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SuperCurve log file (check_x86_64_linux)

Tue Nov 14 15:25:29 2017: Checking package SuperCurve (SVN revision 1000) ...
* using log directory ‘/mnt/building/build_2017-11-14-15-24/RF_PKG_CHECK/PKGS/SuperCurve.Rcheck’
* using R version 3.4.2 Patched (2017-11-10 r73707)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--as-cran’
* checking for file ‘SuperCurve/DESCRIPTION’ ... OK
* this is package ‘SuperCurve’ version ‘1.5.17’
* checking CRAN incoming feasibility ... NOTE
Maintainer: ‘"P. Roebuck" ’

New submission

Suggests or Enhances not in mainstream repositories:
  SuperCurveSampleData
* 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 executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘SuperCurve’ can be installed ... [5s/5s] OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... NOTE
'library' or 'require' calls in package code:
  ‘mgcv’ ‘quantreg’ ‘robustbase’ ‘timeDate’
  Please use :: or requireNamespace() instead.
  See section 'Suggested packages' in the 'Writing R Extensions' manual.
There are ::: calls to the package's namespace in its code. A package
  almost never needs to use ::: for its own objects:
  ‘.slide.model’ ‘layout’
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [17s/17s] NOTE
.coef.quantile.est: no visible global function definition for
  ‘quantile’
.computeBackgroundCutoff: no visible global function definition for
  ‘quantile’
.createFitGraphs: no visible global function definition for ‘par’
.est.bg.noise: no visible global function definition for ‘mad’
.firstPass: no visible global function definition for ‘median’
.generic.trim: no visible global function definition for ‘quantile’
.loess.line: no visible global function definition for ‘lines’
.loess.line: no visible global function definition for ‘approx’
.plotProbabilityOfGoodSlide: no visible global function definition for
  ‘mtext’
.plotProbabilityOfGoodSlide: no visible global function definition for
  ‘axis’
.plotProbabilityOfGoodSlide: no visible global function definition for
  ‘rect’
.plotProbabilityOfGoodSlide: no visible global function definition for
  ‘text’
.plotProbabilityOfGoodSlide: no visible global function definition for
  ‘abline’
.plotProbabilityOfGoodSlide: no visible global function definition for
  ‘points’
DS5RPPAPreFitQC : CV: no visible global function definition for ‘sd’
DS5RPPAPreFitQC : slopeDiff: no visible global function definition for
  ‘lm’
DS5RPPAPreFitQC: no visible global function definition for ‘lm’
RPPADesignFromParams: no visible global function definition for
  ‘median’
RPPAFitFromParams: no visible global function definition for ‘var’
RPPAPreFitQC: no visible binding for global variable ‘SpotType’
getConfidenceInterval: no visible global function definition for
  ‘rnorm’
getConfidenceInterval: no visible global function definition for
  ‘quantile’
normalize.house: no visible binding for global variable ‘median’
normalize.median: no visible binding for global variable ‘rowMedian’
normalize.medpolish: no visible global function definition for
  ‘medpolish’
normalize.vs: no visible binding for global variable ‘rowMedian’
normalize.vs : estimateGamma: no visible global function definition for
  ‘cor’
normalize.vs : estimateGamma: no visible global function definition for
  ‘pt’
normalize.vs : estimateGamma: no visible global function definition for
  ‘var’
rppaNormalize: no visible binding for global variable ‘median’
spatialCorrection: no visible global function definition for ‘par’
spatialCorrection : scaleBySurface: no visible global function
  definition for ‘median’
fitSlide,LogisticFitClass: no visible global function definition for
  ‘nls’
fitSlide,LogisticFitClass: no visible global function definition for
  ‘nls.control’
fitted,CobsFitClass: no visible global function definition for
  ‘predict’
fitted,LoessFitClass: no visible global function definition for
  ‘predict’
image,RPPA: no visible global function definition for ‘par’
image,RPPA: no visible global function definition for ‘axis’
image,RPPA: no visible global function definition for ‘box’
image,RPPA: no visible global function definition for ‘abline’
image,RPPADesign: no visible global function definition for ‘axis’
image,RPPADesign: no visible global function definition for ‘abline’
plot,RPPA-RPPADesign: no visible global function definition for ‘par’
plot,RPPA-RPPADesign: no visible global function definition for ‘lines’
plot,RPPAFit-missing: no visible global function definition for ‘lines’
plot,RPPAFit-missing: no visible global function definition for
  ‘abline’
plot,RPPAFit-missing: no visible global function definition for ‘title’
plot,RPPAFit-missing: no visible global function definition for
  ‘points’
plot,RPPAFit-missing: no visible global function definition for
  ‘legend’
plot,RPPAFit-missing: no visible global function definition for ‘lm’
residuals,RPPAFit: no visible global function definition for ‘var’
Undefined global functions or variables:
  SpotType abline approx axis box cor legend lines lm mad median
  medpolish mtext nls nls.control par points predict pt quantile rect
  rnorm rowMedian sd text title var
Consider adding
  importFrom("graphics", "abline", "axis", "box", "legend", "lines",
             "mtext", "par", "points", "rect", "text", "title")
  importFrom("stats", "approx", "cor", "lm", "mad", "median",
             "medpolish", "nls", "nls.control", "predict", "pt",
             "quantile", "rnorm", "sd", "var")
to your NAMESPACE file.

Found the following assignments to the global environment:
File ‘SuperCurve/R/sc3-0-dilutionFitMethods.R’:
  assign(".RPPA.fit.model", object, envir = .GlobalEnv)
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... WARNING
Codoc mismatches from documentation object 'normalize':
\S4method{normalize}{MatrixLike}
  Code: function(object, method =
                 SuperCurve::getRegisteredNormalizationMethodKeys(),
                 calc.medians = TRUE, sweep.cols = calc.medians, ...)
