##############################################################################
##############################################################################
###
### Running command:
###
###   E:\biocbuild\bbs-3.21-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:netresponse.install-out.txt --library=E:\biocbuild\bbs-3.21-bioc\R\library --no-vignettes --timings netresponse_1.67.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'E:/biocbuild/bbs-3.21-bioc/meat/netresponse.Rcheck'
* using R Under development (unstable) (2024-10-26 r87273 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
    gcc.exe (GCC) 13.2.0
    GNU Fortran (GCC) 13.2.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* using option '--no-vignettes'
* checking for file 'netresponse/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'netresponse' version '1.67.0'
* checking package namespace information ... OK
* checking package dependencies ... INFO
Depends: includes the non-default packages:
  'BiocStyle', 'Rgraphviz', 'rmarkdown', 'minet', 'mclust', 'reshape2'
Adding so many packages to the search path is excessive and importing
selectively is preferable.
* 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
* used C compiler: 'gcc.exe (GCC) 13.3.0'
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' 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 code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* 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 whether startup messages can be suppressed ... 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 ... NOTE
checkRd: (-1) vdp.mixt.Rd:88: Lost braces; missing escapes or markup?
    88 |    'Nc' = \\sum_{n=1}^N q_{z_n} (z_n = c) (larger is better).
       |                 ^
checkRd: (-1) vdp.mixt.Rd:88: Lost braces; missing escapes or markup?
    88 |    'Nc' = \\sum_{n=1}^N q_{z_n} (z_n = c) (larger is better).
       |                           ^
checkRd: (-1) vdp.mixt.Rd:92: Lost braces; missing escapes or markup?
    92 |         q_{z_n}(z_n = c_1) and q_{z_n}(z_n = c_2). 
       |           ^
checkRd: (-1) vdp.mixt.Rd:92: Lost braces; missing escapes or markup?
    92 |         q_{z_n}(z_n = c_1) and q_{z_n}(z_n = c_2). 
       |                                  ^
checkRd: (-1) vdp.mixt.Rd:94: Lost braces
    94 |         belonged to c, and determine the new value for the free energy, F{T+1}.
       |                                                                          ^
checkRd: (-1) vdp.mixt.Rd:97: Lost braces
    97 | 4. Select the split that lead to the maximal reduction of free energy, F{T+1}.
       |                                                                         ^
checkRd: (-1) vdp.mixt.Rd:99: Lost braces
    99 | 6. If FT - F{T+1} < \\epsilon then halt, else set T := T +1 and go to step 2.
       |             ^
* 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 ... NOTE
Documented arguments not in \usage in Rd file 'generate.toydata.Rd':
  'Dim' 'Nc' 'Ns' 'sd0' 'rgam.shape' 'rgam.scale'
Assignments in \usage in Rd file 'generate.toydata.Rd':
  D <- generate.toydata()

Functions with \usage entries need to have the appropriate \alias
entries, and all their arguments documented.
The \usage entries must correspond to syntactically valid R code.
See chapter 'Writing R documentation files' in the 'Writing R
Extensions' manual.
* 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 use of PKG_*FLAGS in Makefiles ... OK
* checking compiled code ... NOTE
Note: information on .o files for x64 is not available
File 'E:/biocbuild/bbs-3.21-bioc/R/library/netresponse/libs/x64/netresponse.dll':
  Found '_exit', possibly from '_exit' (C)
  Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)

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 nor [v]sprintf. The detected symbols are linked into
the code but might come from libraries and not actually be called.

See 'Writing portable packages' in the 'Writing R Extensions' manual.
* checking files in 'vignettes' ... OK
* checking examples ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
  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 'vdpmixture.R'
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 3 NOTEs
See
  'E:/biocbuild/bbs-3.21-bioc/meat/netresponse.Rcheck/00check.log'
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   E:\biocbuild\bbs-3.21-bioc\R\bin\R.exe CMD INSTALL netresponse
###
##############################################################################
##############################################################################


