Multiple platform build/check report for BioC 3.11 - CHECK report for lpNet on tokay2

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CHECK report for lpNet on tokay2

This page was generated on 2020-10-17 11:56:58 -0400 (Sat, 17 Oct 2020).

TO THE DEVELOPERS/MAINTAINERS OF THE lpNet PACKAGE: Please make sure to use the following settings in order to reproduce any error or warning you see on this page.

lpNet 2.20.0
Lars Kaderali

Snapshot Date: 2020-10-16 14:40:19 -0400 (Fri, 16 Oct 2020)

URL: https://git.bioconductor.org/packages/lpNet

Branch: RELEASE_3_11

Last Commit: 86c971a

Last Changed Date: 2020-04-27 14:33:04 -0400 (Mon, 27 Apr 2020)

malbec2

Linux (Ubuntu 18.04.4 LTS) / x86_64

WARNINGS

tokay2

Windows Server 2012 R2 Standard / x64

[ WARNINGS ]

machv2

macOS 10.14.6 Mojave / x86_64

WARNINGS

Summary

Package: lpNet

Version: 2.20.0

Command: C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:lpNet.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings lpNet_2.20.0.tar.gz

StartedAt: 2020-10-17 05:33:53 -0400 (Sat, 17 Oct 2020)

EndedAt: 2020-10-17 05:35:19 -0400 (Sat, 17 Oct 2020)

EllapsedTime: 85.9 seconds

RetCode: 0

Status: WARNINGS

CheckDir: lpNet.Rcheck

Warnings: 1

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:lpNet.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings lpNet_2.20.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.11-bioc/meat/lpNet.Rcheck'
* using R version 4.0.3 (2020-10-10)
* 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 '2.20.0'
* 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 ... WARNING
Found the following significant warnings:
  Warning: Package 'nem' is deprecated and will be removed from Bioconductor
See 'C:/Users/biocbuild/bbs-3.11-bioc/meat/lpNet.Rcheck/00install.out' for details.
* 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
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... 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 ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
** running examples for arch 'x64' ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  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
** running tests for arch 'x64' ...
  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
  'C:/Users/biocbuild/bbs-3.11-bioc/meat/lpNet.Rcheck/00check.log'
for details.

Installation output

lpNet.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.11/bioc/src/contrib/lpNet_2.20.0.tar.gz && rm -rf lpNet.buildbin-libdir && mkdir lpNet.buildbin-libdir && C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=lpNet.buildbin-libdir lpNet_2.20.0.tar.gz && C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD INSTALL lpNet_2.20.0.zip && rm lpNet_2.20.0.tar.gz lpNet_2.20.0.zip
###
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##############################################################################


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

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  204k  100  204k    0     0  3196k      0 --:--:-- --:--:-- --:--:-- 3579k

install for i386

* installing *source* package 'lpNet' ...
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
Warning message:
Package 'nem' is deprecated and will be removed from Bioconductor
  version 3.12 
** 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
Warning: Package 'nem' is deprecated and will be removed from Bioconductor
  version 3.12
** testing if installed package can be loaded from final location
Warning: Package 'nem' is deprecated and will be removed from Bioconductor
  version 3.12
** testing if installed package keeps a record of temporary installation path

install for x64

* installing *source* package 'lpNet' ...
** testing if installed package can be loaded
Warning: Package 'nem' is deprecated and will be removed from Bioconductor
  version 3.12
* MD5 sums
packaged installation of 'lpNet' as lpNet_2.20.0.zip
* DONE (lpNet)
* installing to library 'C:/Users/biocbuild/bbs-3.11-bioc/R/library'
package 'lpNet' successfully unpacked and MD5 sums checked

Tests output

Example timings

lpNet.Rcheck/examples_i386/lpNet-Ex.timings

name	user	system	elapsed
CV	0.50	0.16	1.00
calcActivation	0	0	0
calcPrediction	0.01	0.00	0.01
calcRangeLambda	0	0	0
doILP	0	0	0
generateTimeSeriesNetStates	0.02	0.00	0.02
getAdja	0	0	0
getBaseline	0	0	0
getEdgeAnnot	0	0	0
getObsMat	0	0	0
getSampleAdja	0	0	0
summarizeRepl	0.04	0.00	0.07

lpNet.Rcheck/examples_x64/lpNet-Ex.timings

name	user	system	elapsed
CV	0.51	0.13	0.72
calcActivation	0.02	0.00	0.01
calcPrediction	0	0	0
calcRangeLambda	0	0	0
doILP	0	0	0
generateTimeSeriesNetStates	0	0	0
getAdja	0.02	0.00	0.01
getBaseline	0	0	0
getEdgeAnnot	0	0	0
getObsMat	0	0	0
getSampleAdja	0	0	0
summarizeRepl	0.02	0.01	0.03