  Docs: function(object, method =
                 getRegisteredNormalizationMethodKeys(), calc.medians =
                 TRUE, sweep.cols = calc.medians, ...)
  Mismatches in argument default values:
    Name: 'method' Code: SuperCurve::getRegisteredNormalizationMethodKeys() Docs: getRegisteredNormalizationMethodKeys()

* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking sizes of PDF files under ‘inst/doc’ ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... [12s/12s] NOTE
Examples with CPU or elapsed time > 5s
                        user system elapsed
sc14-spatialCorrection 4.908  0.072   5.023
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘sessionInfo.R’
  Comparing ‘sessionInfo.Rout’ to ‘sessionInfo.Rout.save’ ...33,39c33,34
< R version 3.4.2 Patched (2017-11-10 r73707)
< Platform: x86_64-pc-linux-gnu (64-bit)
< Running under: Debian GNU/Linux 8 (jessie)
< 
< Matrix products: default
< BLAS: /srv/R/R-patched/build.17-11-12/lib/libRblas.so
< LAPACK: /srv/R/R-patched/build.17-11-12/lib/libRlapack.so
---
> R version 2.15.3 Patched (2013-03-13 r62287)
> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
42,47c37
<  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
<  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
<  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
<  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
<  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
< [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
---
> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
54,58c44,48
<  [1] SuperCurveSampleData_1.0.2 timeDate_3012.100         
<  [3] robustbase_0.92-8          quantreg_5.34             
<  [5] SparseM_1.77               mgcv_1.8-22               
<  [7] nlme_3.1-131               boot_1.3-20               
<  [9] SuperCurve_1.5.17          cobs_1.3-3                
---
>  [1] SuperCurveSampleData_1.0.1 timeDate_3011.99          
>  [3] robustbase_0.92-2          quantreg_5.05             
>  [5] SparseM_1.6                mgcv_1.8-4                
>  [7] nlme_3.1-108               boot_1.3-7                
>  [9] SuperCurve_1.5.8           cobs_1.3-0                
61,62c51,52
< [1] lattice_0.20-35    MASS_7.3-47        grid_3.4.2         MatrixModels_0.4-2
< [5] Matrix_1.2-11      DEoptimR_1.0-8     compiler_3.4.2    
---
> [1] DEoptimR_1.0-2  MASS_7.3-23     Matrix_1.1-4    grid_2.15.3    
> [5] lattice_0.20-29
  Running ‘testCobs.R’
  Comparing ‘testCobs.Rout’ to ‘testCobs.Rout.save’ ...16,19c16,293
< >>>>                <<<< 
< >>>>  Test skipped  <<<< 
< >>>>                <<<< 
< To run all package tests, define "SUPERCURVE_FULL_TEST" environment variable
---
> > options(warn=1)
> > library(SuperCurve)
> > library(robustbase)
> > 
> > extdata.dir <- system.file("extdata", package="SuperCurveSampleData")
> > 
> > ######################################
> > ## load the tumor data
> > home <- file.path(extdata.dir, "rppaTumorData")
> > 
> > ## first locate the list of assays
> > ## the name 'proteins' is required
> > ## must include two columns named 'Antibody' and 'Filename'.
> > proteins <- read.delim(file.path(home, "proteinAssay.tsv"), as.is=TRUE)
> > rownames(proteins) <- as.character(proteins$Antibody)
> > 
> > for (i in seq_len(nrow(proteins))) {
> +     temp <- RPPA(proteins$Filename[i],
> +                  path=home,
> +                  antibody=proteins$Antibody[i])
> +     assign(proteins$Antibody[i], temp, 1)
> + }
> > remove(i, temp)
> > 
> > ######################################
> > ## work out the appropriate design layout
> > design <- RPPADesign(ERK2,
> +                      grouping="blockSample",
> +                      center=TRUE,
> +                      controls=list("neg con", "pos con"))
> > 
> > ######################################
> > ## must define the 'model' to use
> > model <- "cobs"
> > 
> > ######################################
> > ## must define the 'measure' to use
> > measure <- "Mean.Net"
> > 
> > ######################################
> > ## must define the 'method' to use
> > method <- 'nlrq'
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = cobs; method = nlrq; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> 
> Attaching package: 'SparseM'
> 
> The following object(s) are masked from 'package:base':
> 
>     backsolve
> 
> [1] "ERK2 Mean.Net 295246 121653"
> [1] "GSK3 Mean.Net 297740 123763"
> [1] "JNK Mean.Net 333207 101898"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > method <- 'nlrob'
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = cobs; method = nlrob; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> [1] "ERK2 Mean.Net 263983 124538"
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> [1] "GSK3 Mean.Net 291770 124061"
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> [1] "JNK Mean.Net 284123 104377"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > method <- "nls"
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = cobs; method = nls; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> [1] "ERK2 Mean.Net 257063 130929"
> [1] "GSK3 Mean.Net 258045 136270"
> [1] "JNK Mean.Net 273147 114311"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > ######################################
