Back to Multiple platform build/check report for BioC 3.20: simplified long |
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This page was generated on 2024-06-11 15:42 -0400 (Tue, 11 Jun 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.0 RC (2024-04-16 r86468) -- "Puppy Cup" | 4679 |
palomino4 | Windows Server 2022 Datacenter | x64 | 4.4.0 RC (2024-04-16 r86468 ucrt) -- "Puppy Cup" | 4414 |
merida1 | macOS 12.7.4 Monterey | x86_64 | 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" | 4441 |
kjohnson1 | macOS 13.6.6 Ventura | arm64 | 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" | 4394 |
Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X |
Package 89/2239 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
aroma.light 3.35.0 (landing page) Henrik Bengtsson
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK | |||||||||
palomino4 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | |||||||||
merida1 | macOS 12.7.4 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson1 | macOS 13.6.6 Ventura / arm64 | OK | OK | OK | OK | |||||||||
To the developers/maintainers of the aroma.light package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/aroma.light.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
Package: aroma.light |
Version: 3.35.0 |
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:aroma.light.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings aroma.light_3.35.0.tar.gz |
StartedAt: 2024-06-10 11:49:28 -0400 (Mon, 10 Jun 2024) |
EndedAt: 2024-06-10 11:50:57 -0400 (Mon, 10 Jun 2024) |
EllapsedTime: 88.8 seconds |
RetCode: 0 |
Status: OK |
CheckDir: aroma.light.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:aroma.light.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings aroma.light_3.35.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.20-bioc/meat/aroma.light.Rcheck’ * using R version 4.4.0 Patched (2024-04-24 r86482) * using platform: aarch64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 12.2.0 * running under: macOS Ventura 13.6.6 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘aroma.light/DESCRIPTION’ ... OK * this is package ‘aroma.light’ version ‘3.35.0’ * package encoding: latin1 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... NOTE Found the following hidden files and directories: inst/rsp/.rspPlugins These were most likely included in error. See section ‘Package structure’ in the ‘Writing R Extensions’ manual. * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘aroma.light’ can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking whether startup messages can be suppressed ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * 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 examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed normalizeCurveFit 8.494 0.096 8.593 normalizeAffine 8.305 0.079 8.387 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘backtransformAffine.matrix.R’ Running ‘backtransformPrincipalCurve.matrix.R’ Running ‘callNaiveGenotypes.R’ Running ‘distanceBetweenLines.R’ Running ‘findPeaksAndValleys.R’ Running ‘fitPrincipalCurve.matrix.R’ Running ‘fitXYCurve.matrix.R’ Running ‘iwpca.matrix.R’ Running ‘likelihood.smooth.spline.R’ Running ‘medianPolish.matrix.R’ Running ‘normalizeAffine.matrix.R’ Running ‘normalizeAverage.list.R’ Running ‘normalizeAverage.matrix.R’ Running ‘normalizeCurveFit.matrix.R’ Running ‘normalizeDifferencesToAverage.R’ Running ‘normalizeFragmentLength-ex1.R’ Running ‘normalizeFragmentLength-ex2.R’ Running ‘normalizeQuantileRank.list.R’ Running ‘normalizeQuantileRank.matrix.R’ Running ‘normalizeQuantileSpline.matrix.R’ Running ‘normalizeTumorBoost,flavors.R’ Running ‘normalizeTumorBoost.R’ Running ‘robustSmoothSpline.R’ Running ‘rowAverages.matrix.R’ Running ‘sampleCorrelations.matrix.R’ Running ‘sampleTuples.R’ Running ‘wpca.matrix.R’ Running ‘wpca2.matrix.R’ OK * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/Users/biocbuild/bbs-3.20-bioc/meat/aroma.light.Rcheck/00check.log’ for details.
aroma.light.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL aroma.light ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library’ * installing *source* package ‘aroma.light’ ... ** using staged installation ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (aroma.light)
aroma.light.Rcheck/tests/backtransformAffine.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > X <- matrix(1:8, nrow=4, ncol=2) > X[2,2] <- NA_integer_ > > print(X) [,1] [,2] [1,] 1 5 [2,] 2 NA [3,] 3 7 [4,] 4 8 > > # Returns a 4x2 matrix > print(backtransformAffine(X, a=c(1,5))) [,1] [,2] [1,] 0 0 [2,] 1 NA [3,] 2 2 [4,] 3 3 > > # Returns a 4x2 matrix > print(backtransformAffine(X, b=c(1,1/2))) [,1] [,2] [1,] 1 10 [2,] 2 NA [3,] 3 14 [4,] 4 16 > > # Returns a 4x2 matrix > print(backtransformAffine(X, a=matrix(1:4,ncol=1))) [,1] [,2] [1,] 0 4 [2,] 0 NA [3,] 0 4 [4,] 0 4 > > # Returns a 4x2 matrix > print(backtransformAffine(X, a=matrix(1:3,ncol=1))) [,1] [,2] [1,] 0 4 [2,] 0 NA [3,] 0 4 [4,] 3 7 > > # Returns a 4x2 matrix > print(backtransformAffine(X, a=matrix(1:2,ncol=1), b=c(1,2))) [,1] [,2] [1,] 0 2 [2,] 0 NA [3,] 2 3 [4,] 2 3 > > # Returns a 4x1 matrix > print(backtransformAffine(X, b=c(1,1/2), project=TRUE)) [,1] [1,] 2.8 [2,] 1.6 [3,] 5.2 [4,] 6.4 > > # If the columns of X are identical, and a identity > # backtransformation is applied and projected, the > # same matrix is returned. > X <- matrix(1:4, nrow=4, ncol=3) > Y <- backtransformAffine(X, b=c(1,1,1), project=TRUE) > print(X) [,1] [,2] [,3] [1,] 1 1 1 [2,] 2 2 2 [3,] 3 3 3 [4,] 4 4 4 > print(Y) [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 > stopifnot(sum(X[,1]-Y) <= .Machine$double.eps) > > > # If the columns of X are identical, and a identity > # backtransformation is applied and projected, the > # same matrix is returned. > X <- matrix(1:4, nrow=4, ncol=3) > X[,2] <- X[,2]*2; X[,3] <- X[,3]*3 > print(X) [,1] [,2] [,3] [1,] 1 2 3 [2,] 2 4 6 [3,] 3 6 9 [4,] 4 8 12 > Y <- backtransformAffine(X, b=c(1,2,3)) > print(Y) [,1] [,2] [,3] [1,] 1 1 1 [2,] 2 2 2 [3,] 3 3 3 [4,] 4 4 4 > Y <- backtransformAffine(X, b=c(1,2,3), project=TRUE) > print(Y) [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 > stopifnot(sum(X[,1]-Y) <= .Machine$double.eps) > > proc.time() user system elapsed 0.303 0.073 0.365
