Back to Multiple platform build/check report for BioC 3.14
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This page was generated on 2022-04-13 12:06:31 -0400 (Wed, 13 Apr 2022).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 20.04.4 LTS)x86_644.1.3 (2022-03-10) -- "One Push-Up" 4324
tokay2Windows Server 2012 R2 Standardx644.1.3 (2022-03-10) -- "One Push-Up" 4077
machv2macOS 10.14.6 Mojavex86_644.1.3 (2022-03-10) -- "One Push-Up" 4137
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

CHECK results for evaluomeR on tokay2


To the developers/maintainers of the evaluomeR package:
- Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/evaluomeR.git to
reflect on this report. See How and When does the builder pull? When will my changes propagate? for more information.
- Make sure to use the following settings in order to reproduce any error or warning you see on this page.

raw results

Package 608/2083HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
evaluomeR 1.10.0  (landing page)
José Antonio Bernabé-Díaz
Snapshot Date: 2022-04-12 01:55:07 -0400 (Tue, 12 Apr 2022)
git_url: https://git.bioconductor.org/packages/evaluomeR
git_branch: RELEASE_3_14
git_last_commit: 1989f6a
git_last_commit_date: 2021-10-26 12:50:27 -0400 (Tue, 26 Oct 2021)
nebbiolo2Linux (Ubuntu 20.04.4 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
tokay2Windows Server 2012 R2 Standard / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
machv2macOS 10.14.6 Mojave / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published

Summary

Package: evaluomeR
Version: 1.10.0
Command: C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:evaluomeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings evaluomeR_1.10.0.tar.gz
StartedAt: 2022-04-12 19:26:25 -0400 (Tue, 12 Apr 2022)
EndedAt: 2022-04-12 19:31:56 -0400 (Tue, 12 Apr 2022)
EllapsedTime: 330.7 seconds
RetCode: 0
Status:   OK  
CheckDir: evaluomeR.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:evaluomeR.install-out.txt --library=C:\Users\biocbuild\bbs-3.14-bioc\R\library --no-vignettes --timings evaluomeR_1.10.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.14-bioc/meat/evaluomeR.Rcheck'
* using R version 4.1.3 (2022-03-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'evaluomeR/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'evaluomeR' version '1.10.0'
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... NOTE
Depends: includes the non-default packages:
  'SummarizedExperiment', 'MultiAssayExperiment', 'cluster', 'fpc',
  'randomForest', 'flexmix'
Adding so many packages to the search path is excessive and importing
selectively is preferable.
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'evaluomeR' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... NOTE
File
  LICENSE
is not mentioned in the DESCRIPTION file.
* 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 ... NOTE
Namespace in Imports field not imported from: 'kableExtra'
  All declared Imports should be used.
Packages in Depends field not imported from:
  'flexmix' 'randomForest'
  These packages need to be imported from (in the NAMESPACE file)
  for when this namespace is loaded but not attached.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
flemixModel: no visible global function definition for 'FLXMRglm'
flemixModel: no visible global function definition for 'stepFlexmix'
flemixModel: no visible global function definition for 'getModel'
globalMetric: no visible global function definition for 'prior'
metrics_pca: no visible global function definition for 'prcomp'
metrics_randomforest: no visible global function definition for
  'randomForest'
metrics_randomforest: no visible global function definition for 'head'
speccCBI: no visible global function definition for 'specc'
Undefined global functions or variables:
  FLXMRglm getModel head prcomp prior randomForest specc stepFlexmix
Consider adding
  importFrom("stats", "prcomp")
  importFrom("utils", "head")
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 contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... 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 'testAll.R'
  Running 'testAnalysis.R'
 OK
** running tests for arch 'x64' ...
  Running 'testAll.R'
  Running 'testAnalysis.R'
 OK
* checking for unstated dependencies in vignettes ... OK
* 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: 4 NOTEs
See
  'C:/Users/biocbuild/bbs-3.14-bioc/meat/evaluomeR.Rcheck/00check.log'
for details.