> > ## print the concentrations from the last fit. These will
> > ## automatically be compared with the saved output when we
> > ## run 'R CMD check'
> > round(temp@concentrations, digits=4)
>   sample 1.1.1   sample 2.1.1   sample 3.1.2   sample 4.1.2   sample 5.1.3 
>        -4.0811        -7.6974        -3.3025        -3.9834        -3.9924 
>   sample 6.1.3   sample 7.1.4   sample 8.1.4   sample 9.1.5  sample 10.1.5 
>        -7.4559        -4.0671        -7.2802        -4.9620        -8.9690 
>  sample 11.1.6  sample 12.1.6  sample 13.1.7  sample 14.1.7  sample 15.1.8 
>        -5.0436        -9.2994        -5.4855        -7.0199        -7.9932 
>  sample 16.1.8  sample 17.1.9  sample 18.1.9 sample 19.1.10 sample 20.1.10 
>        -4.3036        -3.6753        -4.4229        -2.1163        -7.8233 
> sample 21.1.11 sample 22.1.11 sample 23.1.12 sample 24.1.12  sample 25.2.1 
>        -4.4619        -5.8950        -1.2669        -6.2549        -2.9121 
>  sample 26.2.1  sample 27.2.2  sample 28.2.2  sample 29.2.3  sample 30.2.3 
>        -8.6760        -3.1489        -6.5158        -2.3676        -9.2994 
>  sample 31.2.4  sample 32.2.4  sample 33.2.5  sample 34.2.5  sample 35.2.6 
>        -4.0385        -6.0364        -3.2396        -5.1490        -3.3325 
>  sample 36.2.6  sample 37.2.7  sample 38.2.7  sample 39.2.8  sample 40.2.8 
>        -3.8885        -3.1190        -3.6320        -0.6942        -7.3687 
>  sample 41.2.9  sample 42.2.9 sample 43.2.10 sample 44.2.10 sample 45.2.11 
>        -3.1086        -3.4728        -1.0455        -4.5497        -2.6940 
> sample 46.2.11 sample 47.2.12 sample 48.2.12  sample 49.3.1  sample 50.3.1 
>        -8.0472        -0.7377        -1.9651        -3.0461        -3.9900 
>  sample 51.3.2  sample 52.3.2  sample 53.3.3  sample 54.3.3  sample 55.3.4 
>        -3.0364        -5.8671        -2.6317        -3.5379        -3.2286 
>  sample 56.3.4  sample 57.3.5  sample 58.3.5  sample 59.3.6  sample 60.3.6 
>        -5.8460        -2.4706        -3.9862        -3.2859        -4.3974 
>  sample 61.3.7  sample 62.3.7  sample 63.3.8  sample 64.3.8  sample 65.3.9 
>        -2.5938        -3.6830        -3.4970        -6.6738        -2.8363 
>  sample 66.3.9 sample 67.3.10 sample 68.3.10 sample 69.3.11 sample 70.3.11 
>        -6.3722        -6.5586        -6.7842        -2.7708        -4.3201 
> sample 71.3.12 sample 72.3.12  sample 73.4.1  sample 74.4.1  sample 75.4.2 
>        -4.5702        -7.5675        -3.8352        -6.5253        -8.0721 
>  sample 76.4.2  sample 77.4.3  sample 78.4.3  sample 79.4.4  sample 80.4.4 
>        -7.9626        -4.8128        -4.7216        -0.9142        -3.2661 
>  sample 81.4.5  sample 82.4.5  sample 83.4.6  sample 84.4.6  sample 85.4.7 
>        -4.3757        -7.6829        -3.2964        -4.6277        -6.3757 
>  sample 86.4.7  sample 87.4.8  sample 88.4.8  sample 89.4.9  sample 90.4.9 
>        -6.0699        -5.4793        -8.3355        -4.7962        -4.4836 
> sample 91.4.10 sample 92.4.10 sample 93.4.11 sample 94.4.11 sample 95.4.12 
>        -3.1734        -6.7587        -3.8599        -5.4981        -7.4548 
> sample 96.4.12 
>        -9.2994 
> > 
> > 
  Running ‘testDesign.R’
  Comparing ‘testDesign.Rout’ to ‘testDesign.Rout.save’ ... OK
  Running ‘testFit.R’ [128s/128s]
  Comparing ‘testFit.Rout’ to ‘testFit.Rout.save’ ...19c19
< The following object is masked from 'package:robustbase':
---
> The following object(s) are masked from 'package:robustbase':
42c42
< Error in RPPAFitParams() : argument "measure" is missing, with no default
---
> Error in is.character(measure) : 'measure' is missing
156c156
< The following object is masked from 'package:base':
---
> The following object(s) are masked from 'package:base':
  Running ‘testLoess.R’
  Comparing ‘testLoess.Rout’ to ‘testLoess.Rout.save’ ...16,19c16,369
< >>>>                <<<< 
< >>>>  Test skipped  <<<< 
< >>>>                <<<< 
< To run all package tests, define "SUPERCURVE_FULL_TEST" environment variable
---
> > options(warn=1)
> > library(SuperCurve)
> > library(robustbase)
> > 
> > extdata.dir <- system.file("extdata", package="SuperCurveSampleData")
> > 
> > ######################################
> > ## load the tumor data
> > home <- file.path(extdata.dir, "rppaTripleData")
> > 
> > ## first locate the list of assays
> > ## the name 'proteins' is required
> > ## must include two columns named 'Antibody' and 'Filename'.
> > proteins <- read.delim(file.path(home, "proteinAssay.tsv"), as.is=TRUE)
> > rownames(proteins) <- as.character(proteins$Antibody)
> > 
> > for (i in seq_len(nrow(proteins))) {
> +     temp <- RPPA(proteins$Filename[i],
> +                  path=home,
> +                  antibody=proteins$Antibody[i])
> +     assign(proteins$Antibody[i], temp, 1)
> + }
> > remove(i, temp)
> > 
> > ######################################
> > ## work out the appropriate design layout
> > design <- RPPADesign(ACTB)
> > 
> > ######################################
> > ## must define the 'model' to use
> > model <- "loess"
> > 
> > ######################################
> > ## must define the 'measure' to use
> > measure <- "Mean.Net"
> > 
> > ######################################
> > ## loess is very slow; we are only going to test
> > ## a single protein.