aroma.light.Rcheck/tests/backtransformPrincipalCurve.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Consider the case where K=4 measurements have been done > # for the same underlying signals 'x'. The different measurements > # have different systematic variation > # > # y_k = f(x_k) + eps_k; k = 1,...,K. > # > # In this example, we assume non-linear measurement functions > # > # f(x) = a + b*x + x^c + eps(b*x) > # > # where 'a' is an offset, 'b' a scale factor, and 'c' an exponential. > # We also assume heteroscedastic zero-mean noise with standard > # deviation proportional to the rescaled underlying signal 'x'. > # > # Furthermore, we assume that measurements k=2 and k=3 undergo the > # same transformation, which may illustrate that the come from > # the same batch. However, when *fitting* the model below we > # will assume they are independent. > > # Transforms > a <- c(2, 15, 15, 3) > b <- c(2, 3, 3, 4) > c <- c(1, 2, 2, 1/2) > K <- length(a) > > # The true signal > N <- 1000 > x <- rexp(N) > > # The noise > bX <- outer(b,x) > E <- apply(bX, MARGIN=2, FUN=function(x) rnorm(K, mean=0, sd=0.1*x)) > > # The transformed signals with noise > Xc <- t(sapply(c, FUN=function(c) x^c)) > Y <- a + bX + Xc + E > Y <- t(Y) > > > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Fit principal curve > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Fit principal curve through Y = (y_1, y_2, ..., y_K) > fit <- fitPrincipalCurve(Y) > > # Flip direction of 'lambda'? > rho <- cor(fit$lambda, Y[,1], use="complete.obs") > flip <- (rho < 0) > if (flip) { + fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda + } > > L <- ncol(fit$s) > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Backtransform data according to model fit > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Backtransform toward the principal curve (the "common scale") > YN1 <- backtransformPrincipalCurve(Y, fit=fit) > stopifnot(ncol(YN1) == K) > > > # Backtransform toward the first dimension > YN2 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=1) > stopifnot(ncol(YN2) == K) > > > # Backtransform toward the last (fitted) dimension > YN3 <- backtransformPrincipalCurve(Y, fit=fit, targetDimension=L) > stopifnot(ncol(YN3) == K) > > > # Backtransform toward the third dimension (dimension by dimension) > # Note, this assumes that K == L. > YN4 <- Y > for (cc in 1:L) { + YN4[,cc] <- backtransformPrincipalCurve(Y, fit=fit, + targetDimension=1, dimensions=cc) + } > stopifnot(identical(YN4, YN2)) > > > # Backtransform a subset toward the first dimension > # Note, this assumes that K == L. > YN5 <- backtransformPrincipalCurve(Y, fit=fit, + targetDimension=1, dimensions=2:3) > stopifnot(identical(YN5, YN2[,2:3])) > stopifnot(ncol(YN5) == 2) > > > # Extract signals from measurement #2 and backtransform according > # its model fit. Signals are standardized to target dimension 1. > y6 <- Y[,2,drop=FALSE] > yN6 <- backtransformPrincipalCurve(y6, fit=fit, dimensions=2, + targetDimension=1) > stopifnot(identical(yN6, YN2[,2,drop=FALSE])) > stopifnot(ncol(yN6) == 1) > > > # Extract signals from measurement #2 and backtransform according > # the the model fit of measurement #3 (because we believe these > # two have undergone very similar transformations. > # Signals are standardized to target dimension 1. > y7 <- Y[,2,drop=FALSE] > yN7 <- backtransformPrincipalCurve(y7, fit=fit, dimensions=3, + targetDimension=1) > stopifnot(ncol(yN7) == 1) > > rho <- cor(yN7, yN6) > print(rho) [,1] [1,] 0.9999306 > stopifnot(rho > 0.999) > > proc.time() user system elapsed 0.815 0.087 0.888
aroma.light.Rcheck/tests/callNaiveGenotypes.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > layout(matrix(1:3, ncol=1)) > par(mar=c(2,4,4,1)+0.1) > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # A bimodal distribution > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > xAA <- rnorm(n=10000, mean=0, sd=0.1) > xBB <- rnorm(n=10000, mean=1, sd=0.1) > x <- c(xAA,xBB) > fit <- findPeaksAndValleys(x) > print(fit) type x density 1 peak -0.003866461 1.6894658661 2 valley 0.501381187 0.0004038902 3 peak 0.998208041 1.6771731687 > calls <- callNaiveGenotypes(x, cn=rep(1,length(x)), verbose=-20) Calling genotypes from allele B fractions (BAFs)... Fitting naive genotype model... Fitting naive genotype model from normal allele B fractions (BAFs)... Flavor: density Censoring BAFs... Before: Min. 1st Qu. Median Mean 3rd Qu. Max. -0.3779398 0.0005765 0.4993076 0.5015562 1.0027690 1.3933334 [1] 20000 After: Min. 1st Qu. Median Mean 3rd Qu. Max. -Inf 0.0005765 0.4993076 1.0027690 Inf [1] 16870 Censoring BAFs...done Copy number level #1 (C=1) of 1... Identified extreme points in density of BAF: type x density 1 peak 0.01092275 1.635704595 2 valley 0.49826374 0.004076236 3 peak 0.98217274 1.624599500 Local minimas ("valleys") in BAF: type x density 2 valley 0.4982637 0.004076236 Copy number level #1 (C=1) of 1...done Fitting naive genotype model from normal allele B fractions (BAFs)...done [[1]] [[1]]$flavor [1] "density" [[1]]$cn [1] 1 [[1]]$nbrOfGenotypeGroups [1] 2 [[1]]$tau [1] 0.4982637 [[1]]$n [1] 16870 [[1]]$fit type x density 1 peak 0.01092275 1.635704595 2 valley 0.49826374 0.004076236 3 peak 0.98217274 1.624599500 [[1]]$fitValleys type x density 2 valley 0.4982637 0.004076236 attr(,"class") [1] "NaiveGenotypeModelFit" "list" Fitting naive genotype model...done Copy number level #1 (C=1) of 1... Model fit: $flavor [1] "density" $cn [1] 1 $nbrOfGenotypeGroups [1] 2 $tau [1] 0.4982637 $n [1] 16870 $fit type x density 1 peak 0.01092275 1.635704595 2 valley 0.49826374 0.004076236 3 peak 0.98217274 1.624599500 $fitValleys type x density 2 valley 0.4982637 0.004076236 Genotype threshholds [1]: 0.498263735142027 TCN=1 => BAF in {0,1}. Call regions: A = (-Inf,0.498], B = (0.498,+Inf) Copy number level #1 (C=1) of 1...done Calling genotypes from allele B fractions (BAFs)...done > xc <- split(x, calls) > print(table(calls)) calls 0 1 10000 10000 > xx <- c(list(x),xc) > plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,BB)") > abline(v=fit$x) > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # A trimodal distribution with missing values > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > xAB <- rnorm(n=10000, mean=1/2, sd=0.1) > x <- c(xAA,xAB,xBB) > x[sample(length(x), size=0.05*length(x))] <- NA_real_ > x[sample(length(x), size=0.01*length(x))] <- -Inf > x[sample(length(x), size=0.01*length(x))] <- +Inf > fit <- findPeaksAndValleys(x) > print(fit) type x density 1 peak -0.005775248 1.1705855 2 valley 0.246477000 0.1860534 3 peak 0.494725243 1.1762550 4 valley 0.746977490 0.1834609 5 peak 0.999229737 1.1611572 > calls <- callNaiveGenotypes(x) > xc <- split(x, calls) > print(table(calls)) calls 0 0.5 1 9565 9334 9640 > xx <- c(list(x),xc) > plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,AB,BB)") > abline(v=fit$x) > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # A trimodal distribution with clear separation > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > xAA <- rnorm(n=10000, mean=0, sd=0.02) > xAB <- rnorm(n=10000, mean=1/2, sd=0.02) > xBB <- rnorm(n=10000, mean=1, sd=0.02) > x <- c(xAA,xAB,xBB) > fit <- findPeaksAndValleys(x) > print(fit) type x density 1 peak -0.003875287 2.604627e+00 2 valley 0.246409944 3.365412e-05 3 peak 0.496695175 2.599776e+00 4 valley 0.746980405 3.416165e-05 5 peak 0.997265636 2.607462e+00 > calls <- callNaiveGenotypes(x) > xc <- split(x, calls) > print(table(calls)) calls 0 0.5 1 10000 10000 10000 > xx <- c(list(x),xc) > plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA',AB',BB')") > abline(v=fit$x) > > proc.time() user system elapsed 0.580 0.086 0.655
aroma.light.Rcheck/tests/distanceBetweenLines.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > for (zzz in 0) { + + # This example requires plot3d() in R.basic [http://www.braju.com/R/] + if (!require(pkgName <- "R.basic", character.only=TRUE)) break + + layout(matrix(1:4, nrow=2, ncol=2, byrow=TRUE)) + + ############################################################ + # Lines in two-dimensions + ############################################################ + x <- list(a=c(1,0), b=c(1,2)) + y <- list(a=c(0,2), b=c(1,1)) + fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b) + + xlim <- ylim <- c(-1,8) + plot(NA, xlab="", ylab="", xlim=ylim, ylim=ylim) + + # Highlight the offset coordinates for both lines + points(t(x$a), pch="+", col="red") + text(t(x$a), label=expression(a[x]), adj=c(-1,0.5)) + points(t(y$a), pch="+", col="blue") + text(t(y$a), label=expression(a[y]), adj=c(-1,0.5)) + + v <- c(-1,1)*10 + xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v) + yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v) + + lines(xv, col="red") + lines(yv, col="blue") + + points(t(fit$xs), cex=2.0, col="red") + text(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5)) + points(t(fit$yt), cex=1.5, col="blue") + text(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5)) + print(fit) + + + ############################################################ + # Lines in three-dimensions + ############################################################ + x <- list(a=c(0,0,0), b=c(1,1,1)) # The 'diagonal' + y <- list(a=c(2,1,2), b=c(2,1,3)) # A 'fitted' line + fit <- distanceBetweenLines(ax=x$a, bx=x$b, ay=y$a, by=y$b) + + xlim <- ylim <- zlim <- c(-1,3) + dummy <- t(c(1,1,1))*100 + + # Coordinates for the lines in 3d + v <- seq(-10,10, by=1) + xv <- list(x=x$a[1]+x$b[1]*v, y=x$a[2]+x$b[2]*v, z=x$a[3]+x$b[3]*v) + yv <- list(x=y$a[1]+y$b[1]*v, y=y$a[2]+y$b[2]*v, z=y$a[3]+y$b[3]*v) + + for (theta in seq(30,140,length.out=3)) { + plot3d(dummy, theta=theta, phi=30, xlab="", ylab="", zlab="", + xlim=ylim, ylim=ylim, zlim=zlim) + + # Highlight the offset coordinates for both lines + points3d(t(x$a), pch="+", col="red") + text3d(t(x$a), label=expression(a[x]), adj=c(-1,0.5)) + points3d(t(y$a), pch="+", col="blue") + text3d(t(y$a), label=expression(a[y]), adj=c(-1,0.5)) + + # Draw the lines + lines3d(xv, col="red") + lines3d(yv, col="blue") + + # Draw the two points that are closest to each other + points3d(t(fit$xs), cex=2.0, col="red") + text3d(t(fit$xs), label=expression(x(s)), adj=c(+2,0.5)) + points3d(t(fit$yt), cex=1.5, col="blue") + text3d(t(fit$yt), label=expression(y(t)), adj=c(-1,0.5)) + + # Draw the distance between the two points + lines3d(rbind(fit$xs,fit$yt), col="purple", lwd=2) + } + + print(fit) + + } # for (zzz in 0) Loading required package: R.basic Warning message: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called 'R.basic' > rm(zzz) > > proc.time() user system elapsed 0.403 0.070 0.457
aroma.light.Rcheck/tests/findPeaksAndValleys.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > layout(matrix(1:3, ncol=1)) > par(mar=c(2,4,4,1)+0.1) > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # A unimodal distribution > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > x1 <- rnorm(n=10000, mean=0, sd=1) > x <- x1 > fit <- findPeaksAndValleys(x) > print(fit) type x density 1 peak -0.02776458 0.3909186 > plot(density(x), lwd=2, main="x1") > abline(v=fit$x) > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # A trimodal distribution > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > x2 <- rnorm(n=10000, mean=4, sd=1) > x3 <- rnorm(n=10000, mean=8, sd=1) > x <- c(x1,x2,x3) > fit <- findPeaksAndValleys(x) > print(fit) type x density 1 peak 0.008308716 0.12268529 2 valley 2.003967944 0.04389563 3 peak 3.999627172 0.12448833 4 valley 5.925263270 0.04304314 5 peak 7.990945628 0.12468700 > plot(density(x), lwd=2, main="c(x1,x2,x3)") > abline(v=fit$x) > > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # A trimodal distribution with clear separation > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - > x1b <- rnorm(n=10000, mean=0, sd=0.1) > x2b <- rnorm(n=10000, mean=4, sd=0.1) > x3b <- rnorm(n=10000, mean=8, sd=0.1) > x <- c(x1b,x2b,x3b) > > # Illustrating explicit usage of density() > d <- density(x) > fit <- findPeaksAndValleys(d, tol=0) > print(fit) type x density 1 peak -0.01441638 3.430130e-01 2 valley 1.99028217 1.124843e-06 3 peak 3.97342482 3.419885e-01 4 valley 5.97812338 1.206338e-06 5 peak 7.98282193 3.427692e-01 > plot(d, lwd=2, main="c(x1b,x2b,x3b)") > abline(v=fit$x) > > proc.time() user system elapsed 0.376 0.077 0.440
