Leo Lahti
| Snapshot Date: 2019-04-08 17:01:18 -0400 (Mon, 08 Apr 2019) |
| URL: https://git.bioconductor.org/packages/netresponse |
| Branch: master |
| Last Commit: d215282 |
| Last Changed Date: 2018-10-30 11:54:29 -0400 (Tue, 30 Oct 2018) |
| Back to Multiple platform build/check report for BioC 3.9 |
|
This page was generated on 2019-04-09 11:29:26 -0400 (Tue, 09 Apr 2019).
| Package 1079/1703 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| netresponse 1.43.0 Leo Lahti
| malbec2 | Linux (Ubuntu 18.04.2 LTS) / x86_64 | OK | OK | [ WARNINGS ] | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | WARNINGS | OK | | ||||||
| celaya2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | WARNINGS | OK | | ||||||
| merida2 | OS X 10.11.6 El Capitan / x86_64 | OK | OK | WARNINGS | OK |
| Package: netresponse |
| Version: 1.43.0 |
| Command: /home/biocbuild/bbs-3.9-bioc/R/bin/R CMD check --install=check:netresponse.install-out.txt --library=/home/biocbuild/bbs-3.9-bioc/R/library --no-vignettes --timings netresponse_1.43.0.tar.gz |
| StartedAt: 2019-04-09 02:21:26 -0400 (Tue, 09 Apr 2019) |
| EndedAt: 2019-04-09 02:25:19 -0400 (Tue, 09 Apr 2019) |
| EllapsedTime: 233.2 seconds |
| RetCode: 0 |
| Status: WARNINGS |
| CheckDir: netresponse.Rcheck |
| Warnings: 1 |
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### Running command:
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### /home/biocbuild/bbs-3.9-bioc/R/bin/R CMD check --install=check:netresponse.install-out.txt --library=/home/biocbuild/bbs-3.9-bioc/R/library --no-vignettes --timings netresponse_1.43.0.tar.gz
###
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* using log directory ‘/home/biocbuild/bbs-3.9-bioc/meat/netresponse.Rcheck’
* using R Under development (unstable) (2019-03-18 r76245)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘netresponse/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘netresponse’ version ‘1.43.0’
* checking package namespace information ... OK
* checking package dependencies ... 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 for sufficient/correct file permissions ... 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
* 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/netresponse.so’:
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 ... OK
Examples with CPU or elapsed time > 5s
user system elapsed
ICMg.combined.sampler 39.158 0.016 39.782
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
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
‘/home/biocbuild/bbs-3.9-bioc/meat/netresponse.Rcheck/00check.log’
for details.
netresponse.Rcheck/00install.out
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### Running command:
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### /home/biocbuild/bbs-3.9-bioc/R/bin/R CMD INSTALL netresponse
###
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* installing to library ‘/home/biocbuild/bbs-3.9-bioc/R/library’
* installing *source* package ‘netresponse’ ...
** using staged installation
** libs
gcc -I"/home/biocbuild/bbs-3.9-bioc/R/include" -DNDEBUG -I/usr/local/include -fpic -g -O2 -Wall -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,
^˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜
AlphaKsi, BetaKsi, KsiAlpha, KsiBeta,
˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜
post_gamma, log_lambda, prior_alpha,
˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜
U_p, U_hat,
˜˜˜˜˜˜˜˜˜˜˜
datalen, dim1, dim2, data1, data2,
˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜
Ns, ncentroids, implicitnoisevar);
˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜˜
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]
gcc -shared -L/home/biocbuild/bbs-3.9-bioc/R/lib -L/usr/local/lib -o netresponse.so netresponse.o -L/home/biocbuild/bbs-3.9-bioc/R/lib -lR
installing to /home/biocbuild/bbs-3.9-bioc/R/library/00LOCK-netresponse/00new/netresponse/libs
** 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
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (netresponse)