> > proteins <- proteins[nrow(proteins), ]
> > 
> > ######################################
> > ## must define the 'method' to use
> > method <- 'nlrq'
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = loess; method = nlrq; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> 
> Attaching package: 'SparseM'
> 
> The following object(s) are masked from 'package:base':
> 
>     backsolve
> 
> [1] "ACTB-gray Mean.Net 469690 205795"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > method <- 'nlrob'
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = loess; method = nlrob; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> [1] "ACTB-gray Mean.Net 438465 212956"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > method <- "nls"
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = loess; method = nls; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> [1] "ACTB-gray Mean.Net 421580 234844"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > ######################################
> > ## print the concentrations from the last fit. These will
> > ## automatically be compared with the saved output when we
> > ## run 'R CMD check'
> > round(temp@concentrations, digits=4)
>  Series.1.1.1  Series.1.1.2  Series.1.1.3  Series.1.1.4  Series.1.1.5 
>       -0.8602       -0.5906       -0.7306        1.9298        1.9597 
>  Series.1.1.6  Series.1.2.1  Series.1.2.2  Series.1.2.3  Series.1.2.4 
>        2.2992       -0.0766       -0.0985        0.2027        2.8092 
>  Series.1.2.5  Series.1.2.6  Series.1.3.1  Series.1.3.2  Series.1.3.3 
>        3.2177        3.2285        1.9441        2.2368        1.9823 
>  Series.1.3.4  Series.1.3.5  Series.1.3.6  Series.1.4.1  Series.1.4.2 
>        2.9777        2.5031        2.9114        3.3828        3.6138 
>  Series.1.4.3  Series.1.4.4  Series.1.4.5  Series.1.4.6  Series.1.5.1 
>        3.5499        3.5921        3.6464        4.0593        3.1576 
>  Series.1.5.2  Series.1.5.3  Series.1.5.4  Series.1.5.5  Series.1.5.6 
>        3.2333        2.9223        2.9495        3.1347        3.2423 
>  Series.1.6.1  Series.1.6.2  Series.1.6.3  Series.1.6.4  Series.1.6.5 
>        3.6149        3.9160        3.6874        3.3865        3.5561 
>  Series.1.6.6  Series.1.7.1  Series.1.7.2  Series.1.7.3  Series.1.7.4 
>        3.8625        3.8820        4.0591        3.8911        7.2820 
>  Series.1.7.5  Series.1.7.6  Series.1.8.1  Series.1.8.2  Series.1.8.3 
>        6.6822        6.8652        4.7676        5.1979        4.7265 
>  Series.1.8.4  Series.1.8.5  Series.1.8.6  Series.1.9.1  Series.1.9.2 
>        3.7238        3.8765        4.1746        4.5535        4.5426 
>  Series.1.9.3  Series.1.9.4  Series.1.9.5  Series.1.9.6 Series.1.10.1 
>        4.5674        6.8556        7.3246        7.5989        4.1443 
> Series.1.10.2 Series.1.10.3 Series.1.10.4 Series.1.10.5 Series.1.10.6 
>        4.6125        4.4950        4.3480        3.9703        4.5727 
> Series.1.11.1 Series.1.11.2 Series.1.11.3 Series.1.11.4 Series.1.11.5 
>        2.9584        3.1721        2.9397        3.0561        2.7478 
> Series.1.11.6 Series.1.12.1 Series.1.12.2 Series.1.12.3 Series.1.12.4 
>        3.1182        1.7063        1.7571        1.7199        0.9924 
> Series.1.12.5 Series.1.12.6  Series.2.1.1  Series.2.1.2  Series.2.1.3 
>        0.5025        1.1475        1.9074        1.6779        2.0198 
>  Series.2.1.4  Series.2.1.5  Series.2.1.6  Series.2.2.1  Series.2.2.2 
>       -1.0751       -0.2828       -1.1889        2.1312        1.9446 
>  Series.2.2.3  Series.2.2.4  Series.2.2.5  Series.2.2.6  Series.2.3.1 
>        1.8413        1.8101        1.5698        1.7560        2.9397 
>  Series.2.3.2  Series.2.3.3  Series.2.3.4  Series.2.3.5  Series.2.3.6 
>        2.7681        2.7168        1.3844        1.4297        1.2760 
>  Series.2.4.1  Series.2.4.2  Series.2.4.3  Series.2.4.4  Series.2.4.5 
>        4.7562        4.5977        4.3986        4.7928        4.9474 
>  Series.2.4.6  Series.2.5.1  Series.2.5.2  Series.2.5.3  Series.2.5.4 
>        5.1598        4.7345        4.7211        4.4231        4.6817 
>  Series.2.5.5  Series.2.5.6  Series.2.6.1  Series.2.6.2  Series.2.6.3 
>        4.5105        4.4279        6.1930        5.6408        5.3673 
>  Series.2.6.4  Series.2.6.5  Series.2.6.6  Series.2.7.1  Series.2.7.2 
>        5.1603        5.0483        4.8821        6.4825        6.4761 
>  Series.2.7.3  Series.2.7.4  Series.2.7.5  Series.2.7.6  Series.2.8.1 
>        6.5935        4.2292        3.9509        3.7344        6.4995 
>  Series.2.8.2  Series.2.8.3  Series.2.8.4  Series.2.8.5  Series.2.8.6 
>        6.3415        6.0467        4.2122        4.0063        3.9265 
>  Series.2.9.1  Series.2.9.2  Series.2.9.3  Series.2.9.4  Series.2.9.5 
>        5.8043        5.2290        5.0294        3.9849        3.9305 
>  Series.2.9.6 Series.2.10.1 Series.2.10.2 Series.2.10.3 Series.2.10.4 
>        3.9001       -0.2383       -0.3098       -0.5901       -0.0632 