aroma.light.Rcheck/tests/fitPrincipalCurve.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate data from the model y <- a + bx + x^c + eps(bx) > J <- 1000 > x <- rexp(J) > a <- c(2,15,3) > b <- c(2,3,4) > c <- c(1,2,1/2) > bx <- outer(b,x) > xc <- t(sapply(c, FUN=function(c) x^c)) > eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(b), mean=0, sd=0.1*x)) > y <- a + bx + xc + eps > y <- t(y) > > # Fit principal curve through (y_1, y_2, y_3) > fit <- fitPrincipalCurve(y, verbose=TRUE) Fitting principal curve... Data size: 1000x3 Identifying missing values... Identifying missing values...done Data size after removing non-finite data points: 1000x3 Calling principal_curve()... Starting curve---distance^2: 1957368 Iteration 1---distance^2: 330.7002 Iteration 2---distance^2: 330.4327 Converged: TRUE Number of iterations: 2 Processing time/iteration: 0.1s (0.0s/iteration) Calling principal_curve()...done Fitting principal curve...done > > # Flip direction of 'lambda'? > rho <- cor(fit$lambda, y[,1], use="complete.obs") > flip <- (rho < 0) > if (flip) { + fit$lambda <- max(fit$lambda, na.rm=TRUE)-fit$lambda + } > > > # Backtransform (y_1, y_2, y_3) to be proportional to each other > yN <- backtransformPrincipalCurve(y, fit=fit) > > # Same backtransformation dimension by dimension > yN2 <- y > for (cc in 1:ncol(y)) { + yN2[,cc] <- backtransformPrincipalCurve(y, fit=fit, dimensions=cc) + } > stopifnot(identical(yN2, yN)) > > > xlim <- c(0, 1.04*max(x)) > ylim <- range(c(y,yN), na.rm=TRUE) > > > # Pairwise signals vs x before and after transform > layout(matrix(1:4, nrow=2, byrow=TRUE)) > par(mar=c(4,4,3,2)+0.1) > for (cc in 1:3) { + ylab <- substitute(y[c], env=list(c=cc)) + plot(NA, xlim=xlim, ylim=ylim, xlab="x", ylab=ylab) + abline(h=a[cc], lty=3) + mtext(side=4, at=a[cc], sprintf("a=%g", a[cc]), + cex=0.8, las=2, line=0, adj=1.1, padj=-0.2) + points(x, y[,cc]) + points(x, yN[,cc], col="tomato") + legend("topleft", col=c("black", "tomato"), pch=19, + c("orignal", "transformed"), bty="n") + } > title(main="Pairwise signals vs x before and after transform", outer=TRUE, line=-2) > > > # Pairwise signals before and after transform > layout(matrix(1:4, nrow=2, byrow=TRUE)) > par(mar=c(4,4,3,2)+0.1) > for (rr in 3:2) { + ylab <- substitute(y[c], env=list(c=rr)) + for (cc in 1:2) { + if (cc == rr) { + plot.new() + next + } + xlab <- substitute(y[c], env=list(c=cc)) + plot(NA, xlim=ylim, ylim=ylim, xlab=xlab, ylab=ylab) + abline(a=0, b=1, lty=2) + points(y[,c(cc,rr)]) + points(yN[,c(cc,rr)], col="tomato") + legend("topleft", col=c("black", "tomato"), pch=19, + c("orignal", "transformed"), bty="n") + } + } > title(main="Pairwise signals before and after transform", outer=TRUE, line=-2) > > proc.time() user system elapsed 1.091 0.107 1.184
aroma.light.Rcheck/tests/fitXYCurve.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate data from the model y <- a + bx + x^c + eps(bx) > x <- rexp(1000) > a <- c(2,15) > b <- c(2,1) > c <- c(1,2) > bx <- outer(b,x) > xc <- t(sapply(c, FUN=function(c) x^c)) > eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) > Y <- a + bx + xc + eps > Y <- t(Y) > > lim <- c(0,70) > plot(Y, xlim=lim, ylim=lim) > > # Fit principal curve through a subset of (y_1, y_2) > subset <- sample(nrow(Y), size=0.3*nrow(Y)) > fit <- fitXYCurve(Y[subset,], bandwidth=0.2) > > lines(fit, col="red", lwd=2) > > # Backtransform (y_1, y_2) keeping y_1 unchanged > YN <- backtransformXYCurve(Y, fit=fit) > points(YN, col="blue") > abline(a=0, b=1, col="red", lwd=2) > > proc.time() user system elapsed 0.424 0.079 0.519
aroma.light.Rcheck/tests/iwpca.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > for (zzz in 0) { + + # This example requires plot3d() in R.basic [http://www.braju.com/R/] + if (!require(pkgName <- "R.basic", character.only=TRUE)) break + + # Simulate data from the model y <- a + bx + eps(bx) + x <- rexp(1000) + a <- c(2,15,3) + b <- c(2,3,4) + bx <- outer(b,x) + eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) + y <- a + bx + eps + y <- t(y) + + # Add some outliers by permuting the dimensions for 1/10 of the observations + idx <- sample(1:nrow(y), size=1/10*nrow(y)) + y[idx,] <- y[idx,c(2,3,1)] + + # Plot the data with fitted lines at four different view points + opar <- par(mar=c(1,1,1,1)+0.1) + N <- 4 + layout(matrix(1:N, nrow=2, byrow=TRUE)) + theta <- seq(0,270,length.out=N) + phi <- rep(20, length.out=N) + xlim <- ylim <- zlim <- c(0,45) + persp <- list() + for (kk in seq_along(theta)) { + # Plot the data + persp[[kk]] <- plot3d(y, theta=theta[kk], phi=phi[kk], xlim=xlim, ylim=ylim, zlim=zlim) + } + + # Weights on the observations + # Example a: Equal weights + w <- NULL + # Example b: More weight on the outliers (uncomment to test) + w <- rep(1, length(x)); w[idx] <- 0.8 + + # ...and show all iterations too with different colors. + maxIter <- c(seq(1,20,length.out=10),Inf) + col <- topo.colors(length(maxIter)) + # Show the fitted value for every iteration + for (ii in seq_along(maxIter)) { + # Fit a line using IWPCA through data + fit <- iwpca(y, w=w, maxIter=maxIter[ii], swapDirections=TRUE) + + ymid <- fit$xMean + d0 <- apply(y, MARGIN=2, FUN=min) - ymid + d1 <- apply(y, MARGIN=2, FUN=max) - ymid + b <- fit$vt[1,] + y0 <- -b * max(abs(d0)) + y1 <- b * max(abs(d1)) + yline <- matrix(c(y0,y1), nrow=length(b), ncol=2) + yline <- yline + ymid + + for (kk in seq_along(theta)) { + # Set pane to draw in + par(mfg=c((kk-1) %/% 2, (kk-1) %% 2) + 1) + # Set the viewpoint of the pane + options(persp.matrix=persp[[kk]]) + + # Get the first principal component + points3d(t(ymid), col=col[ii]) + lines3d(t(yline), col=col[ii]) + + # Highlight the last one + if (ii == length(maxIter)) + lines3d(t(yline), col="red", lwd=3) + } + } + + par(opar) + + } # for (zzz in 0) Loading required package: R.basic Warning message: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called 'R.basic' > rm(zzz) > > proc.time() user system elapsed 0.391 0.069 0.446