Installation output

evaluomeR.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O http://155.52.207.166/BBS/3.14/bioc/src/contrib/evaluomeR_1.10.0.tar.gz && rm -rf evaluomeR.buildbin-libdir && mkdir evaluomeR.buildbin-libdir && C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=evaluomeR.buildbin-libdir evaluomeR_1.10.0.tar.gz && C:\Users\biocbuild\bbs-3.14-bioc\R\bin\R.exe CMD INSTALL evaluomeR_1.10.0.zip && rm evaluomeR_1.10.0.tar.gz evaluomeR_1.10.0.zip
###
##############################################################################
##############################################################################


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

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  108k  100  108k    0     0   321k      0 --:--:-- --:--:-- --:--:--  320k

install for i386

* installing *source* package 'evaluomeR' ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
Loading required namespace: evaluomeR
*** installing help indices
  converting help for package 'evaluomeR'
    finding HTML links ... done
    bioMetrics                              html  
    evaluomeRSupportedCBI                   html  
    getDataQualityRange                     html  
    finding level-2 HTML links ... done

    getOptimalKValue                        html  
    globalMetric                            html  
    metricsCorrelations                     html  
    ontMetrics                              html  
    plotMetricsBoxplot                      html  
    plotMetricsCluster                      html  
    plotMetricsClusterComparison            html  
    plotMetricsMinMax                       html  
    plotMetricsViolin                       html  
    quality                                 html  
    qualityRange                            html  
    qualitySet                              html  
    rnaMetrics                              html  
    stability                               html  
    stabilityRange                          html  
    stabilitySet                            html  
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path

install for x64

* installing *source* package 'evaluomeR' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'evaluomeR' as evaluomeR_1.10.0.zip
* DONE (evaluomeR)
* installing to library 'C:/Users/biocbuild/bbs-3.14-bioc/R/library'
package 'evaluomeR' successfully unpacked and MD5 sums checked

Tests output

evaluomeR.Rcheck/tests_i386/testAll.Rout


R version 4.1.3 (2022-03-10) -- "One Push-Up"
Copyright (C) 2022 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.

> library(evaluomeR)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

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

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    I, expand.grid, unname

Loading required package: IRanges

Attaching package: 'IRanges'

The following object is masked from 'package:grDevices':

    windows

Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

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

    anyMissing, rowMedians

Loading required package: MultiAssayExperiment
Loading required package: cluster
Loading required package: fpc
Loading required package: randomForest
randomForest 4.7-1
Type rfNews() to see new features/changes/bug fixes.

Attaching package: 'randomForest'

The following object is masked from 'package:Biobase':

    combine

The following object is masked from 'package:BiocGenerics':