> Series.2.10.5 Series.2.10.6 Series.2.11.1 Series.2.11.2 Series.2.11.3 
>       -0.3272       -0.4103       -0.2294       -0.4625       -0.4174 
> Series.2.11.4 Series.2.11.5 Series.2.11.6 Series.2.12.1 Series.2.12.2 
>       -0.8695       -1.1522       -1.3751       -0.7454       -1.2136 
> Series.2.12.3 Series.2.12.4 Series.2.12.5 Series.2.12.6  Series.3.1.1 
>       -1.0312       -3.2044       -3.1881       -2.7890        0.0310 
>  Series.3.1.2  Series.3.1.3  Series.3.1.4  Series.3.1.5  Series.3.1.6 
>        0.5650        0.6239        2.5525        2.3076        2.1425 
>  Series.3.2.1  Series.3.2.2  Series.3.2.3  Series.3.2.4  Series.3.2.5 
>        3.4380        3.3023        3.1705        4.0556        4.0233 
>  Series.3.2.6  Series.3.3.1  Series.3.3.2  Series.3.3.3  Series.3.3.4 
>        4.0407        3.5934        3.5800        3.2331        5.4542 
>  Series.3.3.5  Series.3.3.6  Series.3.4.1  Series.3.4.2  Series.3.4.3 
>        5.1599        5.4712        5.5806        5.1363        4.9577 
>  Series.3.4.4  Series.3.4.5  Series.3.4.6  Series.3.5.1  Series.3.5.2 
>        5.0053        4.6410        4.8304        4.5422        4.5434 
>  Series.3.5.3  Series.3.5.4  Series.3.5.5  Series.3.5.6  Series.3.6.1 
>        4.3845        4.1813        4.3831        4.4786        5.5073 
>  Series.3.6.2  Series.3.6.3  Series.3.6.4  Series.3.6.5  Series.3.6.6 
>        5.6549        5.3021        4.5688        5.0839        5.0391 
>  Series.3.7.1  Series.3.7.2  Series.3.7.3  Series.3.7.4  Series.3.7.5 
>        5.2775        4.6508        4.7517        7.8111        7.6375 
>  Series.3.7.6  Series.3.8.1  Series.3.8.2  Series.3.8.3  Series.3.8.4 
>        8.1827        5.0843        4.7216        4.3684        6.9000 
>  Series.3.8.5  Series.3.8.6  Series.3.9.1  Series.3.9.2  Series.3.9.3 
>        7.0314        7.5694        4.0392        4.2468        3.9571 
>  Series.3.9.4  Series.3.9.5  Series.3.9.6 Series.3.10.1 Series.3.10.2 
>        7.6208        8.4689        9.0228        4.0383        4.0884 
> Series.3.10.3 Series.3.10.4 Series.3.10.5 Series.3.10.6 Series.3.11.1 
>        4.1733        3.5459        4.2132        3.7448        3.6395 
> Series.3.11.2 Series.3.11.3 Series.3.11.4 Series.3.11.5 Series.3.11.6 
>        3.8334        3.7254        2.9809        2.8519        3.2691 
> Series.3.12.1 Series.3.12.2 Series.3.12.3 Series.3.12.4 Series.3.12.5 
>        2.7177        2.9806        2.7030        2.5105        2.8469 
> Series.3.12.6  Series.4.1.1  Series.4.1.2  Series.4.1.3  Series.4.1.4 
>        2.7346        1.3324        1.5012        1.3320        1.7609 
>  Series.4.1.5  Series.4.1.6  Series.4.2.1  Series.4.2.2  Series.4.2.3 
>        0.9723        1.4843        3.4579        3.3962        3.3801 
>  Series.4.2.4  Series.4.2.5  Series.4.2.6  Series.4.3.1  Series.4.3.2 
>        3.1690        3.1007        2.6054        4.1886        3.8864 
>  Series.4.3.3  Series.4.3.4  Series.4.3.5  Series.4.3.6  Series.4.4.1 
>        3.1904        3.4127        3.2599        2.7650        7.2229 
>  Series.4.4.2  Series.4.4.3  Series.4.4.4  Series.4.4.5  Series.4.4.6 
>        6.7795        6.1075        6.8747        6.4518        5.7240 
>  Series.4.5.1  Series.4.5.2  Series.4.5.3  Series.4.5.4  Series.4.5.5 
>        5.1526        5.0230        4.7089        5.5965        5.1155 
>  Series.4.5.6  Series.4.6.1  Series.4.6.2  Series.4.6.3  Series.4.6.4 
>        4.8510        4.4937        4.6357        4.5758        4.9750 
>  Series.4.6.5  Series.4.6.6  Series.4.7.1  Series.4.7.2  Series.4.7.3 
>        4.7688        4.6433        7.3814        7.5395        7.1163 
>  Series.4.7.4  Series.4.7.5  Series.4.7.6  Series.4.8.1  Series.4.8.2 
>        5.3279        5.0409        4.4522        7.0404        7.2365 
>  Series.4.8.3  Series.4.8.4  Series.4.8.5  Series.4.8.6  Series.4.9.1 
>        6.9942        5.7698        5.5294        5.1406        7.3507 
>  Series.4.9.2  Series.4.9.3  Series.4.9.4  Series.4.9.5  Series.4.9.6 
>        7.4498        7.1456        5.5059        4.9892        4.4154 
> Series.4.10.1 Series.4.10.2 Series.4.10.3 Series.4.10.4 Series.4.10.5 
>       -1.5823       -1.1667       -1.3396       -1.8055       -1.7891 
> Series.4.10.6 Series.4.11.1 Series.4.11.2 Series.4.11.3 Series.4.11.4 
>       -1.7296       -1.9226       -1.9552       -1.6579       -1.7498 
> Series.4.11.5 Series.4.11.6 Series.4.12.1 Series.4.12.2 Series.4.12.3 
>       -2.1154       -3.0667       -4.2887       -3.7101       -4.2395 
> Series.4.12.4 Series.4.12.5 Series.4.12.6 
>       -2.7825       -3.5347       -3.8492 
> > 
> > ######################################
> > ## This early design had each series repeated nine times,
> > ## three times within a subgrid and again in three adjacent
> > ## subgrids. Here we measure the variability of the
> > ## replicates.
> > d <- ACTB@data[seq(6, nrow(ACTB@data), by=6), ]
> > attach(d)
> > foo <- paste("Series", Main.Row, Main.Col, Sub.Row, sep=".")