aroma.light.Rcheck/tests/likelihood.smooth.spline.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Define f(x) > f <- expression(0.1*x^4 + 1*x^3 + 2*x^2 + x + 10*sin(2*x)) > > # Simulate data from this function in the range [a,b] > a <- -2; b <- 5 > x <- seq(a, b, length.out=3000) > y <- eval(f) > > # Add some noise to the data > y <- y + rnorm(length(y), 0, 10) > > # Plot the function and its second derivative > plot(x,y, type="l", lwd=4) > > # Fit a cubic smoothing spline and plot it > g <- smooth.spline(x,y, df=16) > lines(g, col="yellow", lwd=2, lty=2) > > # Calculating the (log) likelihood of the fitted spline > l <- likelihood(g) > > cat("Log likelihood with unique x values:\n") Log likelihood with unique x values: > print(l) Likelihood of smoothing spline: -301767.5 Log base: 2.718282 Weighted residuals sum of square: 301767.6 Penalty: -0.1147806 Smoothing parameter lambda: 0.0009257147 Roughness score: 123.9913 > > # Note that this is not the same as the log likelihood of the > # data on the fitted spline iff the x values are non-unique > x[1:5] <- x[1] # Non-unique x values > g <- smooth.spline(x,y, df=16) > l <- likelihood(g) > > cat("\nLog likelihood of the *spline* data set:\n") Log likelihood of the *spline* data set: > print(l) Likelihood of smoothing spline: -301425 Log base: 2.718282 Weighted residuals sum of square: 301425.1 Penalty: -0.1147203 Smoothing parameter lambda: 0.0009261969 Roughness score: 123.8617 > > # In cases with non unique x values one has to proceed as > # below if one want to get the log likelihood for the original > # data. > l <- likelihood(g, x=x, y=y) > cat("\nLog likelihood of the *original* data set:\n") Log likelihood of the *original* data set: > print(l) Likelihood of smoothing spline: -301772 Log base: 2.718282 Weighted residuals sum of square: 301772.1 Penalty: -0.1147203 Smoothing parameter lambda: 0.0009261969 Roughness score: 123.8617 > > > > > > > proc.time() user system elapsed 0.430 0.077 0.490
aroma.light.Rcheck/tests/medianPolish.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Deaths from sport parachuting; from ABC of EDA, p.224: > deaths <- matrix(c(14,15,14, 7,4,7, 8,2,10, 15,9,10, 0,2,0), ncol=3, byrow=TRUE) > rownames(deaths) <- c("1-24", "25-74", "75-199", "200++", "NA") > colnames(deaths) <- 1973:1975 > > print(deaths) 1973 1974 1975 1-24 14 15 14 25-74 7 4 7 75-199 8 2 10 200++ 15 9 10 NA 0 2 0 > > mp <- medianPolish(deaths) > mp1 <- medpolish(deaths, trace=FALSE) > print(mp) Median Polish Results (Dataset: "deaths") Overall: 8 Row Effects: 1-24 25-74 75-199 200++ NA 6 -1 0 2 -8 Column Effects: 1973 1974 1975 0 -1 0 Residuals: 1973 1974 1975 1-24 0 2 0 25-74 0 -2 0 75-199 0 -5 2 200++ 5 0 0 NA 0 3 0 > > ff <- c("overall", "row", "col", "residuals") > stopifnot(all.equal(mp[ff], mp1[ff])) > > # Validate decomposition: > stopifnot(all.equal(deaths, mp$overall+outer(mp$row,mp$col,"+")+mp$resid)) > > proc.time() user system elapsed 0.310 0.071 0.366
aroma.light.Rcheck/tests/normalizeAffine.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light") > rg <- read.table(pathname, header=TRUE, sep="\t") > nbrOfScans <- max(rg$slide) > > rg <- as.list(rg) > for (field in c("R", "G")) + rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans) > rg$slide <- rg$spot <- NULL > rg <- as.matrix(as.data.frame(rg)) > colnames(rg) <- rep(c("R", "G"), each=nbrOfScans) > > rgC <- rg > > layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE)) > > for (channel in c("R", "G")) { + sidx <- which(colnames(rg) == channel) + channelColor <- switch(channel, R="red", G="green") + + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # The raw data + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + plotMvsAPairs(rg, channel=channel) + title(main=paste("Observed", channel)) + box(col=channelColor) + + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # The calibrated data + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL) + + plotMvsAPairs(rgC, channel=channel) + title(main=paste("Calibrated", channel)) + box(col=channelColor) + } # for (channel ...) There were 50 or more warnings (use warnings() to see the first 50) > > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # The average calibrated data > # > # Note how the red signals are weaker than the green. The reason > # for this can be that the scale factor in the green channel is > # greater than in the red channel, but it can also be that there > # is a remaining relative difference in bias between the green > # and the red channel, a bias that precedes the scanning. > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > rgCA <- matrix(NA_real_, nrow=nrow(rg), ncol=2) > colnames(rgCA) <- c("R", "G") > for (channel in c("R", "G")) { + sidx <- which(colnames(rg) == channel) + rgCA[,channel] <- calibrateMultiscan(rg[,sidx]) + } > > plotMvsA(rgCA) > title(main="Average calibrated") > > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # The affine normalized average calibrated data > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Create a matrix where the columns represent the channels > # to be normalized. > rgCAN <- rgCA > # Affine normalization of channels > rgCAN <- normalizeAffine(rgCAN) > > plotMvsA(rgCAN) > title(main="Affine normalized A.C.") > > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # It is always ok to rescale the affine normalized data if its > # done on (R,G); not on (A,M)! However, this is only needed for > # esthetic purposes. > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > rgCAN <- rgCAN * 2^5 > plotMvsA(rgCAN) > title(main="Rescaled normalized") > > > > proc.time() user system elapsed 2.148 0.165 2.306
