(Ns*Nd, mean = 2), ncol = Nd),
+       	      cbind(rnorm(Ns, mean = -1), rnorm(Ns, mean = 3))
+  	    )
+ 	    )
+ 
+   rownames(D) <- paste("R", 1:nrow(D), sep = "-")
+   colnames(D) <- paste("C", 1:ncol(D), sep = "-")
+ 
+   ts <- c()
+   for (mm in c("bic", "vdp")) {
+ 
+ 
+     # NOTE: no PCA basis needed with mixture.method = "bic"
+     tt <- system.time(detect.responses(D, verbose = TRUE, max.responses = 5, 
+ 	   		       mixture.method = mm, information.criterion = "BIC", 
+ 			       merging.threshold = 0, bic.threshold = 0, pca.basis = TRUE))
+ 
+     print(paste(mm, ":", round(tt[["elapsed"]], 3)))
+     ts[[mm]] <- tt[["elapsed"]]
+   }
+ 
+    print(paste(names(ts)[[1]], "/", names(ts)[[2]], ": ", round(ts[[1]]/ts[[2]], 3)))
+ 
+ }
> 
> # -> VDP is much faster when sample sizes increase 
> # 1000 samples -> 25-fold speedup with VDP
> 
> 
> 
> proc.time()
   user  system elapsed 
   0.12    0.06    0.17 

netresponse.Rcheck/tests_i386/toydata2.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> # Generate Nc components from normal-inverseGamma prior
> 
> set.seed(12346)
> 
> Ns <- 300
> Nd <- 2
> 
> # Isotropic cloud
> D1 <- matrix(rnorm(Ns*Nd), ncol = Nd) 
> 
> # Single diagonal mode
> D2 <- matrix(rnorm(Ns*Nd), ncol = Nd) %*% rbind(c(1,2), c(2,1)) 
> 
> # Two isotropic modes
> D3 <- rbind(matrix(rnorm(Ns/2*Nd), ncol = Nd), matrix(rnorm(Ns/2*Nd, mean = 3), ncol = Nd))
> D <- cbind(D1, D2, D3)
> 
> colnames(D) <- paste("Feature-",  1:ncol(D), sep = "")
> rownames(D) <- paste("Sample-", 1:nrow(D), sep = "")
> 
> 
> proc.time()
   user  system elapsed 
   0.17    0.00    0.15 

netresponse.Rcheck/tests_x64/toydata2.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> # Generate Nc components from normal-inverseGamma prior
> 
> set.seed(12346)
> 
> Ns <- 300
> Nd <- 2
> 
> # Isotropic cloud
> D1 <- matrix(rnorm(Ns*Nd), ncol = Nd) 
> 
> # Single diagonal mode
> D2 <- matrix(rnorm(Ns*Nd), ncol = Nd) %*% rbind(c(1,2), c(2,1)) 
> 
> # Two isotropic modes
> D3 <- rbind(matrix(rnorm(Ns/2*Nd), ncol = Nd), matrix(rnorm(Ns/2*Nd, mean = 3), ncol = Nd))
> D <- cbind(D1, D2, D3)
> 
> colnames(D) <- paste("Feature-",  1:ncol(D), sep = "")
> rownames(D) <- paste("Sample-", 1:nrow(D), sep = "")
> 
> 
> proc.time()
   user  system elapsed 
   0.18    0.01    0.18 