> > detach()
> > sum(foo == seriesNames(design))
> [1] 288
> > 
> > avgs <- tapply(temp@concentrations, list(d$Sample), mean)
> > spread <- tapply(temp@concentrations, list(d$Sample), sd)
> > res <- data.frame(Mean=avgs, SD=spread, CV=spread/abs(avgs))
> > round(res[order(res$SD), ], digits=4)
>                       Mean     SD     CV
> G26                 3.9861 0.1538 0.0386
> U251 10%            7.2505 0.1902 0.0262
> KM12L4              4.8456 0.2662 0.0549
> PASW 480 10%01      3.4531 0.3068 0.0888
> L3.6 10%            4.6902 0.3184 0.0679
> L3.6                3.4922 0.3522 0.1009
> SNB19               4.4653 0.4409 0.0987
> SNB19 10%           4.5664 0.4514 0.0988
> MDA468              2.2163 0.4642 0.2094
> G26 10%             5.1302 0.4673 0.0911
> PASW 480 10%01 10%  5.0677 0.4861 0.0959
> MDA231              2.6485 0.4997 0.1887
> SW 480 10%          3.1886 0.5514 0.1729
> U251                6.0558 0.5838 0.0964
> U87 EGFR 10%        3.5444 0.5884 0.1660
> KM12C               4.9814 0.6154 0.1235
> U87 10%             7.8049 0.6710 0.0860
> KM12L4 10%          5.4445 0.7859 0.1443
> BT474 10%           2.5034 0.8812 0.3520
> Buffer             -1.0343 1.0071 0.9738
> KM12C 10%           5.4111 1.0302 0.1904
> Blank              -2.4603 1.0446 0.4246
> MDA468 10%          2.8517 1.1378 0.3990
> U87 EGFR 0.5        3.0561 1.1730 0.3838
> BT474               0.7421 1.2301 1.6576
> MCF7                0.4455 1.2552 2.8177
> MDA231 10%          3.9119 1.3215 0.3378
> MCF7 10%            2.3930 1.5054 0.6291
> K562                2.7173 1.5122 0.5565
> U87                 6.0426 1.6166 0.2675
> > 
> > 
  Running ‘testLogistic.R’
  Comparing ‘testLogistic.Rout’ to ‘testLogistic.Rout.save’ ...16,19c16,330
< >>>>                <<<< 
< >>>>  Test skipped  <<<< 
< >>>>                <<<< 
< To run all package tests, define "SUPERCURVE_FULL_TEST" environment variable
---
> > options(warn=1)
> > library(SuperCurve)
> > library(robustbase)
> > library(boot)
> 
> Attaching package: 'boot'
> 
> The following object(s) are masked from 'package:robustbase':
> 
>     salinity
> 
> > 
> > extdata.dir <- system.file("extdata", package="SuperCurveSampleData")
> > 
> > ######################################
> > ## load the data from the 40 cell lines
> > home <- file.path(extdata.dir, "rppaCellData")
> > 
> > ## first locate the list of assays
> > ## the name 'proteins' is required
> > ## must include two columns named 'Antibody' and 'Filename'.
> > proteins <- read.delim(file.path(home, "proteinAssay.tsv"), as.is=TRUE)
> > rownames(proteins) <- as.character(proteins$Antibody)
> > 
> > for (i in seq_len(nrow(proteins))) {
> +     temp <- RPPA(proteins$Filename[i],
> +                  path=home,
> +                  antibody=proteins$Antibody[i])
> +     assign(proteins$Antibody[i], temp, 1)
> + }
> > remove(i, temp)
> > 
> > ######################################
> > ## work out the appropriate design layout
> > 
> > steps <- rep(c(rep(8:5, 2), rep(4:1, 2)), 40) - 4.5
> > rep.temp <- factor(paste("Rep", rep(rep(1:2, each=4), 80), sep=""))
> > series <- factor(paste(as.character(AKT@data$Sample),
> +                        as.character(rep.temp),
> +                        sep="."))
> > ## the name 'design' is required'
> > design <- RPPADesign(AKT, steps=steps, series=series)
> > remove(steps, rep.temp, series)
> > 
> > ######################################
> > ## must define the 'model' to use
> > model <- "logistic"
> > 
> > ######################################
> > ## must define the 'measure' to use
> > measure <- "Mean.Net"
> > 
> > ######################################
> > ## must define the 'method' to use
> > method <- "nlrq"
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = logistic; method = nlrq; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> 
> Attaching package: 'SparseM'
> 
> The following object(s) are masked from 'package:base':
> 
>     backsolve
> 
> [1] "AKT Mean.Net 1544083 692611"
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> Warning in lsfit(x, y, d^2, intercept = FALSE) :
>   'X' matrix was collinear
> Warning in lsfit(x, y, d^2, intercept = FALSE) :
>   'X' matrix was collinear
> [1] "ERK2 Mean.Net 6277287 2330149"
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> [1] "CTNNB1 Mean.Net 4737490 928624"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > method <- "nlrob"
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = logistic; method = nlrob; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> [1] "AKT Mean.Net 1457191 689323"
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> [1] "ERK2 Mean.Net 6139545 2305429"
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> Warning in nlsmeth(Y ~ SuperCurve:::.slide.model(Steps + X), data = data.frame(Y = intensity,  :
>   failed to converge in 20 steps
> [1] "CTNNB1 Mean.Net 3931938 1054908"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > method <- "nls"
> > source("testRblock", echo=TRUE, max.deparse.len=1024)
> 
> > ver <- packageDescription("SuperCurve", fields = "Version")
> 
> > cat(paste("\nTest of SuperCurve version", ver), "\n")
> 
> Test of SuperCurve version 1.5.8 
> 
> > cat(paste("model = ", model, "; method = ", method, 
> +     "; measure = ", measure, sep = ""), "\n")
> model = logistic; method = nls; measure = Mean.Net 
> 
> > for (i in seq_len(nrow(proteins))) {
> +     itemName <- proteins$Antibody[i]
> +     item <- eval(as.name(itemName))
> +     temp <- try(RPPAFit(item, design, measure, method = method, 
> +         model = model, ignoreNegative = FALSE, warnLevel = -1), 
> +         TRUE)
> +     if (is(temp, "try-error")) {
> +         print(paste(itemName, measure, ver, "FAILED"))
> +         next
> +     }
> +     meansq <- mean(resid(temp)^2, na.rm = TRUE)
> +     tmeansq <- mean(resid(temp)^2, na.rm = TRUE, trim = 0.1)
> +     par(bg = "white")
> +     plot(temp, xlim = c(-15, 15), main = paste(itemName, measure, 
> +         ver), sub = paste("Mean square residuals =", round(meansq)))
> +     if (FALSE) {
> +         dev.copy(png, file.path(paste(itemName, measure, ver, 
> +             "png", sep = ".")), width = 640, height = 640)
> +         dev.off()
> +     }
> +     print(paste(itemName, measure, round(meansq), round(tmeansq)))
> + }
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> [1] "AKT Mean.Net 1431296 731954"
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> [1] "ERK2 Mean.Net 6139134 2305947"
> Warning in RPPAFitFromParams(rppa, design, params) :
>   number of sample labels in design (80) does not match number of sample labels given in raw RPPA quantification file (40).