lpNet.Rcheck/tests_i386/runitCalcActivation.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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 0.32 0.01 0.32	lpNet.Rcheck/tests_x64/runitCalcActivation.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.29 0.04 0.32
lpNet.Rcheck/tests_i386/runitCalcPredictionKfoldCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), 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 0.21 0.09 0.29	lpNet.Rcheck/tests_x64/runitCalcPredictionKfoldCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), 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 0.26 0.06 0.31
lpNet.Rcheck/tests_i386/runitCalcPredictionKfoldCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, nKT_), c(n,K,T_)) + } > > > test.runitCalcPredictionKfoldCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$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 0.29 0.06 0.36	lpNet.Rcheck/tests_x64/runitCalcPredictionKfoldCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, nKT_), c(n,K,T_)) + } > > > test.runitCalcPredictionKfoldCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$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 0.23 0.06 0.28
lpNet.Rcheck/tests_i386/runitCalcPredictionLOOCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), 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 0.28 0.03 0.29	lpNet.Rcheck/tests_x64/runitCalcPredictionLOOCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), 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 0.20 0.06 0.26
lpNet.Rcheck/tests_i386/runitCalcPredictionLOOCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, nKT_), c(n,K,T_)) + } > > > test.runitCalcPredictionLOOCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$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 0.21 0.09 0.31	lpNet.Rcheck/tests_x64/runitCalcPredictionLOOCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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]]$active_mu <<- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <<- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <<- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <<- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <<- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <<- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <<- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <<- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <<- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <<- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <<- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <<- array(rep(0.755, nKT_), c(n,K,T_)) + } > > > test.runitCalcPredictionLOOCV01 <- function() { + + T_nw <- matrix(c(0,0,1, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.95, 0.56, 0.95, 0.95, + 0.95, 0.95, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + baseline <- c(0, 0, 0) + + obs_modified <- obs_mat + rem_gene <- 2 + rem_k <- 4 + rem_t <- 2 + obs_modified[2,4,2] <- NA + + rem_entries <- which(is.na(obs_modified), arr.ind=TRUE) + rem_entries_vec <- which(is.na(obs_modified)) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + active_mu <- mu_list[[i]]$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 0.31 0.04 0.34
lpNet.Rcheck/tests_i386/runitCalcRangeLambda.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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 0.25 0.07 0.31	lpNet.Rcheck/tests_x64/runitCalcRangeLambda.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.29 0.03 0.31
lpNet.Rcheck/tests_i386/runitDoILP.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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 0.21 0.09 0.29	lpNet.Rcheck/tests_x64/runitDoILP.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.25 0.06 0.29
lpNet.Rcheck/tests_i386/runitDoILP_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > .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 0.26 0.07 0.31	lpNet.Rcheck/tests_x64/runitDoILP_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.29 0.03 0.31
lpNet.Rcheck/tests_i386/runitGenerateTimeSeriesNetStates.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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 0.23 0.09 0.29	lpNet.Rcheck/tests_x64/runitGenerateTimeSeriesNetStates.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.28 0.06 0.34
lpNet.Rcheck/tests_i386/runitGetAdja.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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 0.28 0.03 0.29	lpNet.Rcheck/tests_x64/runitGetAdja.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.25 0.06 0.29
lpNet.Rcheck/tests_i386/runitGetBaseline.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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 0.28 0.04 0.29	lpNet.Rcheck/tests_x64/runitGetBaseline.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.26 0.04 0.29
lpNet.Rcheck/tests_i386/runitGetEdgeAnnot.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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 0.29 0.04 0.45	lpNet.Rcheck/tests_x64/runitGetEdgeAnnot.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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 0.25 0.04 0.28
lpNet.Rcheck/tests_i386/runitGetObsMat.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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, nK), 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, nK), 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, nK), 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, nK), 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, nK), 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, nK), 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 0.37 0.01 0.36	lpNet.Rcheck/tests_x64/runitGetObsMat.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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, nK), 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, nK), 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, nK), 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, nK), 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, nK), 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, nK), 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 0.35 0.01 0.79
