Back to Multiple platform build/check report for BioC 3.21: simplified long |
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This page was generated on 2024-12-24 11:45 -0500 (Tue, 24 Dec 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo1 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | R Under development (unstable) (2024-10-21 r87258) -- "Unsuffered Consequences" | 4754 |
palomino7 | Windows Server 2022 Datacenter | x64 | R Under development (unstable) (2024-10-26 r87273 ucrt) -- "Unsuffered Consequences" | 4472 |
lconway | macOS 12.7.1 Monterey | x86_64 | R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" | 4426 |
kjohnson3 | macOS 13.7.1 Ventura | arm64 | R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" | 4381 |
kunpeng2 | Linux (openEuler 22.03 LTS-SP1) | aarch64 | R Under development (unstable) (2024-11-24 r87369) -- "Unsuffered Consequences" | 4373 |
Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X |
Package 1114/2274 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
lpNet 2.39.0 (landing page) Lars Kaderali
| nebbiolo1 | Linux (Ubuntu 24.04.1 LTS) / x86_64 | OK | OK | WARNINGS | |||||||||
palomino7 | Windows Server 2022 Datacenter / x64 | OK | OK | WARNINGS | OK | |||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | WARNINGS | OK | |||||||||
kjohnson3 | macOS 13.7.1 Ventura / arm64 | OK | OK | WARNINGS | OK | |||||||||
kunpeng2 | Linux (openEuler 22.03 LTS-SP1) / aarch64 | OK | OK | WARNINGS | ||||||||||
To the developers/maintainers of the lpNet package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/lpNet.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
Package: lpNet |
Version: 2.39.0 |
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:lpNet.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings lpNet_2.39.0.tar.gz |
StartedAt: 2024-12-23 19:57:31 -0500 (Mon, 23 Dec 2024) |
EndedAt: 2024-12-23 19:57:49 -0500 (Mon, 23 Dec 2024) |
EllapsedTime: 18.4 seconds |
RetCode: 0 |
Status: WARNINGS |
CheckDir: lpNet.Rcheck |
Warnings: 1 |
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:lpNet.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings lpNet_2.39.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.21-bioc/meat/lpNet.Rcheck’ * using R Under development (unstable) (2024-11-20 r87352) * using platform: aarch64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 12.2.0 * running under: macOS Ventura 13.7.1 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘lpNet/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘lpNet’ version ‘2.39.0’ * 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 ‘lpNet’ can be installed ... OK * 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 dependencies in R code ... NOTE Package in Depends field not imported from: ‘KEGGgraph’ These packages need to be imported from (in the NAMESPACE file) for when this namespace is loaded but not attached. * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... NOTE .calcRangeLambda_steadyState: no visible global function definition for ‘var’ .calcRangeLambda_timeSeries: no visible global function definition for ‘var’ .calculatePredictionValue_Kfold_ts: no visible global function definition for ‘rnorm’ .calculatePredictionValue_LOOCV_ss: no visible global function definition for ‘rnorm’ .calculatePredictionValue_LOOCV_ts: no visible global function definition for ‘rnorm’ .set_per_gene_exp_time_values: no visible global function definition for ‘rnorm’ .set_per_gene_exp_values: no visible global function definition for ‘rnorm’ .set_per_gene_time_values: no visible global function definition for ‘rnorm’ .set_per_gene_values: no visible global function definition for ‘rnorm’ .set_single_values: no visible global function definition for ‘rnorm’ getSampleAdja: no visible binding for global variable ‘median’ getSampleAdjaMAD: no visible binding for global variable ‘median’ getSampleAdjaMAD: no visible binding for global variable ‘mad’ summarizeRepl: no visible binding for global variable ‘median’ Undefined global functions or variables: mad median rnorm var Consider adding importFrom("stats", "mad", "median", "rnorm", "var") to your NAMESPACE file. * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... WARNING Undocumented data sets: ‘dat.normalized’ ‘dat.unnormalized’ All user-level objects in a package should have documentation entries. See chapter ‘Writing R documentation files’ in the ‘Writing R Extensions’ manual. * 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 sizes of PDF files under ‘inst/doc’ ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘runitCalcActivation.R’ Running ‘runitCalcPredictionKfoldCV.R’ Running ‘runitCalcPredictionKfoldCV_timeSeries.R’ Running ‘runitCalcPredictionLOOCV.R’ Running ‘runitCalcPredictionLOOCV_timeSeries.R’ Running ‘runitCalcRangeLambda.R’ Running ‘runitDoILP.R’ Running ‘runitDoILP_timeSeries.R’ Running ‘runitGenerateTimeSeriesNetStates.R’ Running ‘runitGetAdja.R’ Running ‘runitGetBaseline.R’ Running ‘runitGetEdgeAnnot.R’ Running ‘runitGetObsMat.R’ Running ‘runitGetSampleAdja.R’ Running ‘runitGetSampleAdjaMAD.R’ Running ‘runitKfoldCV.R’ Running ‘runitKfoldCV_timeSeries.R’ Running ‘runitLOOCV.R’ Running ‘runitLOOCV_timeSeries.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: 1 WARNING, 2 NOTEs See ‘/Users/biocbuild/bbs-3.21-bioc/meat/lpNet.Rcheck/00check.log’ for details.