> [1] "CTNNB1 Mean.Net 3513461 1285953"
> 
> > remove(i, itemName, item, meansq, tmeansq)
> > 
> > ######################################
> > ## print the concentrations from the last fit. These will
> > ## automatically be compared with the saved output when we
> > ## run 'R CMD check'
> > round(temp@concentrations, digits=4)
>   sample1.Rep1   sample1.Rep2   sample2.Rep1   sample2.Rep2   sample3.Rep1 
>        -5.1892        -4.5712        -3.5405        -3.5980        -4.0855 
>   sample3.Rep2   sample4.Rep1   sample4.Rep2   sample5.Rep1   sample5.Rep2 
>        -4.1104        -6.7238        -6.8595        -6.8595        -6.8595 
>   sample6.Rep1   sample6.Rep2   sample7.Rep1   sample7.Rep2   sample8.Rep1 
>        -3.5128        -3.4557        -5.2420        -5.1163        -6.1570 
>   sample8.Rep2   sample9.Rep1   sample9.Rep2  sample10.Rep1  sample10.Rep2 
>        -6.0861        -4.6253        -4.5410        -4.0139        -3.6981 
>  sample11.Rep1  sample11.Rep2  sample12.Rep1  sample12.Rep2  sample13.Rep1 
>        -3.5221        -3.8266        -6.8595        -6.8595        -3.5580 
>  sample13.Rep2  sample14.Rep1  sample14.Rep2  sample15.Rep1  sample15.Rep2 
>        -4.1612        -3.3573        -3.6340        -3.8601        -4.0298 
>  sample16.Rep1  sample16.Rep2  sample17.Rep1  sample17.Rep2  sample18.Rep1 
>        -3.0052        -2.5149        -3.5660        -3.8485        -4.6309 
>  sample18.Rep2  sample19.Rep1  sample19.Rep2  sample20.Rep1  sample20.Rep2 
>        -3.8807        -2.8514        -2.8337        -3.5091        -3.3617 
>  sample21.Rep1  sample21.Rep2  sample22.Rep1  sample22.Rep2  sample23.Rep1 
>        -2.8416        -2.7097        -4.0453        -3.9206        -3.4822 
>  sample23.Rep2  sample24.Rep1  sample24.Rep2  sample25.Rep1  sample25.Rep2 
>        -3.5596        -3.5323        -3.5729        -4.4223        -4.2475 
>  sample26.Rep1  sample26.Rep2  sample27.Rep1  sample27.Rep2  sample28.Rep1 
>        -4.1082        -4.2054        -3.0064        -2.7806        -5.7280 
>  sample28.Rep2  sample29.Rep1  sample29.Rep2  sample30.Rep1  sample30.Rep2 
>        -6.1335        -3.7236        -3.4875        -3.0277        -3.4006 
>  sample31.Rep1  sample31.Rep2  sample32.Rep1  sample32.Rep2  sample33.Rep1 
>        -3.5409        -3.6323        -2.9251        -2.4574        -3.7302 
>  sample33.Rep2  sample34.Rep1  sample34.Rep2  sample35.Rep1  sample35.Rep2 
>        -3.2884        -3.9493        -3.9947        -3.5154        -3.8421 
>  sample36.Rep1  sample36.Rep2  sample37.Rep1  sample37.Rep2  sample38.Rep1 
>        -3.1455        -3.2030        -4.0554        -4.0488        -2.5447 
>  sample38.Rep2 sample 39.Rep1 sample 39.Rep2 sample 40.Rep1 sample 40.Rep2 
>        -2.8795        -1.4051        -1.7690        -3.6933        -4.1579 
> > 
> > ######################################
> > x <- temp@concentrations
> > rep1 <- which(regexpr("Rep1", names(x)) > 0)
> > rep2 <- which(regexpr("Rep2", names(x)) > 0)
> > cat("Difference between replicates", "\n")
> Difference between replicates 
> > summary(x[rep1]-x[rep2])
>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
> -0.750200 -0.135700  0.012470  0.001151  0.196500  0.603300 
> > 
> > 
  Running ‘testNormalize.R’
  Running ‘testRPPA.R’
  Comparing ‘testRPPA.Rout’ to ‘testRPPA.Rout.save’ ...45,47c45,46
<   cannot open URL 'http://www.example.org/myslide.tsv': HTTP status was '404 Not Found'
< Error in url(path_or_url, "r") : 
<   cannot open the connection to 'http://www.example.org/myslide.tsv'
---
>   cannot open: HTTP status was '404 Not Found'
> Error in url(path_or_url, "r") : cannot open the connection
122c121
<   file "/tmp/RtmpwJBOf3/RLIBS_339710bff1e7/SuperCurveSampleData/extdata/rppaCellData/cellLineInfo.tsv" is not a MicroVigene datafile
---
>   file "/Library/Frameworks/R.framework/Versions/2.15/Resources/library/SuperCurveSampleData/extdata/rppaCellData/cellLineInfo.tsv" is not a MicroVigene datafile
165c164
<   cannot import data from file "/tmp/RtmpwJBOf3/RLIBS_339710bff1e7/SuperCurveSampleData/extdata/rppaCellData/Akt.txt"
---