netresponse.Rcheck/tests_i386/validate.netresponse.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> 
> skip <- FALSE
> 
> if (!skip) {
+ 
+ # 2. netresponse test
+ # test later with varying parameters
+ 
+ # Load the package
+ library(netresponse)
+ #load("../data/toydata.rda")
+ fs <- list.files("../R/", full.names = TRUE); for (f in fs) {source(f)};
+ 
+ data(toydata)
+ 
+ D <- toydata$emat
+ netw <- toydata$netw
+ 
+ # The toy data is random data with 10 features (genes). 
+ # The features 
+ rf <- c(4, 5, 6)
+ #form a subnetwork with coherent responses
+ # with means 
+ r1 <- c(0, 3, 0)
+ r2 <- c(-5, 0, 2)
+ r3 <- c(5, -3, -3)
+ mu.real <- rbind(r1, r2, r3)
+ # real weights
+ w.real <- c(70, 70, 60)/200
+ # and unit variances
+ rv <- 1
+ 
+ # Fit the model
+ #res <- detect.responses(D, netw, verbose = TRUE, mc.cores = 2)
+ #res <- detect.responses(D, netw, verbose = TRUE, max.responses = 4)
+ 
+ res <- detect.responses(D, netw, verbose = TRUE, max.responses = 3, mixture.method = "bic", information.criterion = "BIC", merging.threshold = 1, bic.threshold = 10, pca.basis = FALSE)
+ 
+ print("OK")
+ 
+ # Subnets (each is a list of nodes)
+ subnets <- get.subnets(res)
+ 
+ # the correct subnet is retrieved in subnet number 2:
+ #> subnet[[2]]
+ #[1] "feat4" "feat5" "feat6"
+ 
+ # how about responses
+ # Retrieve model for the subnetwork with lowest cost function value
+ # means, standard devations and weights for the components
+ if (!is.null(subnets)) {
+ m <- get.model.parameters(res, subnet.id = "Subnet-2")
+ 
+ # order retrieved and real response means by the first feature 
+ # (to ensure responses are listed in the same order)
+ # and compare deviation from correct solution
+ ord.obs <- order(m$mu[,1])
+ ord.real <- order(mu.real[,1])
+ 
+ print(paste("Correlation between real and observed responses:", cor(as.vector(m$mu[ord.obs,]), as.vector(mu.real[ord.real,]))))
+ 
+ # all real variances are 1, compare to observed ones
+ print(paste("Maximum deviation from real variances: ", max(abs(rv - range(m$sd))/rv)))
+ 
+ # weights deviate somewhat, this is likely due to relatively small sample size
+ #print("Maximum deviation from real weights: ")
+ #print( (w.real[ord.real] - m$w[ord.obs])/w.real[ord.real])
+ 
+ print("estimated and real mean matrices")
+ print(m$mu[ord.obs,])
+ print(mu.real[ord.real,])
+ 
+ }
+ 
+ }
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
convert the network into edge matrix
removing self-links
matching the features between network and datamatrix
Filter the network to only keep the edges with highest mutual information
1 / 8
2 / 8
3 / 8
4 / 8
5 / 8
6 / 8
7 / 8
8 / 8
Compute cost for each variable
Computing model for node 1 / 10
Computing model for node 2 / 10
Computing model for node 3 / 10
Computing model for node 4 / 10
Computing model for node 5 / 10
Computing model for node 6 / 10
Computing model for node 7 / 10
Computing model for node 8 / 10
Computing model for node 9 / 10
Computing model for node 10 / 10
independent models done
Computing delta values for edge  1 / 29 

Computing delta values for edge  2 / 29 

Computing delta values for edge  3 / 29 

Computing delta values for edge  4 / 29 

Computing delta values for edge  5 / 29 

Computing delta values for edge  6 / 29 

Computing delta values for edge  7 / 29 

Computing delta values for edge  8 / 29 

Computing delta values for edge  9 / 29 

Computing delta values for edge  10 / 29 

Computing delta values for edge  11 / 29 

Computing delta values for edge  12 / 29 

Computing delta values for edge  13 / 29 

Computing delta values for edge  14 / 29 

Computing delta values for edge  15 / 29 

Computing delta values for edge  16 / 29 

Computing delta values for edge  17 / 29 

Computing delta values for edge  18 / 29 

Computing delta values for edge  19 / 29 

Computing delta values for edge  20 / 29 

Computing delta values for edge  21 / 29 

Computing delta values for edge  22 / 29 

Computing delta values for edge  23 / 29 

Computing delta values for edge  24 / 29 

Computing delta values for edge  25 / 29 

Computing delta values for edge  26 / 29 

Computing delta values for edge  27 / 29 

Computing delta values for edge  28 / 29 

Computing delta values for edge  29 / 29 

Combining groups,  10  group(s) left...

Combining groups,  9  group(s) left...

Combining groups,  8  group(s) left...

Combining groups,  7  group(s) left...

Combining groups,  6  group(s) left...

Combining groups,  5  group(s) left...

Combining groups,  4  group(s) left...