lpNet.Rcheck/tests_i386/runitGetSampleAdja.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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=nK, 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 0.31 0.04 0.34	lpNet.Rcheck/tests_x64/runitGetSampleAdja.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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=nK, 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 0.26 0.04 0.29
lpNet.Rcheck/tests_i386/runitGetSampleAdjaMAD.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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=nK, 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 0.28 0.04 0.29	lpNet.Rcheck/tests_x64/runitGetSampleAdjaMAD.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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=nK, 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 0.26 0.03 0.31
lpNet.Rcheck/tests_i386/runitKfoldCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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]]$active_mu <- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), 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$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.6) + } + } > > > proc.time() user system elapsed 0.25 0.01 0.26	lpNet.Rcheck/tests_x64/runitKfoldCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitKfoldCV <- function() { + + n <- 3 + K <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$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, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$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 0.25 0.07 0.34
lpNet.Rcheck/tests_i386/runitKfoldCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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]]$active_mu <- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, nKT_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$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 0.29 0.06 0.32	lpNet.Rcheck/tests_x64/runitKfoldCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 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, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, nKT_), 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 0.29 0.03 0.31
lpNet.Rcheck/tests_i386/runitLOOCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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]]$active_mu <- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), 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$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.23 0.01 0.23	lpNet.Rcheck/tests_x64/runitLOOCV.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitLOOCV <- function() { + + n <- 3 + K <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76,0.76,0) + + mu_types <- c("single", "perGene", "perGeneExp") + delta_types <- c("perGene", "perGene", "perGeneExp") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + + mu_list[[1]]$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, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$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 0.31 0.01 0.50
lpNet.Rcheck/tests_i386/runitLOOCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: i386-w64-mingw32/i386 (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.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]]$active_mu <- matrix(rep(0.95, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, nKT_), c(n,K,T_)) + + kfold <- 10 + lambda <- 1/10 + annot <- getEdgeAnnot(n) + annot_node <- seq(1,n) + + true_result <- matrix(c(0, 0.7947368, 0.0000000, + 0, 0.0000000, 0.7947368, + 0, 0.0000000, 0.0000000), nrow=n, ncol=n, byrow=TRUE) + + colnames(true_result) <- rownames(true_result) <- seq(1,n) + + for (i in 1:length(mu_types)) { + mu_type <- mu_types[i] + delta_type <- delta_types[i] + + active_mu <- mu_list[[i]]$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$edges_all, n, annot_node, method=median, septype="->") + + checkEquals(true_result, adja, tolerance=0.00001) + } + } > > > proc.time() user system elapsed 0.32 0.01 0.31	lpNet.Rcheck/tests_x64/runitLOOCV_timeSeries.Rout R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > test.runitLOOCV_timeSeries <- function() { + + n <- 3 + K <- 4 + T_ <- 4 + + T_nw <- matrix(c(0,1,0, + 0,0,1, + 0,0,0), nrow=n, ncol=n, byrow=TRUE) + + b <- c(0,1,1, + 1,0,1, + 1,1,0, + 1,1,1) + + obs_mat <- array(NA, c(n,K,T_)) + + obs_mat[,,1] <- matrix(c(0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,2] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,3] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.56), nrow=n, ncol=K, byrow=TRUE) + + obs_mat[,,4] <- matrix(c(0.56, 0.95, 0.95, 0.95, + 0.56, 0.56, 0.95, 0.95, + 0.56, 0.56, 0.56, 0.95), nrow=n, ncol=K, byrow=TRUE) + + baseline <- c(0.76, 0.76, 0) + + mu_types <- c("single", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + delta_types <- c("perGene", "perGene", "perGeneExp", "perGeneTime", "perGeneExpTime") + + mu_list <- list() + mu_list[[1]] <- list() + mu_list[[2]] <- list() + mu_list[[3]] <- list() + mu_list[[4]] <- list() + mu_list[[5]] <- list() + + mu_list[[1]]$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, nK), nrow=n, ncol=K) + mu_list[[3]]$active_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_mu <- matrix(rep(0.56, nK), nrow=n, ncol=K) + mu_list[[3]]$inactive_sd <- matrix(rep(0.01, nK), nrow=n, ncol=K) + mu_list[[3]]$delta <- matrix(rep(0.755, nK), nrow=n, ncol=K) + + mu_list[[4]]$active_mu <- matrix(rep(0.95, nT_), nrow=n, ncol=T_) + mu_list[[4]]$active_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_mu <- matrix(rep(0.56, nT_), nrow=n, ncol=T_) + mu_list[[4]]$inactive_sd <- matrix(rep(0.01, nT_), nrow=n, ncol=T_) + mu_list[[4]]$delta <- matrix(rep(0.755, nT_), nrow=n, ncol=T_) + + mu_list[[5]]$active_mu <- array(rep(0.95, nKT_), c(n,K,T_)) + mu_list[[5]]$active_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_mu <- array(rep(0.56, nKT_), c(n,K,T_)) + mu_list[[5]]$inactive_sd <- array(rep(0.01, nKT_), c(n,K,T_)) + mu_list[[5]]$delta <- array(rep(0.755, nKT_), 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 0.23 0.07 0.40