>   cannot import data from file "/Library/Frameworks/R.framework/Versions/2.15/Resources/library/SuperCurveSampleData/extdata/rppaCellData/Akt.txt"
181c180
<   cannot import data from file "/tmp/RtmpwJBOf3/RLIBS_339710bff1e7/SuperCurveSampleData/extdata/rppaCellData/Akt.txt"
---
>   cannot import data from file "/Library/Frameworks/R.framework/Versions/2.15/Resources/library/SuperCurveSampleData/extdata/rppaCellData/Akt.txt"
  Running ‘testRPPANormalizationParams.R’
  Comparing ‘testRPPANormalizationParams.Rout’ to ‘testRPPANormalizationParams.Rout.save’ ... OK
  Running ‘testRPPASet.R’ [14s/14s]
  Comparing ‘testRPPASet.Rout’ to ‘testRPPASet.Rout.save’ ...15c15
< The following object is masked from 'package:robustbase':
---
> The following object(s) are masked from 'package:robustbase':
127c127
<   directory "/tmp/RtmpMlD3P8/supercurve/nosuch" does not exist
---
>   directory "/tmp/RtmpXvDrjD/supercurve/nosuch" does not exist
136c136
<   directory "/tmp/RtmpMlD3P8/supercurve/somefile" does not exist
---
>   directory "/tmp/RtmpXvDrjD/supercurve/somefile" does not exist
147c147
<   no quantification files found in directory "/tmp/RtmpMlD3P8/supercurve/emptydir"
---
>   no quantification files found in directory "/tmp/RtmpXvDrjD/supercurve/emptydir"
219c219
<   cannot load antibody data from file "/tmp/RtmpwJBOf3/RLIBS_339710bff1e7/SuperCurveSampleData/extdata/rppaTumorData/nosuch.tsv" - file.exists(antibodyfile) is not TRUE
---
>   cannot load antibody data from file "/Library/Frameworks/R.framework/Versions/2.15/Resources/library/SuperCurveSampleData/extdata/rppaTumorData/nosuch.tsv" - file.exists(antibodyfile) is not TRUE
229c229
<   cannot load antibody data from file "/tmp/RtmpwJBOf3/RLIBS_339710bff1e7/SuperCurveSampleData/extdata/rppaTumorData" - argument is not a file
---
>   cannot load antibody data from file "/Library/Frameworks/R.framework/Versions/2.15/Resources/library/SuperCurveSampleData/extdata/rppaTumorData" - argument is not a file
244c244
<   cannot load antibody data from file "/tmp/RtmpMlD3P8/supercurve/emptyfile.tsv" - no lines available in input
---
>   cannot load antibody data from file "/tmp/RtmpXvDrjD/supercurve/emptyfile.tsv" - no lines available in input
264c264
<   cannot load antibody data from file "/tmp/RtmpMlD3P8/supercurve/singlecolfile.tsv" - missing required column: "Antibody"
---
>   cannot load antibody data from file "/tmp/RtmpXvDrjD/supercurve/singlecolfile.tsv" - missing required column: "Antibody"
286c286
<   cannot load antibody data from file "/tmp/RtmpMlD3P8/supercurve/missingreqdcols.tsv" - missing required column: "Antibody"
---
>   cannot load antibody data from file "/tmp/RtmpXvDrjD/supercurve/missingreqdcols.tsv" - missing required column: "Antibody"
298c298
< Error in (function (classes, fdef, mtable)  : 
---
> Error in function (classes, fdef, mtable)  : 
309c309
<   directory "/tmp/RtmpMlD3P8/supercurve/nosuch" does not exist
---
>   directory "/tmp/RtmpXvDrjD/supercurve/nosuch" does not exist
324c324
<   directory "/tmp/RtmpMlD3P8/supercurve/readonly" is not writable
---
>   directory "/tmp/RtmpXvDrjD/supercurve/readonly" is not writable
388c388
<   directory "/tmp/RtmpMlD3P8/supercurve/nosuch" does not exist
---
>   directory "/tmp/RtmpXvDrjD/supercurve/nosuch" does not exist
397c397
<   directory "/tmp/RtmpMlD3P8/supercurve/somefile" does not exist
---
>   directory "/tmp/RtmpXvDrjD/supercurve/somefile" does not exist
454c454,458
< skipped ImageMagick test - 'convert' binary in "/usr/bin" 
---
> dev.new(): using pdf(file="Rplots4.pdf")
> merging graphs and image for ERK2
> sh: convert: command not found
> Error : (converted from warning) ImageMagick executable 'convert' not installed or unavailable via PATH
> [1] TRUE
  Running ‘testRPPASpatialParams.R’
  Comparing ‘testRPPASpatialParams.Rout’ to ‘testRPPASpatialParams.Rout.save’ ... OK
 [156s/157s] OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking re-building of vignette outputs ... [5s/6s] OK
* checking PDF version of manual ... OK
* DONE

Status: 1 WARNING, 4 NOTEs
See
  ‘/mnt/building/build_2017-11-14-15-24/RF_PKG_CHECK/PKGS/SuperCurve.Rcheck/00check.log’
for details.


Run time: 265.65 seconds.