[1] "OK"
[1] "Correlation between real and observed responses: 0.999117848017521"
[1] "Maximum deviation from real variances:  0.0391530538149302"
[1] "estimated and real mean matrices"
           [,1]       [,2]       [,3]
[1,] -4.9334982 -0.1575946  2.1613225
[2,] -0.1299285  3.0047767 -0.1841669
[3,]  5.0738471 -2.9334877 -3.2217492
   [,1] [,2] [,3]
r2   -5    0    2
r1    0    3    0
r3    5   -3   -3
> 
> proc.time()
   user  system elapsed 
  42.56    0.20   42.76 

netresponse.Rcheck/tests_x64/validate.netresponse.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> 
> skip <- FALSE
> 
> if (!skip) {
+ 
+ # 2. netresponse test
+ # test later with varying parameters
+ 
+ # Load the package
+ library(netresponse)
+ #load("../data/toydata.rda")
+ fs <- list.files("../R/", full.names = TRUE); for (f in fs) {source(f)};
+ 
+ data(toydata)
+ 
+ D <- toydata$emat
+ netw <- toydata$netw
+ 
+ # The toy data is random data with 10 features (genes). 
+ # The features 
+ rf <- c(4, 5, 6)
+ #form a subnetwork with coherent responses
+ # with means 
+ r1 <- c(0, 3, 0)
+ r2 <- c(-5, 0, 2)
+ r3 <- c(5, -3, -3)
+ mu.real <- rbind(r1, r2, r3)
+ # real weights
+ w.real <- c(70, 70, 60)/200
+ # and unit variances
+ rv <- 1
+ 
+ # Fit the model
+ #res <- detect.responses(D, netw, verbose = TRUE, mc.cores = 2)
+ #res <- detect.responses(D, netw, verbose = TRUE, max.responses = 4)
+ 
+ res <- detect.responses(D, netw, verbose = TRUE, max.responses = 3, mixture.method = "bic", information.criterion = "BIC", merging.threshold = 1, bic.threshold = 10, pca.basis = FALSE)
+ 
+ print("OK")
+ 
+ # Subnets (each is a list of nodes)
+ subnets <- get.subnets(res)
+ 
+ # the correct subnet is retrieved in subnet number 2:
+ #> subnet[[2]]
+ #[1] "feat4" "feat5" "feat6"
+ 
+ # how about responses
+ # Retrieve model for the subnetwork with lowest cost function value
+ # means, standard devations and weights for the components
+ if (!is.null(subnets)) {
+ m <- get.model.parameters(res, subnet.id = "Subnet-2")
+ 
+ # order retrieved and real response means by the first feature 
+ # (to ensure responses are listed in the same order)
+ # and compare deviation from correct solution
+ ord.obs <- order(m$mu[,1])
+ ord.real <- order(mu.real[,1])
+ 
+ print(paste("Correlation between real and observed responses:", cor(as.vector(m$mu[ord.obs,]), as.vector(mu.real[ord.real,]))))
+ 
+ # all real variances are 1, compare to observed ones
+ print(paste("Maximum deviation from real variances: ", max(abs(rv - range(m$sd))/rv)))
+ 
+ # weights deviate somewhat, this is likely due to relatively small sample size
+ #print("Maximum deviation from real weights: ")
+ #print( (w.real[ord.real] - m$w[ord.obs])/w.real[ord.real])
+ 
+ print("estimated and real mean matrices")
+ print(m$mu[ord.obs,])
+ print(mu.real[ord.real,])
+ 
+ }
+ 
+ }
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
convert the network into edge matrix
removing self-links
matching the features between network and datamatrix
Filter the network to only keep the edges with highest mutual information
1 / 8
2 / 8
3 / 8
4 / 8
5 / 8
6 / 8
7 / 8
8 / 8
Compute cost for each variable
Computing model for node 1 / 10
Computing model for node 2 / 10
Computing model for node 3 / 10
Computing model for node 4 / 10
Computing model for node 5 / 10
Computing model for node 6 / 10
Computing model for node 7 / 10
Computing model for node 8 / 10
Computing model for node 9 / 10
Computing model for node 10 / 10
independent models done
Computing delta values for edge  1 / 29 