    combine

Loading required package: flexmix
Loading required package: lattice
> 
> data("rnaMetrics")
> 
> dataFrame <- stability(data=rnaMetrics, k=4, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 4
> dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,4), bs=20, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> assay(dataFrame)
     Metric    Mean_stability_k_2  Mean_stability_k_3  Mean_stability_k_4 
[1,] "RIN"     "0.825833333333333" "0.778412698412698" "0.69625"          
[2,] "DegFact" "0.955595238095238" "0.977777777777778" "0.820833333333333"
> # Metric    Mean_stability_k_2  Mean_stability_k_3  Mean_stability_k_4
> # [1,] "RIN"     "0.825833333333333" "0.778412698412698" "0.69625"
> # [2,] "DegFact" "0.955595238095238" "0.977777777777778" "0.820833333333333"
> dataFrame <- stabilitySet(data=rnaMetrics, k.set=c(2,3,4), bs=20, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> 
> dataFrame <- quality(data=rnaMetrics, cbi="kmeans", k=3, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 3
Processing metric: DegFact(2)
	Calculation of k = 3
> assay(dataFrame)
     Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore 
[1,] "RIN"     "0.420502645502646" "0.724044583696066" "0.68338517747747" 
[2,] "DegFact" "0.876516605981734" "0.643613928123002" "0.521618857725795"
     Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size
[1,] "0.627829396038413"  "4"            "4"            "8"           
[2,] "0.737191191352892"  "8"            "5"            "3"           
> # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore
> # [1,] "RIN"     "0.420502645502646" "0.724044583696066" "0.68338517747747"
> # [2,] "DegFact" "0.876516605981734" "0.643613928123002" "0.521618857725795"
> # Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size
> # [1,] "0.627829396038413"  "4"            "4"            "8"
> # [2,] "0.737191191352892"  "8"            "5"            "3"
> dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,4), seed = 20, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> assay(getDataQualityRange(dataFrame, 2))
  Metric    Cluster_1_SilScore  Cluster_2_SilScore  Avg_Silhouette_Width
1 "RIN"     "0.583166775069983" "0.619872562681118" "0.608402004052639" 
2 "DegFact" "0.664573423022171" "0.675315791048653" "0.666587617027136" 
  Cluster_1_Size Cluster_2_Size
1 "5"            "11"          
2 "13"           "3"           
> # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Avg_Silhouette_Width Cluster_1_Size
> # 1 "RIN"     "0.583166775069983" "0.619872562681118" "0.608402004052639"  "5"
> # 2 "DegFact" "0.664573423022171" "0.675315791048653" "0.666587617027136"  "13"
> # Cluster_2_Size
> # 1 "11"
> # 2 "3"
> assay(getDataQualityRange(dataFrame, 4))
  Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore 
1 "RIN"     "0.420502645502646" "0.674226581940152" "0.433333333333333"
2 "DegFact" "0.759196481622952" "0.59496499852177"  "0.600198799385732"
  Cluster_4_SilScore  Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size
1 "0.348714574898785" "0.463905611516569"  "4"            "4"           
2 "0.521618857725795" "0.634170498361632"  "5"            "3"           
  Cluster_3_Size Cluster_4_Size
1 "3"            "5"           
2 "5"            "3"           
> # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore
> # 1 "RIN"     "0.420502645502646" "0.674226581940152" "0.433333333333333"
> # 2 "DegFact" "0.759196481622952" "0.59496499852177"  "0.600198799385732"
> # Cluster_4_SilScore  Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size
> # 1 "0.348714574898785" "0.463905611516569"  "4"            "4"            "3"
> # 2 "0.521618857725795" "0.634170498361632"  "5"            "3"            "5"
> # Cluster_4_Size
> # 1 "5"
> # 2 "3"
> dataFrame1 <- qualitySet(data=rnaMetrics, k.set=c(2,3,4), getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> 
> 
> dataFrame <- metricsCorrelations(data=rnaMetrics, getImages = FALSE, margins = c(4,4,11,10))

Data loaded.
Number of rows: 16
Number of columns: 3


> assay(dataFrame, 1)
               RIN    DegFact
RIN      1.0000000 -0.9744685
DegFact -0.9744685  1.0000000
> 
> 
> dataFrame <- stability(data=rnaMetrics, cbi="kmeans", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="clara", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="clara_pam", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="hclust", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="pamk", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="pamk_pam", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> 
> # Supported CBIs:
> evaluomeRSupportedCBI()
[1] "kmeans"    "clara"     "clara_pam" "hclust"    "pamk"      "pamk_pam" 
> 
> dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,10), getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
	Calculation of k = 5
	Calculation of k = 6
	Calculation of k = 7
	Calculation of k = 8
	Calculation of k = 9
	Calculation of k = 10
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
	Calculation of k = 5
	Calculation of k = 6
	Calculation of k = 7
	Calculation of k = 8
	Calculation of k = 9
	Calculation of k = 10
> dataFrame
ExperimentList class object of length 9:
 [1] k_2: SummarizedExperiment with 2 rows and 6 columns
 [2] k_3: SummarizedExperiment with 2 rows and 8 columns
 [3] k_4: SummarizedExperiment with 2 rows and 10 columns
 [4] k_5: SummarizedExperiment with 2 rows and 12 columns
 [5] k_6: SummarizedExperiment with 2 rows and 14 columns
 [6] k_7: SummarizedExperiment with 2 rows and 16 columns
 [7] k_8: SummarizedExperiment with 2 rows and 18 columns
 [8] k_9: SummarizedExperiment with 2 rows and 20 columns
 [9] k_10: SummarizedExperiment with 2 rows and 22 columns
> 
> #dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,8), bs=20, getImages = FALSE)
> #assay(dataFrame)
> 
> proc.time()
   user  system elapsed 
  11.25    0.96   12.18