netresponse.Rcheck/tests/bicmixture.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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
3.268 0.133 3.389
netresponse.Rcheck/tests/ICMg.test.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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):457418418375449458391415489438455438385451421445446434394438409409432385
m(z):617375857970566471677266686567816675827783657469
I:10
convL:-0.481020234119369n(z):4222553857464862744123004273813914242192481203401391369395808400256215442
convN:-0.00637420635374049m(z):5778861231114680298472575258671215644501138598813132
I:20
convL:-0.401304062841962n(z):3912113948656052424542573133574343612402191293365340287325926315279224553
convN:-0.0032413021324677m(z):5972821221114671277665655460621215551551139297873038
I:30
convL:-0.398283242637119n(z):3721854299316742364032903373084903082121701279366237273342958330296216608
convN:-0.00774736469731015m(z):6073821231074371287762655359611235555551129596872940
I:40
convL:-0.370686334215612n(z):368180404100383320439930630824148433422917211773662302703071028312242233620
convN:-0.0038482842244959m(z):6173831271054372267858665358631245356551129198853041
I:50
convL:-0.362511894730847n(z):369186403122787617437529726322150432821714310703612292513681005294255229605
convN:-0.00205471670478933m(z):65738113110541682778497652566312454555511292101852939
I:60
convL:-0.343656559293857n(z):382223394129590417136128927619952035020414910353552452613731010293214214533
convN:-0.00438606371843158m(z):65738113010641682678497651576312353555511293101853040
I:70
convL:-0.34473226893627n(z):389222354127393517338721029521854731519113310403552302583511075298259204538
convN:-0.00188535493746081m(z):65738113110541682778497652566312454555511292101852939
I:80
convL:-0.330016165507284n(z):35822334613249181954311952752245122822081349853442462733801075291299207525
convN:-0.00205751439816276m(z):65738113010644682678497652566312353555511291100853040
I:90
convL:-0.332890563910103n(z):34925935713059402204521902712134943151871589913452182643561088273307203495
convN:-0.00214660129993363m(z):65738113010544682778497652566212454555511391100852939
I:100
convL:-0.325577855177096n(z):37026134413919392034351802871944983042011829103592162623411118252311204488
convN:-0.00214152817217054m(z):65738113010444682678497652566212554555511391100852940
Sample iterations:100
I:110
convL:-0.342601796626788n(z):37825834013799412094332052822044703051811989173642202653371107274300207476
convN:-0.00624566472447871m(z):65738113010443682678497652566312554555511291101852940
I:120
convL:-0.338868265519796n(z):40026634413979821744271872841964682771831968593662402703521133260303202484
convN:-0.00323537416559513m(z):65738113010544682877507551566212454555511392100852939
I:130
convL:-0.327021719984756n(z):41328935014249751683911803131745032781701908283612312453581108268286223524
convN:-0.00207217995765944m(z):65738113010544692678487650576212354555511392101852940
I:140
convL:-0.330350888168451n(z):40927837314769691793681733091625002511762038183612212573651094265307209527
convN:-0.00327006819758574m(z):64738113010544702778477551566212354555511394101852939
I:150
convL:-0.315206937753241n(z):43430837314399461724311732761944792641711868473532082793501091261313212490
convN:-0.00453712263590436m(z):61738113010647692678497551566212254555511394102842939
I:160
convL:-0.302581144590889n(z):447284369139010061844421502831914792691661708303672122883291160250306195483
convN:-0.00578502288790427m(z):61738313010548692678497650566212154555511394103822939
I:170
convL:-0.325838913591242n(z):42630034413939991664251672722014772681661838683632022893611148267291208466
convN:-0.00258587475089815m(z):61738313010747692678497650566211954555511394104822939
I:180
convL:-0.33016526605084n(z):396286383139710531514201732501954892831452018413522183003541099285310212457
convN:-0.00273545499986246m(z):61738213010747692678497650576211954555511395103822939
I:190
convL:-0.334844923124728n(z):401276365146910351673371622602024772651621878423732252963881156289294200422