Computing delta values for edge  2 / 29 

Computing delta values for edge  3 / 29 

Computing delta values for edge  4 / 29 

Computing delta values for edge  5 / 29 

Computing delta values for edge  6 / 29 

Computing delta values for edge  7 / 29 

Computing delta values for edge  8 / 29 

Computing delta values for edge  9 / 29 

Computing delta values for edge  10 / 29 

Computing delta values for edge  11 / 29 

Computing delta values for edge  12 / 29 

Computing delta values for edge  13 / 29 

Computing delta values for edge  14 / 29 

Computing delta values for edge  15 / 29 

Computing delta values for edge  16 / 29 

Computing delta values for edge  17 / 29 

Computing delta values for edge  18 / 29 

Computing delta values for edge  19 / 29 

Computing delta values for edge  20 / 29 

Computing delta values for edge  21 / 29 

Computing delta values for edge  22 / 29 

Computing delta values for edge  23 / 29 

Computing delta values for edge  24 / 29 

Computing delta values for edge  25 / 29 

Computing delta values for edge  26 / 29 

Computing delta values for edge  27 / 29 

Computing delta values for edge  28 / 29 

Computing delta values for edge  29 / 29 

Combining groups,  10  group(s) left...

Combining groups,  9  group(s) left...

Combining groups,  8  group(s) left...

Combining groups,  7  group(s) left...

Combining groups,  6  group(s) left...

Combining groups,  5  group(s) left...

Combining groups,  4  group(s) left...

[1] "OK"
[1] "Correlation between real and observed responses: 0.999117848017521"
[1] "Maximum deviation from real variances:  0.0391530538149302"
[1] "estimated and real mean matrices"
           [,1]       [,2]       [,3]
[1,] -4.9334982 -0.1575946  2.1613225
[2,] -0.1299285  3.0047767 -0.1841669
[3,]  5.0738471 -2.9334877 -3.2217492
   [,1] [,2] [,3]
r2   -5    0    2
r1    0    3    0
r3    5   -3   -3
> 
> proc.time()
   user  system elapsed 
  40.48    0.10   40.59 

netresponse.Rcheck/tests_i386/validate.pca.basis.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> 
> skip <- FALSE
> 
> if (!skip) {
+ # Visualization
+ 
+ library(netresponse)
+ 
+ #fs <- list.files("˜/Rpackages/netresponse/netresponse/R/", full.names = T); for (f in fs) {source(f)}
+ 
+ source("toydata2.R")
+ 
+ # --------------------------------------------------------------------
+ 
+ set.seed(4243)
+ mixture.method <- "bic"
+ 
+ # --------------------------------------------------------------------
+ 
+ res <- detect.responses(D, verbose = TRUE, max.responses = 10, 
+ 	   		       mixture.method = mixture.method, information.criterion = "BIC", 
+ 			       merging.threshold = 1, bic.threshold = 10, pca.basis = FALSE)
+ 
+ res.pca <- detect.responses(D, verbose = TRUE, max.responses = 10, mixture.method = mixture.method, information.criterion = "BIC", merging.threshold = 1, bic.threshold = 10, pca.basis = TRUE)
+ 
+ # --------------------------------------------------------------------
+ 
+ k <- 1
+ 
+ # Incorrect VDP: two modes detected
+ # Correct BIC: single mode detected
+ subnet.id <- names(get.subnets(res))[[k]]
+ 
+ # Correct: single mode detected (VDP & BIC)
+ subnet.id.pca <- names(get.subnets(res.pca))[[k]]
+ 
+ # --------------------------------------------------------------------------------------------------
+ 
+ vis1 <- plot_responses(res, subnet.id, plot_mode = "pca", main = paste("NoPCA; NoDM"))
+ vis2 <- plot_responses(res, subnet.id, plot_mode = "pca", datamatrix = D, main = "NoPCA, DM")
+ vis3 <- plot_responses(res.pca, subnet.id.pca, plot_mode = "pca", main = "PCA, NoDM")
+ vis4 <- plot_responses(res.pca, subnet.id.pca, plot_mode = "pca", datamatrix = D, main = "PCA, DM")
+ 
+ # With original data: VDP overlearns; BIC works; with full covariance data 
+ # With PCA basis: modes detected ok with both VDP and BIC.
+ 
+ # ------------------------------------------------------------------------
+ 
+ # TODO
+ # pca.plot(res, subnet.id)
+ # plot_subnet(res, subnet.id) 
+ }
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
convert the network into edge matrix
removing self-links
matching the features between network and datamatrix
Filter the network to only keep the edges with highest mutual information
1 / 5
2 / 5
3 / 5
4 / 5
5 / 5
Compute cost for each variable
Computing model for node 1 / 6
Computing model for node 2 / 6
Computing model for node 3 / 6
Computing model for node 4 / 6
Computing model for node 5 / 6
Computing model for node 6 / 6
independent models done
Computing delta values for edge  1 / 15 