aroma.light.Rcheck/tests/normalizeAverage.list.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate ten samples of different lengths > N <- 10000 > X <- list() > for (kk in 1:8) { + rfcn <- list(rnorm, rgamma)[[sample(2, size=1)]] + size <- runif(1, min=0.3, max=1) + a <- rgamma(1, shape=20, rate=10) + b <- rgamma(1, shape=10, rate=10) + values <- rfcn(size*N, a, b) + + # "Censor" values + values[values < 0 | values > 8] <- NA_real_ + + X[[kk]] <- values + } > > # Add 20% missing values > X <- lapply(X, FUN=function(x) { + x[sample(length(x), size=0.20*length(x))] <- NA_real_ + x + }) > > # Normalize quantiles > Xn <- normalizeAverage(X, na.rm=TRUE, targetAvg=median(unlist(X), na.rm=TRUE)) > > # Plot the data > layout(matrix(1:2, ncol=1)) > xlim <- range(X, Xn, na.rm=TRUE) > plotDensity(X, lwd=2, xlim=xlim, main="The original distributions") > plotDensity(Xn, lwd=2, xlim=xlim, main="The normalized distributions") > > proc.time() user system elapsed 0.423 0.072 0.481
aroma.light.Rcheck/tests/normalizeAverage.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate three samples with on average 20% missing values > N <- 10000 > X <- cbind(rnorm(N, mean=3, sd=1), + rnorm(N, mean=4, sd=2), + rgamma(N, shape=2, rate=1)) > X[sample(3*N, size=0.20*3*N)] <- NA_real_ > > # Normalize quantiles > Xn <- normalizeAverage(X, na.rm=TRUE, targetAvg=median(X, na.rm=TRUE)) > > # Plot the data > layout(matrix(1:2, ncol=1)) > xlim <- range(X, Xn, na.rm=TRUE) > plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") > plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") > > proc.time() user system elapsed 0.372 0.075 0.430
aroma.light.Rcheck/tests/normalizeCurveFit.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light") > rg <- read.table(pathname, header=TRUE, sep="\t") > nbrOfScans <- max(rg$slide) > > rg <- as.list(rg) > for (field in c("R", "G")) + rg[[field]] <- matrix(as.double(rg[[field]]), ncol=nbrOfScans) > rg$slide <- rg$spot <- NULL > rg <- as.matrix(as.data.frame(rg)) > colnames(rg) <- rep(c("R", "G"), each=nbrOfScans) > > layout(matrix(c(1,2,0,3,4,0,5,6,7), ncol=3, byrow=TRUE)) > > rgC <- rg > for (channel in c("R", "G")) { + sidx <- which(colnames(rg) == channel) + channelColor <- switch(channel, R="red", G="green") + + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # The raw data + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + plotMvsAPairs(rg[,sidx]) + title(main=paste("Observed", channel)) + box(col=channelColor) + + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + # The calibrated data + # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + rgC[,sidx] <- calibrateMultiscan(rg[,sidx], average=NULL) + + plotMvsAPairs(rgC[,sidx]) + title(main=paste("Calibrated", channel)) + box(col=channelColor) + } # for (channel ...) > > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # The average calibrated data > # > # Note how the red signals are weaker than the green. The reason > # for this can be that the scale factor in the green channel is > # greater than in the red channel, but it can also be that there > # is a remaining relative difference in bias between the green > # and the red channel, a bias that precedes the scanning. > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > rgCA <- rg > for (channel in c("R", "G")) { + sidx <- which(colnames(rg) == channel) + rgCA[,sidx] <- calibrateMultiscan(rg[,sidx]) + } > > rgCAavg <- matrix(NA_real_, nrow=nrow(rgCA), ncol=2) > colnames(rgCAavg) <- c("R", "G") > for (channel in c("R", "G")) { + sidx <- which(colnames(rg) == channel) + rgCAavg[,channel] <- apply(rgCA[,sidx], MARGIN=1, FUN=median, na.rm=TRUE) + } > > # Add some "fake" outliers > outliers <- 1:600 > rgCAavg[outliers,"G"] <- 50000 > > plotMvsA(rgCAavg) > title(main="Average calibrated (AC)") > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Normalize data > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Weight-down outliers when normalizing > weights <- rep(1, nrow(rgCAavg)) > weights[outliers] <- 0.001 > > # Affine normalization of channels > rgCANa <- normalizeAffine(rgCAavg, weights=weights) > # It is always ok to rescale the affine normalized data if its > # done on (R,G); not on (A,M)! However, this is only needed for > # esthetic purposes. > rgCANa <- rgCANa *2^1.4 > plotMvsA(rgCANa) > title(main="Normalized AC") > > # Curve-fit (lowess) normalization > rgCANlw <- normalizeLowess(rgCAavg, weights=weights) Warning message: In normalizeCurveFit.matrix(X, method = "lowess", ...) : Weights were rounded to {0,1} since 'lowess' normalization supports only zero-one weights. > plotMvsA(rgCANlw, col="orange", add=TRUE) > > # Curve-fit (loess) normalization > rgCANl <- normalizeLoess(rgCAavg, weights=weights) > plotMvsA(rgCANl, col="red", add=TRUE) > > # Curve-fit (robust spline) normalization > rgCANrs <- normalizeRobustSpline(rgCAavg, weights=weights) > plotMvsA(rgCANrs, col="blue", add=TRUE) > > legend(x=0,y=16, legend=c("affine", "lowess", "loess", "r. spline"), pch=19, + col=c("black", "orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") > > > plotMvsMPairs(cbind(rgCANa, rgCANlw), col="orange", xlab=expression(M[affine])) > title(main="Normalized AC") > plotMvsMPairs(cbind(rgCANa, rgCANl), col="red", add=TRUE) > plotMvsMPairs(cbind(rgCANa, rgCANrs), col="blue", add=TRUE) > abline(a=0, b=1, lty=2) > legend(x=-6,y=6, legend=c("lowess", "loess", "r. spline"), pch=19, + col=c("orange", "red", "blue"), ncol=2, x.intersp=0.3, bty="n") > > > proc.time() user system elapsed 8.164 0.147 8.299
aroma.light.Rcheck/tests/normalizeDifferencesToAverage.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate three shifted tracks of different lengths with same profiles > ns <- c(A=2, B=1, C=0.25)*1000 > xx <- lapply(ns, FUN=function(n) { seq(from=1, to=max(ns), length.out=n) }) > zz <- mapply(seq_along(ns), ns, FUN=function(z,n) rep(z,n)) > > yy <- list( + A = rnorm(ns["A"], mean=0, sd=0.5), + B = rnorm(ns["B"], mean=5, sd=0.4), + C = rnorm(ns["C"], mean=-5, sd=1.1) + ) > yy <- lapply(yy, FUN=function(y) { + n <- length(y) + y[1:(n/2)] <- y[1:(n/2)] + 2 + y[1:(n/4)] <- y[1:(n/4)] - 4 + y + }) > > # Shift all tracks toward the first track > yyN <- normalizeDifferencesToAverage(yy, baseline=1) > > # The baseline channel is not changed > stopifnot(identical(yy[[1]], yyN[[1]])) > > # Get the estimated parameters > fit <- attr(yyN, "fit") > > # Plot the tracks > layout(matrix(1:2, ncol=1)) > x <- unlist(xx) > col <- unlist(zz) > y <- unlist(yy) > yN <- unlist(yyN) > plot(x, y, col=col, ylim=c(-10,10)) > plot(x, yN, col=col, ylim=c(-10,10)) > > proc.time() user system elapsed 0.441 0.072 0.497