lpNet.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL lpNet ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library’ * installing *source* package ‘lpNet’ ... ** using staged installation ** 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 (lpNet)
lpNet.Rcheck/tests/runitCalcActivation.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.calcActivationShortExample <- function() { + n <- 3 + K <- 4 + + true_result <- matrix(c(0,0,0, + 1,0,0, + 1,1,0, + 1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationShortExampleTimeSeries <- function() { + n <- 3 + K <- 4 + + true_result <- matrix(c(0,0,0, + 1,0,0, + 1,1,0, + 1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > > test.calcActivation <- function() { + n <- 5 + K <- 6 + + true_result <- matrix(c(0,0,0,0,0, + 1,0,1,1,1, + 1,1,0,0,0, + 1,1,1,0,0, + 1,1,1,0,0, + 1,1,1,0,0), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,1,0,0, + 0,0,0,-1,0, + 0,0,0,1,0, + 0,0,0,0,1, + 0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,1, + 1,1,1,1,0, + 1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationTimeSeries <- function() { + n <- 5 + K <- 6 + + true_result <- matrix(c(0,0,0,0,0, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,0, + 1,1,1,1,0, + 1,1,1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,1,0,0, + 0,0,0,-1,0, + 0,0,0,1,0, + 0,0,0,0,1, + 0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1,1,1, + 1,0,1,1,1, + 1,1,0,1,1, + 1,1,1,0,1, + 1,1,1,1,0, + 1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationLargeExample <- function() { + n <- 10 + K <- 11 + + true_result <- matrix(c(0,0,0,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,1,1,0,0,0,0,0,0,0, + 1,1,1,1,0,0,0,0,0,0, + 1,1,1,1,1,0,0,0,0,0, + 1,0,0,1,1,1,0,0,0,0, + 1,0,0,1,1,1,1,0,0,0, + 1,0,0,1,1,1,1,1,0,0, + 1,0,0,1,1,1,1,1,1,0, + 1,0,0,1,1,1,1,1,1,1), nrow=n, ncol=K) + + T_nw <- matrix(c(0,1,0,0,0,0,0,0,0,0, + 0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,-1,0,0,0,0,1,0,0,0, + 0,0,0,0,0,0,0,1,0,0, + 0,0,0,0,0,0,0,0,1,0, + 0,0,0,0,0,0,1,0,0,1, + 0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + checkEquals(true_result, act_mat) + } > > > test.calcActivationLargeExampleTimeSeries <- function() { + n <- 10 + K <- 11 + + true_result <- matrix(c(0,1,1,1,1,1,1,1,1,1, + 1,0,0,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,0,0,0,0,0, + 1,1,1,1,0,0,0,0,0,0, + 1,1,1,1,1,0,0,0,0,0, + 1,1,1,1,1,1,0,0,0,0, + 1,1,1,1,1,1,1,0,0,0, + 1,1,1,1,1,1,1,1,0,0, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1), nrow = n, ncol=K) + + T_nw <- matrix(c(0,1,0,0,0,0,0,0,0,0, + 0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,-1,0,0,0,0,1,0,0,0, + 0,0,0,0,0,0,0,1,0,0, + 0,0,0,0,0,0,0,0,1,0, + 0,0,0,0,0,0,1,0,0,1, + 0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K, flag_gen_data=TRUE) + + checkEquals(true_result, act_mat) + } > > proc.time() user system elapsed 0.073 0.020 0.090
lpNet.Rcheck/tests/runitCalcPredictionKfoldCV.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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. > .setUp <- function() { + + n <<- 3 + K <<- 4 + + T_nw <<- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <<- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + } > > > test.runitCalcPredictionKfoldCV <- function() { + + obs_modified <- obs_mat + obs_modified[2,4] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionKfoldCV(obs, delta, b, n, K, adja=T_nw, baseline, rem_entries, rem_entries_vec, + active_mu, active_sd, inactive_mu, inactive_sd, mu_type=mu_type) + + checkEquals(obs_mat, predict, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.071 0.017 0.087
lpNet.Rcheck/tests/runitCalcPredictionKfoldCV_timeSeries.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + T_nw <<- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <<- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <<- array(NA, c(n,K,T_)) + + obs_mat[,,1] <<- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.95, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + mu_list[[4]] <<- list() + mu_list[[5]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, n*K*T_), c(n,K,T_)) + } > > > test.runitCalcPredictionKfoldCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,4,2], 0.56, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV02 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,4,2], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV03 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[3,4,3], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV04 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict[3,4,3])) + } + } > > > test.runitCalcPredictionKfoldCV05 <- function() { + + obs_modified <- obs_mat + obs_modified[2,2,2] <- NA + obs_modified[3,2,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,2], 0.56, tolerance=0.05) + checkEquals(predict[3,2,3], 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionKfoldCV06 <- function() { + + obs_modified <- obs_mat + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,1], 0.56, tolerance=0.05) + checkTrue(is.na(predict[3,2,2])) + } + } > > > test.runitCalcPredictionKfoldCV07 <- function() { + + baseline <- c(0.76, 0.76, 0.76) + + obs_modified <- obs_mat + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionKfoldCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_entries=rem_entries, rem_entries_vec=rem_entries_vec, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict[2,2,1], 0.56, tolerance=0.05) + checkEquals(predict[3,2,2], 0.95, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.082 0.027 0.108