Computing delta values for edge  2 / 15 

Computing delta values for edge  3 / 15 

Computing delta values for edge  4 / 15 

Computing delta values for edge  5 / 15 

Computing delta values for edge  6 / 15 

Computing delta values for edge  7 / 15 

Computing delta values for edge  8 / 15 

Computing delta values for edge  9 / 15 

Computing delta values for edge  10 / 15 

Computing delta values for edge  11 / 15 

Computing delta values for edge  12 / 15 

Computing delta values for edge  13 / 15 

Computing delta values for edge  14 / 15 

Computing delta values for edge  15 / 15 

Combining groups,  6  group(s) left...

Combining groups,  5  group(s) left...

Combining groups,  4  group(s) left...

Combining groups,  3  group(s) left...

convert the network into edge matrix
removing self-links
matching the features between network and datamatrix
Filter the network to only keep the edges with highest mutual information
1 / 5
2 / 5
3 / 5
4 / 5
5 / 5
Compute cost for each variable
Computing model for node 1 / 6
Computing model for node 2 / 6
Computing model for node 3 / 6
Computing model for node 4 / 6
Computing model for node 5 / 6
Computing model for node 6 / 6
independent models done
Computing delta values for edge  1 / 15 

Computing delta values for edge  2 / 15 

Computing delta values for edge  3 / 15 

Computing delta values for edge  4 / 15 

Computing delta values for edge  5 / 15 

Computing delta values for edge  6 / 15 

Computing delta values for edge  7 / 15 

Computing delta values for edge  8 / 15 

Computing delta values for edge  9 / 15 

Computing delta values for edge  10 / 15 

Computing delta values for edge  11 / 15 

Computing delta values for edge  12 / 15 

Computing delta values for edge  13 / 15 

Computing delta values for edge  14 / 15 

Computing delta values for edge  15 / 15 

Combining groups,  6  group(s) left...

Combining groups,  5  group(s) left...

Combining groups,  4  group(s) left...

Combining groups,  3  group(s) left...

Warning messages:
1: In check.network(network, datamatrix, verbose = verbose) :
  No network provided in function call: assuming fully connected nodes.
2: In check.network(network, datamatrix, verbose = verbose) :
  No network provided in function call: assuming fully connected nodes.
> 
> proc.time()
   user  system elapsed 
  28.64    0.28   28.90 