* installing to library 'E:/biocbuild/bbs-3.21-bioc/R/library'
* installing *source* package 'netresponse' ...
** using staged installation
** libs
using C compiler: 'gcc.exe (GCC) 13.3.0'
gcc  -I"E:/biocbuild/bbs-3.21-bioc/R/include" -DNDEBUG     -I"C:/rtools44/x86_64-w64-mingw32.static.posix/include"      -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c netresponse.c -o netresponse.o
netresponse.c: In function 'mHPpost':
netresponse.c:264:15: warning: unused variable 'prior_fields' [-Wunused-variable]
  264 |   const char *prior_fields[]={"Mumu","S2mu",
      |               ^~~~~~~~~~~~
netresponse.c: In function 'mLogLambda':
netresponse.c:713:3: warning: 'Mumu' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:655:11: note: 'Mumu' was declared here
  655 |   double *Mumu, *S2mu, *Mubar, *Mutilde,
      |           ^~~~
netresponse.c:713:3: warning: 'S2mu' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:655:18: note: 'S2mu' was declared here
  655 |   double *Mumu, *S2mu, *Mubar, *Mutilde,
      |                  ^~~~
netresponse.c:713:3: warning: 'Mubar' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:655:25: note: 'Mubar' was declared here
  655 |   double *Mumu, *S2mu, *Mubar, *Mutilde,
      |                         ^~~~~
netresponse.c:713:3: warning: 'Mutilde' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:655:33: note: 'Mutilde' was declared here
  655 |   double *Mumu, *S2mu, *Mubar, *Mutilde,
      |                                 ^~~~~~~
netresponse.c:713:3: warning: 'AlphaKsi' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:656:6: note: 'AlphaKsi' was declared here
  656 |     *AlphaKsi, *BetaKsi, *KsiAlpha, *KsiBeta, *U_p, *prior_alpha,
      |      ^~~~~~~~
netresponse.c:713:3: warning: 'BetaKsi' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:656:17: note: 'BetaKsi' was declared here
  656 |     *AlphaKsi, *BetaKsi, *KsiAlpha, *KsiBeta, *U_p, *prior_alpha,
      |                 ^~~~~~~
netresponse.c:713:3: warning: 'KsiAlpha' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:656:27: note: 'KsiAlpha' was declared here
  656 |     *AlphaKsi, *BetaKsi, *KsiAlpha, *KsiBeta, *U_p, *prior_alpha,
      |                           ^~~~~~~~
netresponse.c:713:3: warning: 'KsiBeta' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:656:38: note: 'KsiBeta' was declared here
  656 |     *AlphaKsi, *BetaKsi, *KsiAlpha, *KsiBeta, *U_p, *prior_alpha,
      |                                      ^~~~~~~
netresponse.c:713:3: warning: 'U_p' may be used uninitialized [-Wmaybe-uninitialized]
  713 |   vdp_mk_log_lambda(Mumu, S2mu, Mubar, Mutilde,
      |   ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  714 |                     AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  715 |                     post_gamma, log_lambda, prior_alpha,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  716 |                     U_p, U_hat,
      |                     ~~~~~~~~~~~
  717 |                     datalen, dim1, dim2, data1, data2,
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  718 |                     Ns, ncentroids, implicitnoisevar);
      |                     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
netresponse.c:656:48: note: 'U_p' was declared here
  656 |     *AlphaKsi, *BetaKsi, *KsiAlpha, *KsiBeta, *U_p, *prior_alpha,
      |                                                ^~~
gcc -shared -s -static-libgcc -o netresponse.dll tmp.def netresponse.o -LC:/rtools44/x86_64-w64-mingw32.static.posix/lib/x64 -LC:/rtools44/x86_64-w64-mingw32.static.posix/lib -LE:/biocbuild/bbs-3.21-bioc/R/bin/x64 -lR
installing to E:/biocbuild/bbs-3.21-bioc/R/library/00LOCK-netresponse/00new/netresponse/libs/x64
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (netresponse)

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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: BiocStyle
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

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

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: grid
Loading required package: rmarkdown

Attaching package: 'rmarkdown'

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

    html_document, md_document, pdf_document

Loading required package: minet
Loading required package: mclust
Package 'mclust' version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2023 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 
   2.34    0.21    2.53 

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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: BiocStyle
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

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

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: grid
Loading required package: rmarkdown

Attaching package: 'rmarkdown'

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

    html_document, md_document, pdf_document

Loading required package: minet
Loading required package: mclust
Package 'mclust' version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2023 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 
   4.35    0.35    4.70 

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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: BiocStyle
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

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

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: grid
Loading required package: rmarkdown

Attaching package: 'rmarkdown'

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

    html_document, md_document, pdf_document

Loading required package: minet
Loading required package: mclust
Package 'mclust' version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2023 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 
   2.31    0.35    2.60 

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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: BiocStyle
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

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

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: grid
Loading required package: rmarkdown

Attaching package: 'rmarkdown'

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

    html_document, md_document, pdf_document

Loading required package: minet
Loading required package: mclust
Package 'mclust' version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2023 Leo Lahti et al.

https://github.com/antagomir/netresponse
> 
> proc.time()
   user  system elapsed 
   2.09    0.34    2.42 

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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 
   0.20    0.01    0.20 

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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 
   0.15    0.07    0.21 

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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 <- TRUE
> 
> 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,])
+ 
+ }
+ 
+ }
> 
> proc.time()
   user  system elapsed 
   0.15    0.07    0.20 

R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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: BiocStyle
Loading required package: Rgraphviz
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: generics

Attaching package: 'generics'

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

    as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
    setequal, union


Attaching package: 'BiocGenerics'

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, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
    mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
    rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
    unsplit, which.max, which.min

Loading required package: grid
Loading required package: rmarkdown

Attaching package: 'rmarkdown'

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

    html_document, md_document, pdf_document

Loading required package: minet
Loading required package: mclust
Package 'mclust' version 6.1.1
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: reshape2

netresponse (C) 2008-2023 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 
   2.00    0.34    2.29