lpNet.Rcheck/tests/runitCalcPredictionLOOCV.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + T_nw <<- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <<- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + } > > > test.runitCalcPredictionLOOCV <- function() { + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionLOOCV(obs=obs_mat, delta=delta, b=b, n=n ,K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, active_mu=active_mu, active_sd=active_sd, + inactive_mu=inactive_mu, inactive_sd=inactive_sd, mu_type=mu_type) + + checkEquals(obs_mat[rem_gene, rem_k], predict, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.071 0.020 0.087
lpNet.Rcheck/tests/runitCalcPredictionLOOCV_timeSeries.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 3 + + T_nw <<- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <<- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <<- array(NA, c(n,K,T_)) + + obs_mat[,,1] <<- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.95, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + baseline <<- c(0.76, 0.76, 0) + + mu_types <<- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <<- list() + mu_list[[1]] <<- list() + mu_list[[2]] <<- list() + mu_list[[3]] <<- list() + mu_list[[4]] <<- list() + mu_list[[5]] <<- list() + + mu_list[[1]]$active_mu <<- 0.95 + mu_list[[1]]$active_sd <<- 0.01 + mu_list[[1]]$inactive_mu <<- 0.56 + mu_list[[1]]$inactive_sd <<- 0.01 + mu_list[[1]]$delta <<- rep(0.755, n) + + mu_list[[2]]$active_mu <<- rep(0.95, n) + mu_list[[2]]$active_sd <<- rep(0.01, n) + mu_list[[2]]$inactive_mu <<- rep(0.56, n) + mu_list[[2]]$inactive_sd <<- rep(0.01, n) + mu_list[[2]]$delta <<- rep(0.755, n) + + mu_list[[3]]$active_mu <<- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, n*K*T_), c(n,K,T_)) + } > > > test.runitCalcPredictionLOOCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + ## calculate mean squared error of predicted and observed + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.56, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV02 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV03 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 4 + rem_t <- 3 + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV04 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,-1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 4 + rem_t <- 3 + obs_modified[2,4,2] <- NA + obs_modified[3,4,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict)) + } + } > > > test.runitCalcPredictionLOOCV05 <- function() { + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 3 + obs_modified[2,2,2] <- NA + obs_modified[3,2,3] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, baseline=baseline, + rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > > test.runitCalcPredictionLOOCV06 <- function() { + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 2 + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkTrue(is.na(predict)) + } + } > > > test.runitCalcPredictionLOOCV07 <- function() { + + baseline <- c(0.76, 0.76, 0.76) + + obs_modified <- obs_mat + rem_gene <- 3 + rem_k <- 2 + rem_t <- 2 + obs_modified[2,2,1] <- NA + obs_modified[3,2,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + predict <- calcPredictionLOOCV(obs=obs_modified, delta=delta, b=b, n=n, K=K, adja=T_nw, + baseline=baseline, rem_gene=rem_gene, rem_k=rem_k, rem_t=rem_t, + active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, flag_time_series=TRUE) + + checkEquals(predict, 0.95, tolerance=0.05) + } + } > > proc.time() user system elapsed 0.080 0.021 0.096
lpNet.Rcheck/tests/runitCalcRangeLambda.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.calcRangeLambda <- function() { + + n <- 3 + K <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.25) + + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + delta <- rep(0.755, n) + delta_type <- "perGene" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaPerGeneExp<- function() { + + n <- 3 + K <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, + 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.33) + + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + delta = matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + delta_type <- "perGeneExp" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.09) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + delta <- rep(0.755, n) + delta_type <- "perGene" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > test.calcRangeLambdaTimeSeriesPerGeneExp <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.20, + 1.25, 1.28) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + delta = matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.96), nrow=n, ncol=K, byrow=TRUE) + delta_type <- "perGeneExp" + + lambda <-calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeriesPerGeneTime <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.20, + 1.25) + + obs_mat = array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + delta <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=T_, byrow=TRUE) + delta_type <- "perGeneTime" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > > test.calcRangeLambdaTimeSeriesperGeneExpTime <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- c(0.00, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, + 0.10, 0.12, 0.14, 0.16, 0.18, 0.20, 0.22, 0.24, 0.26, 0.28, + 0.30, 0.32, 0.34, 0.36, 0.38, 0.40, 0.42, 0.44, 0.46, 0.48, + 0.50, 0.52, 0.54, 0.56, 0.58, 0.60, 0.62, 0.64, 0.66, 0.68, + 0.70, 0.72, 0.74, 0.76, 0.78, 0.80, 0.82, 0.84, 0.86, 0.88, + 0.90, 0.92, 0.94, 0.96, 0.98, 1.00, 1.05, 1.10, 1.15, 1.19) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + delta <- array(NA, c(n,K,T_)) + + delta[,,1] <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta[,,2] <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta[,,3] <- matrix(c(0.755, 0.755, 0.755, 0.755, + 0.755, 0.755, 0.755, 0.755, + 0.755, 0.755, 0.755, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta[,,4] <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta_type <- "perGeneExpTime" + + lambda <- calcRangeLambda(obs=obs_mat, delta=delta, delta_type=delta_type, flag_time_series=TRUE) + + checkEquals(true_result, lambda) + } > > proc.time() user system elapsed 0.074 0.020 0.090
lpNet.Rcheck/tests/runitDoILP.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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. > .setUp <- function(){ + + n <<- 3 + K <<- 4 + + T_nw <<- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <<- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + lambda <<- 1/10 + annot <<- getEdgeAnnot(n) + } > > > test.doILPShortExamplePerGene <- function() { + + true_result_objval <- 13.52785 + true_result_solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + delta = rep(0.755, n) + delta_type <- "perGene" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_=NULL, annot, delta_type, prior=NULL, sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > test.doILPShortExamplePerGeneExp <- function() { + + true_result_objval <- 19.68196 + true_result_solution <- c(0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 1.9358974, 1.9358974, 0.0000000, + 0.0000000, 1.1411606, 1.1411606, + 1.9358974, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + delta = matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta_type <- "perGeneExp" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_=NULL, annot, delta_type, prior=NULL, sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > proc.time() user system elapsed 0.071 0.023 0.090
lpNet.Rcheck/tests/runitDoILP_timeSeries.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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. > .setUp <- function() { + + n <<- 3 + K <<- 4 + T_ <<- 4 + + T_nw <<- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <<- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <<- array(NA, c(n,K,T_)) + + obs_mat[,,1] <<- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <<- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + lambda <<- 1/10 + annot <<- getEdgeAnnot(n) + } > > > test.doILPTimeSeriesShortExamplePerGene <- function() { + + true_result_objval <- 2.344474 + true_result_solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 0.7947368, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + delta <- rep(0.755, n) + + delta_type <- "perGene" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL, + sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > test.doILPTimeSeriesShortExamplePerGenePerExp <- function() { + + + true_result_objval <- 24.99447 + true_result_solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 0.7947368, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + delta <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta_type <- "perGeneExp" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL, + sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > test.doILPTimeSeriesShortExamplePerGenePerTime <- function() { + + + true_result_objval <- 109.5545 + true_result_solution <- c(0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.7947368, 0.7947368, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.7550000, 0.7550000, + 0.0000000, 0.7550000, 0.7550000, + 0.0000000, 0.0000000, 0.7550000, + 0.0000000, 0.7550000, 0.0000000, + 0.0000000, 0.7550000, 0.7550000, + 0.0000000, 0.7550000, 0.7550000, + 0.7550000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.7550000, 0.7550000, + 0.0000000, 0.0000000, 0.7550000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + delta <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta_type <- "perGeneTime" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL, + sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > test.doILPTimeSeriesShortExamplePerGenePerExpPerTime <- function() { + + true_result_objval <- 62.70474 + true_result_solution <- c(0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.7947368, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.7550000, 