aroma.light.Rcheck/tests/normalizeFragmentLength-ex1.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Example 1: Single-enzyme fragment-length normalization of 6 arrays > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Number samples > I <- 9 > > # Number of loci > J <- 1000 > > # Fragment lengths > fl <- seq(from=100, to=1000, length.out=J) > > # Simulate data points with unknown fragment lengths > hasUnknownFL <- seq(from=1, to=J, by=50) > fl[hasUnknownFL] <- NA_real_ > > # Simulate data > y <- matrix(0, nrow=J, ncol=I) > maxY <- 12 > for (kk in 1:I) { + k <- runif(n=1, min=3, max=5) + mu <- function(fl) { + mu <- rep(maxY, length(fl)) + ok <- !is.na(fl) + mu[ok] <- mu[ok] - fl[ok]^{1/k} + mu + } + eps <- rnorm(J, mean=0, sd=1) + y[,kk] <- mu(fl) + eps + } > > # Normalize data (to a zero baseline) > yN <- apply(y, MARGIN=2, FUN=function(y) { + normalizeFragmentLength(y, fragmentLengths=fl, onMissing="median") + }) > > # The correction factors > rho <- y-yN > print(summary(rho)) V1 V2 V3 V4 Min. :6.833 Min. :6.141 Min. :7.489 Min. :4.645 1st Qu.:7.104 1st Qu.:6.526 1st Qu.:7.709 1st Qu.:5.268 Median :7.423 Median :7.025 Median :7.983 Median :5.934 Mean :7.566 Mean :7.137 Mean :8.125 Mean :6.057 3rd Qu.:7.985 3rd Qu.:7.706 3rd Qu.:8.510 3rd Qu.:6.785 Max. :8.767 Max. :8.517 Max. :9.183 Max. :7.966 V5 V6 V7 V8 Min. :7.589 Min. :6.179 Min. :7.378 Min. :2.518 1st Qu.:7.852 1st Qu.:6.573 1st Qu.:7.569 1st Qu.:3.483 Median :8.199 Median :6.970 Median :7.916 Median :4.423 Mean :8.283 Mean :7.085 Mean :8.035 Mean :4.601 3rd Qu.:8.701 3rd Qu.:7.570 3rd Qu.:8.453 3rd Qu.:5.670 Max. :9.243 Max. :8.343 Max. :9.133 Max. :7.257 V9 Min. :4.671 1st Qu.:5.395 Median :6.064 Mean :6.137 3rd Qu.:6.853 Max. :7.860 > # The correction for units with unknown fragment lengths > # equals the median correction factor of all other units > print(summary(rho[hasUnknownFL,])) V1 V2 V3 V4 Min. :7.423 Min. :7.025 Min. :7.983 Min. :5.934 1st Qu.:7.423 1st Qu.:7.025 1st Qu.:7.983 1st Qu.:5.934 Median :7.423 Median :7.025 Median :7.983 Median :5.934 Mean :7.423 Mean :7.025 Mean :7.983 Mean :5.934 3rd Qu.:7.423 3rd Qu.:7.025 3rd Qu.:7.983 3rd Qu.:5.934 Max. :7.423 Max. :7.025 Max. :7.983 Max. :5.934 V5 V6 V7 V8 V9 Min. :8.199 Min. :6.97 Min. :7.916 Min. :4.423 Min. :6.064 1st Qu.:8.199 1st Qu.:6.97 1st Qu.:7.916 1st Qu.:4.423 1st Qu.:6.064 Median :8.199 Median :6.97 Median :7.916 Median :4.423 Median :6.064 Mean :8.199 Mean :6.97 Mean :7.916 Mean :4.423 Mean :6.064 3rd Qu.:8.199 3rd Qu.:6.97 3rd Qu.:7.916 3rd Qu.:4.423 3rd Qu.:6.064 Max. :8.199 Max. :6.97 Max. :7.916 Max. :4.423 Max. :6.064 > > # Plot raw data > layout(matrix(1:9, ncol=3)) > xlim <- c(0,max(fl, na.rm=TRUE)) > ylim <- c(0,max(y, na.rm=TRUE)) > xlab <- "Fragment length" > ylab <- expression(log2(theta)) > for (kk in 1:I) { + plot(fl, y[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab) + ok <- (is.finite(fl) & is.finite(y[,kk])) + lines(lowess(fl[ok], y[ok,kk]), col="red", lwd=2) + } > > # Plot normalized data > layout(matrix(1:9, ncol=3)) > ylim <- c(-1,1)*max(y, na.rm=TRUE)/2 > for (kk in 1:I) { + plot(fl, yN[,kk], xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab) + ok <- (is.finite(fl) & is.finite(y[,kk])) + lines(lowess(fl[ok], yN[ok,kk]), col="blue", lwd=2) + } > > proc.time() user system elapsed 1.103 0.100 1.188
aroma.light.Rcheck/tests/normalizeFragmentLength-ex2.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > # Example 2: Two-enzyme fragment-length normalization of 6 arrays > # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - > set.seed(0xbeef) > > # Number samples > I <- 5 > > # Number of loci > J <- 3000 > > # Fragment lengths (two enzymes) > fl <- matrix(0, nrow=J, ncol=2) > fl[,1] <- seq(from=100, to=1000, length.out=J) > fl[,2] <- seq(from=1000, to=100, length.out=J) > > # Let 1/2 of the units be on both enzymes > fl[seq(from=1, to=J, by=4),1] <- NA_real_ > fl[seq(from=2, to=J, by=4),2] <- NA_real_ > > # Let some have unknown fragment lengths > hasUnknownFL <- seq(from=1, to=J, by=15) > fl[hasUnknownFL,] <- NA_real_ > > # Sty/Nsp mixing proportions: > rho <- rep(1, I) > rho[1] <- 1/3; # Less Sty in 1st sample > rho[3] <- 3/2; # More Sty in 3rd sample > > > # Simulate data > z <- array(0, dim=c(J,2,I)) > maxLog2Theta <- 12 > for (ii in 1:I) { + # Common effect for both enzymes + mu <- function(fl) { + k <- runif(n=1, min=3, max=5) + mu <- rep(maxLog2Theta, length(fl)) + ok <- is.finite(fl) + mu[ok] <- mu[ok] - fl[ok]^{1/k} + mu + } + + # Calculate the effect for each data point + for (ee in 1:2) { + z[,ee,ii] <- mu(fl[,ee]) + } + + # Update the Sty/Nsp mixing proportions + ee <- 2 + z[,ee,ii] <- rho[ii]*z[,ee,ii] + + # Add random errors + for (ee in 1:2) { + eps <- rnorm(J, mean=0, sd=1/sqrt(2)) + z[,ee,ii] <- z[,ee,ii] + eps + } + } > > > hasFl <- is.finite(fl) > > unitSets <- list( + nsp = which( hasFl[,1] & !hasFl[,2]), + sty = which(!hasFl[,1] & hasFl[,2]), + both = which( hasFl[,1] & hasFl[,2]), + none = which(!hasFl[,1] & !hasFl[,2]) + ) > > # The observed data is a mix of two enzymes > theta <- matrix(NA_real_, nrow=J, ncol=I) > > # Single-enzyme units > for (ee in 1:2) { + uu <- unitSets[[ee]] + theta[uu,] <- 2^z[uu,ee,] + } > > # Both-enzyme units (sum on intensity scale) > uu <- unitSets$both > theta[uu,] <- (2^z[uu,1,]+2^z[uu,2,])/2 > > # Missing units (sample from the others) > uu <- unitSets$none > theta[uu,] <- apply(theta, MARGIN=2, sample, size=length(uu)) > > # Calculate target array > thetaT <- rowMeans(theta, na.rm=TRUE) > targetFcns <- list() > for (ee in 1:2) { + uu <- unitSets[[ee]] + fit <- lowess(fl[uu,ee], log2(thetaT[uu])) + class(fit) <- "lowess" + targetFcns[[ee]] <- function(fl, ...) { + predict(fit, newdata=fl) + } + } > > > # Fit model only to a subset of the data > subsetToFit <- setdiff(1:J, seq(from=1, to=J, by=10)) > > # Normalize data (to a target baseline) > thetaN <- matrix(NA_real_, nrow=J, ncol=I) > fits <- vector("list", I) > for (ii in 1:I) { + lthetaNi <- normalizeFragmentLength(log2(theta[,ii]), targetFcns=targetFcns, + fragmentLengths=fl, onMissing="median", + subsetToFit=subsetToFit, .returnFit=TRUE) + fits[[ii]] <- attr(lthetaNi, "modelFit") + thetaN[,ii] <- 