Lars Kaderali
| Snapshot Date: 2022-03-17 13:55:23 -0400 (Thu, 17 Mar 2022) |
| git_url: https://git.bioconductor.org/packages/lpNet |
| git_branch: master |
| git_last_commit: fa5edc5 |
| git_last_commit_date: 2021-10-26 12:06:42 -0400 (Tue, 26 Oct 2021) |
| Back to Multiple platform build/check report for BioC 3.15 |
|
This page was generated on 2022-03-18 11:07:53 -0400 (Fri, 18 Mar 2022).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 20.04.4 LTS) | x86_64 | R Under development (unstable) (2022-02-17 r81757) -- "Unsuffered Consequences" | 4334 |
| riesling1 | Windows Server 2019 Standard | x64 | R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences" | 4097 |
| palomino3 | Windows Server 2022 Datacenter | x64 | R Under development (unstable) (2022-02-17 r81757 ucrt) -- "Unsuffered Consequences" | 4083 |
| merida1 | macOS 10.14.6 Mojave | x86_64 | R Under development (unstable) (2022-03-02 r81842) -- "Unsuffered Consequences" | 4134 |
| 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 | ||||
|
To the developers/maintainers of the lpNet package: - Please 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 How and When does the builder pull? When will my changes propagate? here for more information. - Make sure to use the following settings in order to reproduce any error or warning you see on this page. |
| Package 1020/2090 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| lpNet 2.27.0 (landing page) Lars Kaderali
| nebbiolo1 | Linux (Ubuntu 20.04.4 LTS) / x86_64 | OK | OK | WARNINGS | | ||||||||
| riesling1 | Windows Server 2019 Standard / x64 | OK | OK | WARNINGS | OK | |||||||||
| palomino3 | Windows Server 2022 Datacenter / x64 | OK | OK | WARNINGS | OK | | ||||||||
| merida1 | macOS 10.14.6 Mojave / x86_64 | OK | OK | WARNINGS | OK | | ||||||||
| Package: lpNet |
| Version: 2.27.0 |
| Command: D:\biocbuild\bbs-3.15-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:lpNet.install-out.txt --library=D:\biocbuild\bbs-3.15-bioc\R\library --no-vignettes --timings lpNet_2.27.0.tar.gz |
| StartedAt: 2022-03-17 19:27:31 -0400 (Thu, 17 Mar 2022) |
| EndedAt: 2022-03-17 19:28:09 -0400 (Thu, 17 Mar 2022) |
| EllapsedTime: 38.2 seconds |
| RetCode: 0 |
| Status: WARNINGS |
| CheckDir: lpNet.Rcheck |
| Warnings: 1 |
##############################################################################
##############################################################################
###
### Running command:
###
### D:\biocbuild\bbs-3.15-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:lpNet.install-out.txt --library=D:\biocbuild\bbs-3.15-bioc\R\library --no-vignettes --timings lpNet_2.27.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory 'D:/biocbuild/bbs-3.15-bioc/meat/lpNet.Rcheck'
* using R Under development (unstable) (2021-11-21 r81221)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'lpNet/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'lpNet' version '2.27.0'
* 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 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 R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... 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 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 ... NOTE
'library' or 'require' call not declared from: 'KEGGgraph'
* checking package vignettes in 'inst/doc' ... 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
'D:/biocbuild/bbs-3.15-bioc/meat/lpNet.Rcheck/00check.log'
for details.
lpNet.Rcheck/00install.out
##############################################################################
##############################################################################
###
### Running command:
###
### D:\biocbuild\bbs-3.15-bioc\R\bin\R.exe CMD INSTALL lpNet
###
##############################################################################
##############################################################################
* installing to library 'D:/biocbuild/bbs-3.15-bioc/R/library'
* installing *source* package 'lpNet' ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
converting help for package 'lpNet'
finding HTML links ... done
CV html
calcActivation html
calcPrediction html
calcRangeLambda html
doILP html
generateTimeSeriesNetStates html
getAdja html
getBaseline html
getEdgeAnnot html
getObsMat html
getSampleAdja html
lpNet-package html
summarizeRepl html
** 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)
Making 'packages.html' ...Warning in packageDescription(i, lib.loc = lib, fields = "Title", encoding = "UTF-8") :
DESCRIPTION file of package 'splots' is missing or broken
done
lpNet.Rcheck/tests/runitCalcActivation.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.23 0.01 0.21
lpNet.Rcheck/tests/runitCalcPredictionKfoldCV.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> .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
0.09 0.09 0.15
lpNet.Rcheck/tests/runitCalcPredictionKfoldCV_timeSeries.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> .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
0.14 0.06 0.15
lpNet.Rcheck/tests/runitCalcPredictionLOOCV.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> .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
0.18 0.03 0.18
lpNet.Rcheck/tests/runitCalcPredictionLOOCV_timeSeries.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> .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
0.17 0.04 0.28
lpNet.Rcheck/tests/runitCalcRangeLambda.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.10 0.07 0.15
lpNet.Rcheck/tests/runitDoILP.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> .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
0.12 0.07 0.15
lpNet.Rcheck/tests/runitDoILP_timeSeries.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> .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
0.17 0.04 0.20
lpNet.Rcheck/tests/runitGenerateTimeSeriesNetStates.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.14 0.07 0.20
lpNet.Rcheck/tests/runitGetAdja.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.14 0.03 0.15
lpNet.Rcheck/tests/runitGetBaseline.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.17 0.01 0.17
lpNet.Rcheck/tests/runitGetEdgeAnnot.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.15 0.01 0.15
lpNet.Rcheck/tests/runitGetObsMat.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.17 0.06 0.23
lpNet.Rcheck/tests/runitGetSampleAdja.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.15 0.07 0.21
lpNet.Rcheck/tests/runitGetSampleAdjaMAD.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.14 0.03 0.15
lpNet.Rcheck/tests/runitKfoldCV.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.09 0.10 0.17
lpNet.Rcheck/tests/runitKfoldCV_timeSeries.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.12 0.06 0.20
lpNet.Rcheck/tests/runitLOOCV.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.17 0.03 0.17
lpNet.Rcheck/tests/runitLOOCV_timeSeries.Rout
R Under development (unstable) (2021-11-21 r81221) -- "Unsuffered Consequences"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> test.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
0.14 0.07 0.20
lpNet.Rcheck/lpNet-Ex.timings
| name | user | system | elapsed | |
| CV | 0.31 | 0.10 | 0.40 | |
| calcActivation | 0 | 0 | 0 | |
| calcPrediction | 0 | 0 | 0 | |
| calcRangeLambda | 0 | 0 | 0 | |
| doILP | 0 | 0 | 0 | |
| generateTimeSeriesNetStates | 0 | 0 | 0 | |
| getAdja | 0 | 0 | 0 | |
| getBaseline | 0.02 | 0.00 | 0.02 | |
| getEdgeAnnot | 0 | 0 | 0 | |
| getObsMat | 0 | 0 | 0 | |
| getSampleAdja | 0 | 0 | 0 | |
| summarizeRepl | 0.01 | 0.00 | 0.01 | |