convN:-0.00715731433745574m(z):60738313010648692777497650566212154555511394103822939
I:200
convL:-0.325738969948771n(z):389276368147210111363641652571774692651531748393482463214131171289301214432
convN:-0.0031308974285266m(z):61738113010547692778497650576312154555511394102822940
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
9.329 0.157 9.474
netresponse.Rcheck/tests/mixture.model.test.multimodal.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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
6.295 0.127 6.416
netresponse.Rcheck/tests/mixture.model.test.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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
3.729 0.136 3.852
netresponse.Rcheck/tests/mixture.model.test.singlemode.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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
3.482 0.143 3.609
netresponse.Rcheck/tests/timing.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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
0.231 0.028 0.246
netresponse.Rcheck/tests/toydata2.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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
0.248 0.024 0.260
netresponse.Rcheck/tests/validate.netresponse.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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
48.156 0.160 48.316
netresponse.Rcheck/tests/validate.pca.basis.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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
24.285 0.112 24.388
netresponse.Rcheck/tests/vdpmixture.Rout
R Under development (unstable) (2019-03-18 r76245) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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, 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
3.483 0.137 3.605
netresponse.Rcheck/netresponse-Ex.timings
| name | user | system | elapsed | |
| ICMg.combined.sampler | 39.158 | 0.016 | 39.782 | |
| ICMg.links.sampler | 1.799 | 0.004 | 1.803 | |
| NetResponseModel-class | 0.001 | 0.000 | 0.002 | |
| PlotMixture | 0 | 0 | 0 | |
| PlotMixtureBivariate | 0 | 0 | 0 | |
| PlotMixtureMultivariate | 0.001 | 0.000 | 0.000 | |
| PlotMixtureMultivariate.deprecated | 0 | 0 | 0 | |
| PlotMixtureUnivariate | 0 | 0 | 0 | |
| add.ellipse | 0.000 | 0.000 | 0.001 | |
| centerData | 0 | 0 | 0 | |
| check.matrix | 0 | 0 | 0 | |
| check.network | 0.001 | 0.000 | 0.000 | |
| detect.responses | 0.003 | 0.000 | 0.003 | |
| dna | 0.031 | 0.000 | 0.031 | |
| enrichment.list.factor | 0 | 0 | 0 | |
| enrichment.list.factor.minimal | 0 | 0 | 0 | |
| filter.netw | 0 | 0 | 0 | |
| filter.network | 0.001 | 0.000 | 0.000 | |
| find.similar.features | 0.420 | 0.044 | 0.463 | |
| generate.toydata | 0 | 0 | 0 | |
| get.dat-NetResponseModel-method | 0 | 0 | 0 | |
| get.mis | 0 | 0 | 0 | |
| get.model.parameters | 0.006 | 0.000 | 0.005 | |
| get.subnets-NetResponseModel-method | 0 | 0 | 0 | |
| getqofz-NetResponseModel-method | 0 | 0 | 0 | |
| independent.models | 0.001 | 0.000 | 0.000 | |
| list.significant.responses | 0 | 0 | 0 | |
| listify.groupings | 0 | 0 | 0 | |
| model.stats | 0.000 | 0.004 | 0.004 | |
| netresponse-package | 3.161 | 0.020 | 3.181 | |
| order.responses | 0 | 0 | 0 | |
| osmo | 0.036 | 0.000 | 0.035 | |
| pick.model.pairs | 0 | 0 | 0 | |
| pick.model.parameters | 0 | 0 | 0 | |
| plotPCA | 0 | 0 | 0 | |
| plot_associations | 0 | 0 | 0 | |
| plot_data | 0 | 0 | 0 | |
| plot_expression | 0 | 0 | 0 | |
| plot_matrix | 0.003 | 0.003 | 0.006 | |
| plot_response | 0.001 | 0.000 | 0.000 | |
| plot_responses | 0 | 0 | 0 | |
| plot_scale | 0 | 0 | 0 | |
| plot_subnet | 0 | 0 | 0 | |
| read.sif | 0 | 0 | 0 | |
| remove.negative.edges | 0 | 0 | 0 | |
| response.enrichment | 0 | 0 | 0 | |
| response2sample | 0.000 | 0.004 | 0.004 | |
| sample2response | 0 | 0 | 0 | |
| set.breaks | 0.001 | 0.000 | 0.000 | |
| toydata | 0.002 | 0.000 | 0.002 | |
| update.model.pair | 0 | 0 | 0 | |
| vdp.mixt | 0.032 | 0.000 | 0.031 | |
| vectorize.groupings | 0 | 0 | 0 | |
| write.netresponse.results | 0 | 0 | 0 | |