0.0000000, + 0.0000000, 0.7550000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.7550000, 0.0000000, + 0.0000000, 0.7550000, 0.0000000, + 0.0000000, 0.7550000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.7550000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.7550000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + delta <- array(NA, c(n,K,T_)) + + delta[,,1] <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta[,,2] <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta[,,3] <- matrix(c(0.755, 0.755, 0.755, 0.755, + 0.755, 0.755, 0.755, 0.755, + 0.755, 0.755, 0.755, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta[,,4] <- matrix(c(0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755, + 0.755, 0.755, 0.96, 0.755), nrow=n, ncol=K, byrow=TRUE) + + delta_type <- "perGeneExpTime" + + res <- doILP(obs_mat, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL, + sourceNode=NULL, sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE) + + checkEquals(true_result_objval, res$objval, tolerance=0.00001) + checkEquals(true_result_solution, res$solution, tolerance=0.00001) + } > > > proc.time() user system elapsed 0.074 0.021 0.092
lpNet.Rcheck/tests/runitGenerateTimeSeriesNetStates.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.generateTimeSeriesGeneStates <- function() { + + n <- 10 + K <- 11 + T_ <- 6 + + true_result <- array(NA, c(n,K,T_)) + + true_result[ , , 1] <- matrix(c(0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 2] <- matrix(c(0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,0,1,1,1,1,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 3] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 4] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,0,1,1,1, + 0,0,0,1,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,1,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 5] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1,1, + 0,0,0,1,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,1,0,0,0, + 1,0,1,1,1,1,1,1,1,0,1), nrow=n, ncol=K, byrow=TRUE) + + true_result[ , , 6] <- matrix(c(0,1,1,0,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1,1, + 1,1,0,1,1,0,1,0,1,1,1, + 0,0,0,0,1,0,0,0,0,0,0, + 1,1,1,1,1,1,1,0,1,1,1, + 0,0,0,0,0,1,0,0,0,0,0, + 1,0,1,1,1,1,1,1,1,0,1), nrow=n, ncol=K, byrow=TRUE) + + T_nw <- matrix(c(0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,1, + 0,1,0,0,1,1,0,0,0,0, + 1,0,0,0,1,0,0,0,0,0, + 0,1,0,0,0,0,-1,0,1,0, + 0,1,1,0,0,0,1,0,-1,0, + 0,1,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,-1,0,0,0,0), nrow=n, ncol=n, byrow=T) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + + gene_states <- generateTimeSeriesNetStates(nw_und=T_nw, b=b, n=n, K=K, T_user=NULL) + + checkEquals(true_result, gene_states$node_state_vec) + } > > > test.generateTimeSeriesGeneStatesT10 <- function() { + + n <- 10 + K <- 11 + T_ <- 6 + + T_nw <- matrix(c(0,0,1,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,1, + 0,1,0,0,1,1,0,0,0,0, + 1,0,0,0,1,0,0,0,0,0, + 0,1,0,0,0,0,-1,0,1,0, + 0,1,1,0,0,0,1,0,-1,0, + 0,1,0,0,1,0,0,0,0,0, + 0,0,0,0,0,1,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,1,-1,0,0,0,0), nrow=n, ncol=n, byrow=T) + + b <- c(0,1,1,1,1,1,1,1,1,1, + 1,0,1,1,1,1,1,1,1,1, + 1,1,0,1,1,1,1,1,1,1, + 1,1,1,0,1,1,1,1,1,1, + 1,1,1,1,0,1,1,1,1,1, + 1,1,1,1,1,0,1,1,1,1, + 1,1,1,1,1,1,0,1,1,1, + 1,1,1,1,1,1,1,0,1,1, + 1,1,1,1,1,1,1,1,0,1, + 1,1,1,1,1,1,1,1,1,0, + 1,1,1,1,1,1,1,1,1,1) + + + gene_states <- generateTimeSeriesNetStates(nw_und=T_nw, b=b, n=n, K=K, T_user=10) + + checkEquals(10, gene_states$T_) + } > > > proc.time() user system elapsed 0.079 0.021 0.099
lpNet.Rcheck/tests/runitGetAdja.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.getAdja <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(0, 0.7947368, -1.1411606, + 0, 0.0000000, 1.9358974, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + + res <- list() + + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12") + + adja = getAdja(res, n) + + checkEquals(true_result, adja) + + } > > > test.getAdjaTimeSeries<- function() { + + n <- 3 + + true_result = matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + res = list() + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 0.7947368, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12", + "s_13", "s_14", "s_15", "s_16", + "s_17", "s_18", "s_19", "s_20", + "s_21", "s_22", "s_23", "s_24", + "s_25", "s_26", "s_27", "s_28", + "s_29", "s_30", "s_31", "s_32", + "s_33", "s_34", "s_35", "s_36") + + adja = getAdja(res, n) + + checkEquals(true_result, adja) + } > > proc.time() user system elapsed 0.075 0.023 0.099