evaluomeR.Rcheck/tests_x64/testAll.Rout


R version 4.1.3 (2022-03-10) -- "One Push-Up"
Copyright (C) 2022 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.

> library(evaluomeR)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

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

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    I, expand.grid, unname

Loading required package: IRanges

Attaching package: 'IRanges'

The following object is masked from 'package:grDevices':

    windows

Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

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

    anyMissing, rowMedians

Loading required package: MultiAssayExperiment
Loading required package: cluster
Loading required package: fpc
Loading required package: randomForest
randomForest 4.7-1
Type rfNews() to see new features/changes/bug fixes.

Attaching package: 'randomForest'

The following object is masked from 'package:Biobase':

    combine

The following object is masked from 'package:BiocGenerics':

    combine

Loading required package: flexmix
Loading required package: lattice
> 
> data("rnaMetrics")
> 
> dataFrame <- stability(data=rnaMetrics, k=4, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 4
> dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,4), bs=20, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> assay(dataFrame)
     Metric    Mean_stability_k_2  Mean_stability_k_3  Mean_stability_k_4 
[1,] "RIN"     "0.825833333333333" "0.778412698412698" "0.69625"          
[2,] "DegFact" "0.955595238095238" "0.977777777777778" "0.820833333333333"
> # Metric    Mean_stability_k_2  Mean_stability_k_3  Mean_stability_k_4
> # [1,] "RIN"     "0.825833333333333" "0.778412698412698" "0.69625"
> # [2,] "DegFact" "0.955595238095238" "0.977777777777778" "0.820833333333333"
> dataFrame <- stabilitySet(data=rnaMetrics, k.set=c(2,3,4), bs=20, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> 
> dataFrame <- quality(data=rnaMetrics, cbi="kmeans", k=3, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 3
Processing metric: DegFact(2)
	Calculation of k = 3
> assay(dataFrame)
     Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore 
[1,] "RIN"     "0.420502645502646" "0.724044583696066" "0.68338517747747" 
[2,] "DegFact" "0.876516605981734" "0.643613928123002" "0.521618857725795"
     Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size
[1,] "0.627829396038413"  "4"            "4"            "8"           
[2,] "0.737191191352892"  "8"            "5"            "3"           
> # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore
> # [1,] "RIN"     "0.420502645502646" "0.724044583696066" "0.68338517747747"
> # [2,] "DegFact" "0.876516605981734" "0.643613928123002" "0.521618857725795"
> # Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size
> # [1,] "0.627829396038413"  "4"            "4"            "8"
> # [2,] "0.737191191352892"  "8"            "5"            "3"
> dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,4), seed = 20, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> assay(getDataQualityRange(dataFrame, 2))
  Metric    Cluster_1_SilScore  Cluster_2_SilScore  Avg_Silhouette_Width
1 "RIN"     "0.583166775069983" "0.619872562681118" "0.608402004052639" 
2 "DegFact" "0.664573423022171" "0.675315791048653" "0.666587617027136" 
  Cluster_1_Size Cluster_2_Size
1 "5"            "11"          
2 "13"           "3"           
> # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Avg_Silhouette_Width Cluster_1_Size
> # 1 "RIN"     "0.583166775069983" "0.619872562681118" "0.608402004052639"  "5"
> # 2 "DegFact" "0.664573423022171" "0.675315791048653" "0.666587617027136"  "13"
> # Cluster_2_Size
> # 1 "11"
> # 2 "3"
> assay(getDataQualityRange(dataFrame, 4))
  Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore 
1 "RIN"     "0.420502645502646" "0.674226581940152" "0.433333333333333"
2 "DegFact" "0.759196481622952" "0.59496499852177"  "0.600198799385732"
  Cluster_4_SilScore  Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size
1 "0.348714574898785" "0.463905611516569"  "4"            "4"           
2 "0.521618857725795" "0.634170498361632"  "5"            "3"           
  Cluster_3_Size Cluster_4_Size
1 "3"            "5"           
2 "5"            "3"           
> # Metric    Cluster_1_SilScore  Cluster_2_SilScore  Cluster_3_SilScore
> # 1 "RIN"     "0.420502645502646" "0.674226581940152" "0.433333333333333"
> # 2 "DegFact" "0.759196481622952" "0.59496499852177"  "0.600198799385732"
> # Cluster_4_SilScore  Avg_Silhouette_Width Cluster_1_Size Cluster_2_Size Cluster_3_Size
> # 1 "0.348714574898785" "0.463905611516569"  "4"            "4"            "3"
> # 2 "0.521618857725795" "0.634170498361632"  "5"            "3"            "5"
> # Cluster_4_Size
> # 1 "5"
> # 2 "3"
> dataFrame1 <- qualitySet(data=rnaMetrics, k.set=c(2,3,4), getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
> 
> 
> dataFrame <- metricsCorrelations(data=rnaMetrics, getImages = FALSE, margins = c(4,4,11,10))