netresponse.Rcheck/tests_x64/validate.pca.basis.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> 
> skip <- FALSE
> 
> if (!skip) {
+ # Visualization
+ 
+ library(netresponse)
+ 
+ #fs <- list.files("˜/Rpackages/netresponse/netresponse/R/", full.names = T); for (f in fs) {source(f)}
+ 
+ source("toydata2.R")
+ 
+ # --------------------------------------------------------------------
+ 
+ set.seed(4243)
+ mixture.method <- "bic"
+ 
+ # --------------------------------------------------------------------
+ 
+ res <- detect.responses(D, verbose = TRUE, max.responses = 10, 
+ 	   		       mixture.method = mixture.method, information.criterion = "BIC", 
+ 			       merging.threshold = 1, bic.threshold = 10, pca.basis = FALSE)
+ 
+ res.pca <- detect.responses(D, verbose = TRUE, max.responses = 10, mixture.method = mixture.method, information.criterion = "BIC", merging.threshold = 1, bic.threshold = 10, pca.basis = TRUE)
+ 
+ # --------------------------------------------------------------------
+ 
+ k <- 1
+ 
+ # Incorrect VDP: two modes detected
+ # Correct BIC: single mode detected
+ subnet.id <- names(get.subnets(res))[[k]]
+ 
+ # Correct: single mode detected (VDP & BIC)
+ subnet.id.pca <- names(get.subnets(res.pca))[[k]]
+ 
+ # --------------------------------------------------------------------------------------------------
+ 
+ vis1 <- plot_responses(res, subnet.id, plot_mode = "pca", main = paste("NoPCA; NoDM"))
+ vis2 <- plot_responses(res, subnet.id, plot_mode = "pca", datamatrix = D, main = "NoPCA, DM")
+ vis3 <- plot_responses(res.pca, subnet.id.pca, plot_mode = "pca", main = "PCA, NoDM")
+ vis4 <- plot_responses(res.pca, subnet.id.pca, plot_mode = "pca", datamatrix = D, main = "PCA, DM")
+ 
+ # With original data: VDP overlearns; BIC works; with full covariance data 
+ # With PCA basis: modes detected ok with both VDP and BIC.
+ 
+ # ------------------------------------------------------------------------
+ 
+ # TODO
+ # pca.plot(res, subnet.id)
+ # plot_subnet(res, subnet.id) 
+ }
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
convert the network into edge matrix
removing self-links
matching the features between network and datamatrix
Filter the network to only keep the edges with highest mutual information
1 / 5
2 / 5
3 / 5
4 / 5
5 / 5
Compute cost for each variable
Computing model for node 1 / 6
Computing model for node 2 / 6
Computing model for node 3 / 6
Computing model for node 4 / 6
Computing model for node 5 / 6
Computing model for node 6 / 6
independent models done
Computing delta values for edge  1 / 15 

Computing delta values for edge  2 / 15 

Computing delta values for edge  3 / 15 

Computing delta values for edge  4 / 15 

Computing delta values for edge  5 / 15 

Computing delta values for edge  6 / 15 

Computing delta values for edge  7 / 15 

Computing delta values for edge  8 / 15 

Computing delta values for edge  9 / 15 

Computing delta values for edge  10 / 15 

Computing delta values for edge  11 / 15 

Computing delta values for edge  12 / 15 

Computing delta values for edge  13 / 15 

Computing delta values for edge  14 / 15 

Computing delta values for edge  15 / 15 

Combining groups,  6  group(s) left...

Combining groups,  5  group(s) left...

Combining groups,  4  group(s) left...

Combining groups,  3  group(s) left...

convert the network into edge matrix
removing self-links
matching the features between network and datamatrix
Filter the network to only keep the edges with highest mutual information
1 / 5
2 / 5
3 / 5
4 / 5
5 / 5
Compute cost for each variable
Computing model for node 1 / 6
Computing model for node 2 / 6
Computing model for node 3 / 6
Computing model for node 4 / 6
Computing model for node 5 / 6
Computing model for node 6 / 6
independent models done
Computing delta values for edge  1 / 15 

Computing delta values for edge  2 / 15 

Computing delta values for edge  3 / 15 

Computing delta values for edge  4 / 15 

Computing delta values for edge  5 / 15 

Computing delta values for edge  6 / 15 

Computing delta values for edge  7 / 15 

Computing delta values for edge  8 / 15 

Computing delta values for edge  9 / 15 

Computing delta values for edge  10 / 15 

Computing delta values for edge  11 / 15 

Computing delta values for edge  12 / 15 

Computing delta values for edge  13 / 15 

Computing delta values for edge  14 / 15 

Computing delta values for edge  15 / 15 

Combining groups,  6  group(s) left...

Combining groups,  5  group(s) left...

Combining groups,  4  group(s) left...

Combining groups,  3  group(s) left...

Warning messages:
1: In check.network(network, datamatrix, verbose = verbose) :
  No network provided in function call: assuming fully connected nodes.
2: In check.network(network, datamatrix, verbose = verbose) :
  No network provided in function call: assuming fully connected nodes.
> 
> proc.time()
   user  system elapsed 
  23.64    0.25   23.89 

netresponse.Rcheck/tests_i386/vdpmixture.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