lpNet.Rcheck/tests/runitGetBaseline.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.getBaseline <- function() { + + n <- 3 + K <- 4 + + true_result = c(0.7550000, 0.0000000, 0.0000000) + + res <- list() + + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 1.9358974, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 1.1411606, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.4450526, 0.4450526, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12") + + adja = getBaseline(res, n) + + checkEquals(true_result, adja) + + } > > > test.getBaselineTimeSeries<- function() { + + n <- 3 + + true_result = c(0.7550000, 0.0000000, 0.0000000) + + res = list() + res$solution <- c(0.0000000, 0.7947368, 0.0000000, + 0.0000000, 0.0000000, 0.7947368, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.7550000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000, + 0.0000000, 0.0000000, 0.0000000) + + res$objective <- c(0, 1, 1, 1, 0, 1, 1, 1, 0, 0, + 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, + 1, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10, 10, 10, 10, + 10, 10, 10, 10, 10) + + names(res$objective) <- c("w+_1_1", "w+_1_2", "w+_1_3", + "w+_2_1", "w+_2_2", "w+_2_3", + "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", + "w-_2_1", "w-_2_2", "w-_2_3", + "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0", + "s_1", "s_2", "s_3", "s_4", + "s_5", "s_6", "s_7", "s_8", + "s_9", "s_10", "s_11", "s_12", + "s_13", "s_14", "s_15", "s_16", + "s_17", "s_18", "s_19", "s_20", + "s_21", "s_22", "s_23", "s_24", + "s_25", "s_26", "s_27", "s_28", + "s_29", "s_30", "s_31", "s_32", + "s_33", "s_34", "s_35", "s_36") + + + adja = getBaseline(res, n) + + checkEquals(true_result, adja) + } > > proc.time() user system elapsed 0.075 0.023 0.093
lpNet.Rcheck/tests/runitGetEdgeAnnot.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.getEdgeAnnot <- function() { + + true_result = c("w+_1_1", "w+_1_2", "w+_1_3", "w+_2_1", "w+_2_2", "w+_2_3", "w+_3_1", "w+_3_2", "w+_3_3", + "w-_1_1", "w-_1_2", "w-_1_3", "w-_2_1", "w-_2_2", "w-_2_3", "w-_3_1", "w-_3_2", "w-_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0") + + n <- 3 + edge_annot <- getEdgeAnnot(n, allpos=FALSE) + + checkEquals(true_result, edge_annot) + } > > > test.getEdgeAnnotAllPos <- function() { + + true_result = c("w+_1_1", "w+_1_2", "w+_1_3", "w+_2_1", "w+_2_2", "w+_2_3", "w+_3_1", "w+_3_2", "w+_3_3", + "w_1_^_0", "w_2_^_0", "w_3_^_0") + + n <- 3 + edge_annot <- getEdgeAnnot(n, allpos=TRUE) + + checkEquals(true_result, edge_annot) + } > > > proc.time() user system elapsed 0.077 0.024 0.102
lpNet.Rcheck/tests/runitGetObsMat.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.getObsMatMuTypeSingle <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=T) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + active_mu <- 0.95 + active_sd <- 0.01 + inactive_mu <- 0.56 + inactive_sd <- 0.01 + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="single") + checkEquals(true_result, obs_mat, tolerance=(active_sd + inactive_sd)) + } > > > test.getObsMatMuTypePerGene <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 1.3), nrow=n, ncol=K, byrow=T) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + + active_mu <- c(0.95, 1.1, 1.3) + active_sd <- rep(0.01, n) + inactive_mu <- c(0.56, 0.4, 0.2) + inactive_sd <- rep(0.01, n) + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGene") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExp <- function() { + + n <- 3 + K <- 4 + + true_result <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 30.7), nrow=n, ncol=K, byrow=T) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + act_mat <- calcActivation(T_nw, b, n, K) + + active_mu <- matrix(c(10.1, 20.1, 30.1, + 10.3, 20.3, 30.3, + 10.5, 20.5, 30.5, + 10.7, 20.7, 30.7), nrow=n, ncol=K) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + inactive_mu <- matrix(c(1.1, 2.1, 3.1, + 1.3, 2.3, 3.3, + 1.5, 2.5, 3.5, + 1.7, 2.7, 3.7), nrow=n, ncol=K) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + obs_mat <- getObsMat(act_mat, net_states=NULL, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExp") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypeSingle_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n, K, T_)) + + true_result[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=T) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- 0.95 + active_sd <- 0.01 + inactive_mu <- 0.56 + inactive_sd <- 0.01 + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="single") + checkEquals(true_result, obs_mat, tolerance=(active_sd + inactive_sd)) + } > > > test.getObsMatMuTypePerGene_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.4, 0.4, 0.4, 0.4, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 0.4, 0.4, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 0.2), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.4, 0.4, 1.1, 1.1, + 0.2, 0.2, 0.2, 1.3), nrow=n, ncol=K, byrow=T) + + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- c(0.95, 1.1, 1.3) + active_sd <- rep(0.01, n) + inactive_mu <- c(0.56, 0.4, 0.2) + inactive_sd <- rep(0.01, n) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGene") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExp_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(1.1, 10.3, 10.5, 10.7, + 2.1, 2.3, 20.5, 20.7, + 3.1, 3.3, 3.5, 30.7), nrow=n, ncol=K, byrow=T) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + + active_mu <- matrix(c(10.1, 10.3, 10.5, 10.7, + 20.1, 20.3, 20.5, 20.