Data loaded.
Number of rows: 16
Number of columns: 3


> assay(dataFrame, 1)
               RIN    DegFact
RIN      1.0000000 -0.9744685
DegFact -0.9744685  1.0000000
> 
> 
> dataFrame <- stability(data=rnaMetrics, cbi="kmeans", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="clara", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="clara_pam", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="hclust", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="pamk", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> dataFrame <- stability(data=rnaMetrics, cbi="pamk_pam", k=2, bs=100, getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
Processing metric: DegFact(2)
	Calculation of k = 2
> 
> # Supported CBIs:
> evaluomeRSupportedCBI()
[1] "kmeans"    "clara"     "clara_pam" "hclust"    "pamk"      "pamk_pam" 
> 
> dataFrame <- qualityRange(data=rnaMetrics, k.range=c(2,10), getImages = FALSE)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
	Calculation of k = 5
	Calculation of k = 6
	Calculation of k = 7
	Calculation of k = 8
	Calculation of k = 9
	Calculation of k = 10
Processing metric: DegFact(2)
	Calculation of k = 2
	Calculation of k = 3
	Calculation of k = 4
	Calculation of k = 5
	Calculation of k = 6
	Calculation of k = 7
	Calculation of k = 8
	Calculation of k = 9
	Calculation of k = 10
> dataFrame
ExperimentList class object of length 9:
 [1] k_2: SummarizedExperiment with 2 rows and 6 columns
 [2] k_3: SummarizedExperiment with 2 rows and 8 columns
 [3] k_4: SummarizedExperiment with 2 rows and 10 columns
 [4] k_5: SummarizedExperiment with 2 rows and 12 columns
 [5] k_6: SummarizedExperiment with 2 rows and 14 columns
 [6] k_7: SummarizedExperiment with 2 rows and 16 columns
 [7] k_8: SummarizedExperiment with 2 rows and 18 columns
 [8] k_9: SummarizedExperiment with 2 rows and 20 columns
 [9] k_10: SummarizedExperiment with 2 rows and 22 columns
> 
> #dataFrame <- stabilityRange(data=rnaMetrics, k.range=c(2,8), bs=20, getImages = FALSE)
> #assay(dataFrame)
> 
> proc.time()
   user  system elapsed 
  12.71    0.50   13.20

evaluomeR.Rcheck/tests_i386/testAnalysis.Rout


R version 4.1.3 (2022-03-10) -- "One Push-Up"
Copyright (C) 2022 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.