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

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

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

> 
> # 1. vdp.mixt: moodien loytyminen eri dimensiolla, naytemaarilla ja komponenteilla
> #   -> ainakin nopea check
> 
> #######################################################################
> 
> # Generate random data from five Gaussians. 
> # Detect modes with vdp-gm. 
> # Plot data points and detected clusters with variance ellipses
> 
> #######################################################################
> 
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> #source("˜/Rpackages/netresponse/netresponse/R/detect.responses.R")
> #source("˜/Rpackages/netresponse/netresponse/R/internals.R")
> #source("˜/Rpackages/netresponse/netresponse/R/vdp.mixt.R")
> #dyn.load("/home/tuli/Rpackages/netresponse/netresponse/src/netresponse.so")
> 
> 
> #########  Generate DATA #############################################
> 
> res <- generate.toydata()
> D <- res$data
> component.means <- res$means
> component.sds   <- res$sds
> sample2comp     <- res$sample2comp
> 
> ######################################################################
> 
> # Fit nonparametric Gaussian mixture model
> out <- vdp.mixt(D)
> # out <- vdp.mixt(D, c.max = 3) # try with limited number of components -> OK
> 
> ############################################################
> 
> # Compare input data and results
> 
> ord.out <- order(out$posterior$centroids[,1])
> ord.in <- order(component.means[,1])
> 
> means.out <- out$posterior$centroids[ord.out,]
> means.in <- component.means[ord.in,]
> 
> # Cluster stds and variances
> sds.out <- out$posterior$sds[ord.out,]
> sds.in  <- component.sds[ord.in,]
> vars.out <- sds.out^2
> vars.in <- sds.in^2
> 
> # Check correspondence between input and output
> if (length(means.in) == length(means.out)) {
+    cm <- cor(as.vector(means.in), as.vector(means.out))
+    csd <- cor(as.vector(sds.in), as.vector(sds.out))
+ }
> 
> # Plot results (assuming 2D)
> 
> ran <- range(c(as.vector(means.in - 2*vars.in), 
+                as.vector(means.in + 2*vars.in), 
+ 	       as.vector(means.out + 2*vars.out), 
+ 	       as.vector(means.out - 2*vars.out)))
> 
> plot(D, pch = 20, main = paste("Cor.means:", round(cm,3), "/ Cor.sds:", round(csd,3)), xlim = ran, ylim = ran) 
> for (ci in 1:nrow(means.out))  { add.ellipse(centroid = means.out[ci,], covmat = diag(vars.out[ci,]), col = "red") }
> for (ci in 1:nrow(means.in))  { add.ellipse(centroid = means.in[ci,], covmat = diag(vars.in[ci,]), col = "blue") }
> 
> 
> 
> proc.time()
   user  system elapsed 
   2.31    0.07    2.37 

netresponse.Rcheck/tests_x64/vdpmixture.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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

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

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

> 
> # 1. vdp.mixt: moodien loytyminen eri dimensiolla, naytemaarilla ja komponenteilla
> #   -> ainakin nopea check
> 
> #######################################################################
> 
> # Generate random data from five Gaussians. 
> # Detect modes with vdp-gm. 
> # Plot data points and detected clusters with variance ellipses
> 
> #######################################################################
> 
> library(netresponse)
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

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

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

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

Loading required package: grid
Loading required package: minet
Loading required package: mclust
Package 'mclust' version 5.4.3
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2016 Leo Lahti et al.