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=K, byrow=T) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + inactive_mu <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExp") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneTime_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.1, 1.1, 1.1, + 2.1, 2.1, 2.1, 2.1, + 3.1, 3.1, 3.1, 3.1), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(1.3, 10.3, 10.3, 10.3, + 2.1, 2.3, 2.3, 2.3, + 3.3, 3.3, 3.3, 3.3), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(1.5, 10.5, 10.5, 10.5, + 2.5, 2.5, 20.5, 20.5, + 3.5, 3.5, 3.5, 3.5), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(1.7, 10.7, 10.7, 10.7, + 2.7, 2.7, 20.7, 20.7, + 3.7, 3.7, 3.7, 30.7), nrow=n, ncol=K, byrow=T) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + + active_mu <- matrix(c(10.1, 10.3, 10.5, 10.7, + 20.1, 20.3, 20.5, 20.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=T_, byrow=T) + + active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=T_) + + inactive_mu <- matrix(c(1.1, 1.3, 1.5, 1.7, + 2.1, 2.3, 2.5, 2.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=T_, byrow=T) + + inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=T_) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneTime") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > > test.getObsMatMuTypePerGeneExpTime_nodeStates <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + true_result <- array(NA, c(n,K,T_)) + + true_result[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7), nrow=n, ncol=K, byrow=T) + + true_result[,,2] <- matrix(c(2.1, 20.3, 20.5, 20.7, + 2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7), nrow=n, ncol=K, byrow=T) + + true_result[,,3] <- matrix(c(3.1, 30.3, 30.5, 30.7, + 3.1, 3.3, 30.5, 30.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + true_result[,,4] <- matrix(c(4.1, 40.3, 40.5, 40.7, + 4.1, 4.3, 40.5, 40.7, + 4.1, 4.3, 4.5, 40.7), nrow=n, ncol=K, byrow=T) + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + net_states <- array(NA, c(n,K,T_)) + + net_states[,,1] <- matrix(c(0,0,0,0, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,2] <- matrix(c(0,1,1,1, + 0,0,0,0, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,3] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,0), nrow=n, ncol=K, byrow=T) + + net_states[,,4] <- matrix(c(0,1,1,1, + 0,0,1,1, + 0,0,0,1), nrow=n, ncol=K, byrow=T) + + active_mu <- array(NA, c(n,K,T_)) + + active_mu[,,1] <- matrix(c(10.1, 10.3, 10.5, 10.7, + 10.1, 10.3, 10.5, 10.7, + 10.1, 10.3, 10.5, 10.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,2] <- matrix(c(20.1, 20.3, 20.5, 20.7, + 20.1, 20.3, 20.5, 20.7, + 20.1, 20.3, 20.5, 20.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,3] <- matrix(c(30.1, 30.3, 30.5, 30.7, + 30.1, 30.3, 30.5, 30.7, + 30.1, 30.3, 30.5, 30.7), nrow=n, ncol=K, byrow=T) + + active_mu[,,4] <- matrix(c(40.1, 40.3, 40.5, 40.7, + 40.1, 40.3, 40.5, 40.7, + 40.1, 40.3, 40.5, 40.7), nrow=n, ncol=K, byrow=T) + + active_sd <- array(0.01, c(n,K,T_)) + + inactive_mu <- array(NA, c(n,K,T_)) + inactive_mu[,,1] <- matrix(c(1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7, + 1.1, 1.3, 1.5, 1.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,2] <- matrix(c(2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7, + 2.1, 2.3, 2.5, 2.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,3] <- matrix(c(3.1, 3.3, 3.5, 3.7, + 3.1, 3.3, 3.5, 3.7, + 3.1, 3.3, 3.5, 3.7), nrow=n, ncol=K, byrow=T) + + inactive_mu[,,4] <- matrix(c(4.1, 4.3, 4.5, 4.7, + 4.1, 4.3, 4.5, 4.7, + 4.1, 4.3, 4.5, 4.7), nrow=n, ncol=K, byrow=T) + + inactive_sd <- array(0.01, c(n,K,T_)) + + obs_mat <- getObsMat(act_mat=NULL, net_states, active_mu, active_sd, inactive_mu, inactive_sd, mu_type="perGeneExpTime") + checkEquals(true_result, obs_mat, tolerance=(max(active_sd) + max(inactive_sd))) + } > > proc.time() user system elapsed 0.088 0.025 0.110
lpNet.Rcheck/tests/runitGetSampleAdja.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.getSampleAdja <- function() { + + n <- 3 + K <- 4 + annot <- getEdgeAnnot(n) + annot_node = seq(1,n) + + true_result <- matrix(c(0, 0.7947368, -0.3973684, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- annot_node + + edges_all <- matrix(c(0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.7947368, 0.0000000, 0, 0.7947368, 0, 0.0000000, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + -0.5534774, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.3262604, -0.7947368, 0, 0.7947368, 0, 0.7947368, + 1.9358974, 0.0000000, 0, -1.3482143, 0, -1.9358974, + 1.9358974, 0.0000000, 0, 0.0000000, 0, -1.9358974), nrow=n*K, ncol=n*(n-1), byrow=TRUE) + + colnames(edges_all) <- c("1->2", "1->3", "2->1", "2->3", "3->1", "3->2") + + sampleAdja = getSampleAdja(edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, sampleAdja, tolerance=0.00001) + } > > proc.time() user system elapsed 0.069 0.018 0.084