> library(evaluomeR)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

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

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    I, expand.grid, unname

Loading required package: IRanges

Attaching package: 'IRanges'

The following object is masked from 'package:grDevices':

    windows

Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

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

    anyMissing, rowMedians

Loading required package: MultiAssayExperiment
Loading required package: cluster
Loading required package: fpc
Loading required package: randomForest
randomForest 4.7-1
Type rfNews() to see new features/changes/bug fixes.

Attaching package: 'randomForest'

The following object is masked from 'package:Biobase':

    combine

The following object is masked from 'package:BiocGenerics':

    combine

Loading required package: flexmix
Loading required package: lattice
> 
> data("rnaMetrics")
> plotMetricsMinMax(rnaMetrics)
There were 17 warnings (use warnings() to see them)
> plotMetricsBoxplot(rnaMetrics)
Warning messages:
1: Use of `data.melt$variable` is discouraged. Use `variable` instead. 
2: Use of `data.melt$value` is discouraged. Use `value` instead. 
> cluster = plotMetricsCluster(ontMetrics, scale = TRUE)
> plotMetricsViolin(rnaMetrics)
Warning messages:
1: Use of `data.melt$variable` is discouraged. Use `variable` instead. 
2: Use of `data.melt$value` is discouraged. Use `value` instead. 
3: Use of `data.melt$variable` is discouraged. Use `variable` instead. 
4: Use of `data.melt$value` is discouraged. Use `value` instead. 
> 
> stabilityData <- stabilityRange(data=rnaMetrics, k.range=c(3,4), bs=20, getImages = FALSE, seed=100)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 3
	Calculation of k = 4
> qualityData <- qualityRange(data=rnaMetrics, k.range=c(3,4), getImages = FALSE, seed=100)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 3
	Calculation of k = 4
> 
> kOptTable <- getOptimalKValue(stabilityData, qualityData, k.range=c(3,4))
Processing metric: RIN

	Maximum stability and quality values matches the same K value: '3'

Processing metric: DegFact

	Maximum stability and quality values matches the same K value: '3'

> kOptTable
   Metric Stability_max_k Stability_max_k_stab Stability_max_k_qual
1     RIN               3            0.8901389            0.6278294
2 DegFact               3            1.0000000            0.7371912
  Quality_max_k Quality_max_k_stab Quality_max_k_qual Global_optimal_k
1             3          0.8901389          0.6278294                3
2             3          1.0000000          0.7371912                3
> 
> 
> df = assay(rnaMetrics)
> k.vector1=rep(5,length(colnames(df))-1)
> k.vector2=rep(2,length(colnames(df))-1)
> 
> plotMetricsClusterComparison(rnaMetrics, k.vector1=k.vector1, k.vector2=k.vector2)
> plotMetricsClusterComparison(rnaMetrics, k.vector1=3, k.vector2=c(2,5))
> 
> 
> proc.time()
   user  system elapsed 
   9.23    1.20   10.40

evaluomeR.Rcheck/tests_x64/testAnalysis.Rout


R version 4.1.3 (2022-03-10) -- "One Push-Up"
Copyright (C) 2022 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.

> library(evaluomeR)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'MatrixGenerics'

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

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

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

    IQR, mad, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: 'S4Vectors'

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

    I, expand.grid, unname

Loading required package: IRanges

Attaching package: 'IRanges'

The following object is masked from 'package:grDevices':

    windows

Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

The following object is masked from 'package:MatrixGenerics':

    rowMedians

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

    anyMissing, rowMedians

Loading required package: MultiAssayExperiment
Loading required package: cluster
Loading required package: fpc
Loading required package: randomForest
randomForest 4.7-1
Type rfNews() to see new features/changes/bug fixes.