https://github.com/antagomir/netresponse
> #source("˜/Rpackages/netresponse/netresponse/R/detect.responses.R")
> #source("˜/Rpackages/netresponse/netresponse/R/internals.R")
> #source("˜/Rpackages/netresponse/netresponse/R/vdp.mixt.R")
> #dyn.load("/home/tuli/Rpackages/netresponse/netresponse/src/netresponse.so")
> 
> 
> #########  Generate DATA #############################################
> 
> res <- generate.toydata()
> D <- res$data
> component.means <- res$means
> component.sds   <- res$sds
> sample2comp     <- res$sample2comp
> 
> ######################################################################
> 
> # Fit nonparametric Gaussian mixture model
> out <- vdp.mixt(D)
> # out <- vdp.mixt(D, c.max = 3) # try with limited number of components -> OK
> 
> ############################################################
> 
> # Compare input data and results
> 
> ord.out <- order(out$posterior$centroids[,1])
> ord.in <- order(component.means[,1])
> 
> means.out <- out$posterior$centroids[ord.out,]
> means.in <- component.means[ord.in,]
> 
> # Cluster stds and variances
> sds.out <- out$posterior$sds[ord.out,]
> sds.in  <- component.sds[ord.in,]
> vars.out <- sds.out^2
> vars.in <- sds.in^2
> 
> # Check correspondence between input and output
> if (length(means.in) == length(means.out)) {
+    cm <- cor(as.vector(means.in), as.vector(means.out))
+    csd <- cor(as.vector(sds.in), as.vector(sds.out))
+ }
> 
> # Plot results (assuming 2D)
> 
> ran <- range(c(as.vector(means.in - 2*vars.in), 
+                as.vector(means.in + 2*vars.in), 
+ 	       as.vector(means.out + 2*vars.out), 
+ 	       as.vector(means.out - 2*vars.out)))
> 
> plot(D, pch = 20, main = paste("Cor.means:", round(cm,3), "/ Cor.sds:", round(csd,3)), xlim = ran, ylim = ran) 
> for (ci in 1:nrow(means.out))  { add.ellipse(centroid = means.out[ci,], covmat = diag(vars.out[ci,]), col = "red") }
> for (ci in 1:nrow(means.in))  { add.ellipse(centroid = means.in[ci,], covmat = diag(vars.in[ci,]), col = "blue") }
> 
> 
> 
> proc.time()
   user  system elapsed 
   2.14    0.21    2.34 

Example timings

netresponse.Rcheck/examples_i386/netresponse-Ex.timings

nameusersystemelapsed
ICMg.combined.sampler36.08 0.0436.13
ICMg.links.sampler1.420.021.44
NetResponseModel-class000
PlotMixture000
PlotMixtureBivariate000
PlotMixtureMultivariate000
PlotMixtureMultivariate.deprecated000
PlotMixtureUnivariate000
add.ellipse000
centerData000
check.matrix000
check.network000
detect.responses0.000.010.02
dna0.040.000.03
enrichment.list.factor000
enrichment.list.factor.minimal000
filter.netw000
filter.network000
find.similar.features0.20.10.3
generate.toydata000
get.dat-NetResponseModel-method000
get.mis000
get.model.parameters0.000.010.01
get.subnets-NetResponseModel-method000
getqofz-NetResponseModel-method000
independent.models000
list.significant.responses000
listify.groupings000
model.stats0.010.000.02
netresponse-package2.610.032.64
order.responses000
osmo0.070.000.06
pick.model.pairs000
pick.model.parameters000
plotPCA000
plot_associations000
plot_data000
plot_expression000
plot_matrix0.010.000.02
plot_response000
plot_responses000
plot_scale000
plot_subnet000
read.sif000
remove.negative.edges000
response.enrichment000
response2sample0.020.000.01
sample2response000
set.breaks000
toydata000
update.model.pair000
vdp.mixt0.040.000.05
vectorize.groupings000
write.netresponse.results000

netresponse.Rcheck/examples_x64/netresponse-Ex.timings

nameusersystemelapsed
ICMg.combined.sampler40.56 0.0140.58
ICMg.links.sampler1.190.021.20
NetResponseModel-class000
PlotMixture000
PlotMixtureBivariate000
PlotMixtureMultivariate000
PlotMixtureMultivariate.deprecated000
PlotMixtureUnivariate000
add.ellipse000
centerData000
check.matrix000
check.network000
detect.responses000
dna0.030.020.05
enrichment.list.factor000
enrichment.list.factor.minimal000
filter.netw000
filter.network000
find.similar.features0.280.030.31
generate.toydata000
get.dat-NetResponseModel-method000
get.mis000
get.model.parameters000
get.subnets-NetResponseModel-method000
getqofz-NetResponseModel-method000
independent.models000
list.significant.responses000
listify.groupings000
model.stats000
netresponse-package3.280.033.31
order.responses000
osmo0.050.000.05
pick.model.pairs000
pick.model.parameters000
plotPCA000
plot_associations000
plot_data000
plot_expression000
plot_matrix0.010.000.01
plot_response000
plot_responses000
plot_scale000
plot_subnet000
read.sif000
remove.negative.edges000
response.enrichment000
response2sample000
sample2response000
set.breaks000
toydata0.000.010.02
update.model.pair000
vdp.mixt0.040.000.03
vectorize.groupings000
write.netresponse.results000