lpNet.Rcheck/tests/runitGetSampleAdjaMAD.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.getSampleAdjaMAD <- function() { + + n <- 3 + K <- 4 + annot <- getEdgeAnnot(n) + annot_node = seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.0000000, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- annot_node + + edges_all <- matrix(c(0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.0000000, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + 0.7947368, 0.0000000, 0, 0.7947368, 0, 0.0000000, + 0.7947368, 0.7947368, 0, 0.0000000, 0, 0.0000000, + -0.5534774, -1.1411606, 0, 1.9358974, 0, 1.3482143, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.7947368, -1.1411606, 0, 1.9358974, 0, 0.0000000, + 0.3262604, -0.7947368, 0, 0.7947368, 0, 0.7947368, + 1.9358974, 0.0000000, 0, -1.3482143, 0, -1.9358974, + 1.9358974, 0.0000000, 0, 0.0000000, 0, -1.9358974), nrow=n*K, ncol=n*(n-1), byrow=TRUE) + + colnames(edges_all) <- c("1->2", "1->3", "2->1", "2->3", "3->1", "3->2") + + sampleAdjaMAD = getSampleAdjaMAD(edges_all, n, annot_node, method=median, method2=mad, septype="->") + + checkEquals(true_result, sampleAdjaMAD, tolerance=0.00001) + } > > proc.time() user system elapsed 0.070 0.020 0.086
lpNet.Rcheck/tests/runitKfoldCV.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.runitKfoldCV <- function() { + + n <- 3 + K <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- list() + + true_result <- matrix(c(0, 0.7947368, -0.5, + 0, 0.0000000, 1.0, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- kfoldCV(kfold=kfold, times=1, delta=delta, lambda=lambda, obs=obs_mat, b=b, n=n, K=K, T_=NULL, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.6) + } + } > > > proc.time() user system elapsed 0.073 0.023 0.093
lpNet.Rcheck/tests/runitKfoldCV_timeSeries.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.runitKfoldCV_timeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + delta_types <- c("perGene", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + mu_list[[4]] <- list() + mu_list[[5]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, n*K*T_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- kfoldCV(kfold=kfold, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=T_, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE, flag_time_series=TRUE) + + adja <- getSampleAdjaMAD(res$edges_all, n, annot_node, method=median, method2=mad, septype="->") + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.079 0.019 0.094
lpNet.Rcheck/tests/runitLOOCV.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.runitLOOCV <- function() { + + n <- 3 + K <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- list() + + true_result <- matrix(c(0, 0.7947368, -0.3973684, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- loocv(kfold=NULL, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=NULL, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.077 0.022 0.098
lpNet.Rcheck/tests/runitLOOCV_timeSeries.Rout
R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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.runitLOOCV_timeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76, 0.76, 0) + + mu_types <- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + delta_types <- c("perGene", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + mu_list[[4]] <- list() + mu_list[[5]] <- list() + + mu_list[[1]]$active_mu <- 0.95 + mu_list[[1]]$active_sd <- 0.01 + mu_list[[1]]$inactive_mu <- 0.56 + mu_list[[1]]$inactive_sd <- 0.01 + mu_list[[1]]$delta <- rep(0.755, n) + + + mu_list[[2]]$active_mu <- rep(0.95, n) + mu_list[[2]]$active_sd <- rep(0.01, n) + mu_list[[2]]$inactive_mu <- rep(0.56, n) + mu_list[[2]]$inactive_sd <- rep(0.01, n) + mu_list[[2]]$delta <- rep(0.755, n) + + mu_list[[3]]$active_mu <- matrix(rep(0.95, n*K), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, n*K), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, n*K), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, n*K), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, n*T_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, n*T_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, n*K*T_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, n*K*T_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, n*K*T_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, n*K*T_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$active_mu + active_sd <- mu_list[[i]]$active_sd + inactive_mu <- mu_list[[i]]$inactive_mu + inactive_sd <- mu_list[[i]]$inactive_sd + delta <- mu_list[[i]]$delta + + res <- loocv(kfold=NULL, times=1, obs=obs_mat, delta=delta, lambda=lambda, b=b, n=n, K=K, T_=T_, annot=annot, + annot_node=annot_node, active_mu=active_mu, active_sd=active_sd, inactive_mu=inactive_mu, + inactive_sd=inactive_sd, mu_type=mu_type, delta_type=delta_type, prior=NULL, sourceNode=NULL, + sinkNode=NULL, allint=FALSE, allpos=FALSE, flag_time_series=TRUE) + + adja <- getSampleAdja(res$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.079 0.020 0.097
lpNet.Rcheck/lpNet-Ex.timings
name | user | system | elapsed | |
CV | 0.100 | 0.009 | 0.111 | |
calcActivation | 0.001 | 0.000 | 0.001 | |
calcPrediction | 0.001 | 0.000 | 0.001 | |
calcRangeLambda | 0 | 0 | 0 | |
doILP | 0.000 | 0.000 | 0.001 | |
generateTimeSeriesNetStates | 0.000 | 0.000 | 0.001 | |
getAdja | 0.000 | 0.000 | 0.001 | |
getBaseline | 0.000 | 0.000 | 0.001 | |
getEdgeAnnot | 0 | 0 | 0 | |
getObsMat | 0.000 | 0.000 | 0.001 | |
getSampleAdja | 0.000 | 0.000 | 0.001 | |
summarizeRepl | 0.007 | 0.002 | 0.008 | |