Attaching package: 'randomForest'

The following object is masked from 'package:Biobase':

    combine

The following object is masked from 'package:BiocGenerics':

    combine

Loading required package: flexmix
Loading required package: lattice
> 
> data("rnaMetrics")
> plotMetricsMinMax(rnaMetrics)
There were 17 warnings (use warnings() to see them)
> plotMetricsBoxplot(rnaMetrics)
Warning messages:
1: Use of `data.melt$variable` is discouraged. Use `variable` instead. 
2: Use of `data.melt$value` is discouraged. Use `value` instead. 
> cluster = plotMetricsCluster(ontMetrics, scale = TRUE)
> plotMetricsViolin(rnaMetrics)
Warning messages:
1: Use of `data.melt$variable` is discouraged. Use `variable` instead. 
2: Use of `data.melt$value` is discouraged. Use `value` instead. 
3: Use of `data.melt$variable` is discouraged. Use `variable` instead. 
4: Use of `data.melt$value` is discouraged. Use `value` instead. 
> 
> stabilityData <- stabilityRange(data=rnaMetrics, k.range=c(3,4), bs=20, getImages = FALSE, seed=100)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 3
	Calculation of k = 4
> qualityData <- qualityRange(data=rnaMetrics, k.range=c(3,4), getImages = FALSE, seed=100)

Data loaded.
Number of rows: 16
Number of columns: 3


Processing metric: RIN(1)
	Calculation of k = 3
	Calculation of k = 4
Processing metric: DegFact(2)
	Calculation of k = 3
	Calculation of k = 4
> 
> kOptTable <- getOptimalKValue(stabilityData, qualityData, k.range=c(3,4))
Processing metric: RIN

	Maximum stability and quality values matches the same K value: '3'

Processing metric: DegFact

	Maximum stability and quality values matches the same K value: '3'

> kOptTable
   Metric Stability_max_k Stability_max_k_stab Stability_max_k_qual
1     RIN               3            0.8901389            0.6278294
2 DegFact               3            1.0000000            0.7371912
  Quality_max_k Quality_max_k_stab Quality_max_k_qual Global_optimal_k
1             3          0.8901389          0.6278294                3
2             3          1.0000000          0.7371912                3
> 
> 
> df = assay(rnaMetrics)
> k.vector1=rep(5,length(colnames(df))-1)
> k.vector2=rep(2,length(colnames(df))-1)
> 
> plotMetricsClusterComparison(rnaMetrics, k.vector1=k.vector1, k.vector2=k.vector2)
> plotMetricsClusterComparison(rnaMetrics, k.vector1=3, k.vector2=c(2,5))
> 
> 
> proc.time()
   user  system elapsed 
   9.10    0.45    9.54

Example timings

evaluomeR.Rcheck/examples_i386/evaluomeR-Ex.timings

nameusersystemelapsed
evaluomeRSupportedCBI000
getDataQualityRange0.450.020.50
getOptimalKValue0.450.010.47
globalMetric1.300.031.33
metricsCorrelations0.060.000.06
plotMetricsBoxplot0.410.040.44
plotMetricsCluster0.170.010.18
plotMetricsClusterComparison0.450.020.47
plotMetricsMinMax0.250.030.28
plotMetricsViolin1.200.051.25
quality0.290.000.29
qualityRange0.250.030.28
qualitySet0.040.020.07
stability2.220.002.21
stabilityRange2.750.012.77
stabilitySet0.470.020.48

evaluomeR.Rcheck/examples_x64/evaluomeR-Ex.timings

nameusersystemelapsed
evaluomeRSupportedCBI000
getDataQualityRange0.550.020.56
getOptimalKValue0.610.000.61
globalMetric1.890.001.89
metricsCorrelations0.040.010.06
plotMetricsBoxplot0.410.000.41
plotMetricsCluster0.190.030.22
plotMetricsClusterComparison0.670.040.70
plotMetricsMinMax0.300.010.31
plotMetricsViolin0.670.020.69
quality0.450.031.53
qualityRange0.390.000.39
qualitySet0.140.002.54
stability2.780.002.78
stabilityRange3.220.003.22
stabilitySet0.470.010.49