2^lthetaNi + } > > > # Plot raw data > xlim <- c(0, max(fl, na.rm=TRUE)) > ylim <- c(0, max(log2(theta), na.rm=TRUE)) > Mlim <- c(-1,1)*4 > xlab <- "Fragment length" > ylab <- expression(log2(theta)) > Mlab <- expression(M==log[2](theta/theta[R])) > > layout(matrix(1:(3*I), ncol=I, byrow=TRUE)) > for (ii in 1:I) { + plot(NA, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, main="raw") + + # Single-enzyme units + for (ee in 1:2) { + # The raw data + uu <- unitSets[[ee]] + points(fl[uu,ee], log2(theta[uu,ii]), col=ee+1) + } + + # Both-enzyme units (use fragment-length for enzyme #1) + uu <- unitSets$both + points(fl[uu,1], log2(theta[uu,ii]), col=3+1) + + for (ee in 1:2) { + # The true effects + uu <- unitSets[[ee]] + lines(lowess(fl[uu,ee], log2(theta[uu,ii])), col="black", lwd=4, lty=3) + + # The estimated effects + fit <- fits[[ii]][[ee]]$fit + lines(fit, col="orange", lwd=3) + + muT <- targetFcns[[ee]](fl[uu,ee]) + lines(fl[uu,ee], muT, col="cyan", lwd=1) + } + } > > # Calculate log-ratios > thetaR <- rowMeans(thetaN, na.rm=TRUE) > M <- log2(thetaN/thetaR) > > # Plot normalized data > for (ii in 1:I) { + plot(NA, xlim=xlim, ylim=Mlim, xlab=xlab, ylab=Mlab, main="normalized") + # Single-enzyme units + for (ee in 1:2) { + # The normalized data + uu <- unitSets[[ee]] + points(fl[uu,ee], M[uu,ii], col=ee+1) + } + # Both-enzyme units (use fragment-length for enzyme #1) + uu <- unitSets$both + points(fl[uu,1], M[uu,ii], col=3+1) + } > > ylim <- c(0,1.5) > for (ii in 1:I) { + data <- list() + for (ee in 1:2) { + # The normalized data + uu <- unitSets[[ee]] + data[[ee]] <- M[uu,ii] + } + uu <- unitSets$both + if (length(uu) > 0) + data[[3]] <- M[uu,ii] + + uu <- unitSets$none + if (length(uu) > 0) + data[[4]] <- M[uu,ii] + + cols <- seq_along(data)+1 + plotDensity(data, col=cols, xlim=Mlim, xlab=Mlab, main="normalized") + + abline(v=0, lty=2) + } > > > proc.time() user system elapsed 0.886 0.110 0.980
aroma.light.Rcheck/tests/normalizeQuantileRank.list.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate ten samples of different lengths > N <- 10000 > X <- list() > for (kk in 1:8) { + rfcn <- list(rnorm, rgamma)[[sample(2, size=1)]] + size <- runif(1, min=0.3, max=1) + a <- rgamma(1, shape=20, rate=10) + b <- rgamma(1, shape=10, rate=10) + values <- rfcn(size*N, a, b) + + # "Censor" values + values[values < 0 | values > 8] <- NA_real_ + + X[[kk]] <- values + } > > # Add 20% missing values > X <- lapply(X, FUN=function(x) { + x[sample(length(x), size=0.20*length(x))] <- NA_real_ + x + }) > > # Normalize quantiles > Xn <- normalizeQuantile(X) > > # Plot the data > layout(matrix(1:2, ncol=1)) > xlim <- range(X, na.rm=TRUE) > plotDensity(X, lwd=2, xlim=xlim, main="The original distributions") > plotDensity(Xn, lwd=2, xlim=xlim, main="The normalized distributions") > > proc.time() user system elapsed 0.292 0.063 0.346
aroma.light.Rcheck/tests/normalizeQuantileRank.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate three samples with on average 20% missing values > N <- 10000 > X <- cbind(rnorm(N, mean=3, sd=1), + rnorm(N, mean=4, sd=2), + rgamma(N, shape=2, rate=1)) > X[sample(3*N, size=0.20*3*N)] <- NA_real_ > > # Normalize quantiles > Xn <- normalizeQuantile(X) > > # Plot the data > layout(matrix(1:2, ncol=1)) > xlim <- range(X, Xn, na.rm=TRUE) > plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") > plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") > > proc.time() user system elapsed 0.362 0.065 0.419
aroma.light.Rcheck/tests/normalizeQuantileSpline.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate three samples with on average 20% missing values > N <- 10000 > X <- cbind(rnorm(N, mean=3, sd=1), + rnorm(N, mean=4, sd=2), + rgamma(N, shape=2, rate=1)) > X[sample(3*N, size=0.20*3*N)] <- NA_real_ > > # Plot the data > layout(matrix(c(1,0,2:5), ncol=2, byrow=TRUE)) > xlim <- range(X, na.rm=TRUE) > plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions") > > Xn <- normalizeQuantile(X) > plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions") > plotXYCurve(X, Xn, xlim=xlim, main="The three normalized distributions") > > Xn2 <- normalizeQuantileSpline(X, xTarget=Xn[,1], spar=0.99) > plotDensity(Xn2, lwd=2, xlim=xlim, main="The three normalized distributions") > plotXYCurve(X, Xn2, xlim=xlim, main="The three normalized distributions") > > proc.time() user system elapsed 0.674 0.121 0.785
aroma.light.Rcheck/tests/normalizeTumorBoost,flavors.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > library("R.utils") Loading required package: R.oo Loading required package: R.methodsS3 R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help. R.oo v1.26.0 (2024-01-24 05:12:50 UTC) successfully loaded. See ?R.oo for help. Attaching package: 'R.oo' The following object is masked from 'package:R.methodsS3': throw The following objects are masked from 'package:methods': getClasses, getMethods The following objects are masked from 'package:base': attach, detach, load, save R.utils v2.12.3 (2023-11-18 01:00:02 UTC) successfully loaded. See ?R.utils for help. Attaching package: 'R.utils' The following object is masked from 'package:utils': timestamp The following objects are masked from 'package:base': cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings > > # Load data > pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light") > data <- loadObject(pathname) > > # Drop loci with missing values > data <- na.omit(data) > > attachLocally(data) > pos <- position/1e6 > > # Call naive genotypes > muN <- callNaiveGenotypes(betaN) > > # Genotype classes > isAA <- (muN == 0) > isAB <- (muN == 1/2) > isBB <- (muN == 1) > > # Sanity checks > stopifnot(all(muN[isAA] == 0)) > stopifnot(all(muN[isAB] == 1/2)) > stopifnot(all(muN[isBB] == 1)) > > # TumorBoost normalization with different flavors > betaTNs <- list() > for (flavor in c("v1", "v2", "v3", "v4")) { + betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE, flavor=flavor) + + # Assert that no non-finite values are introduced + stopifnot(all(is.finite(betaTN))) + + # Assert that nothing is flipped + stopifnot(all(betaTN[isAA] < 1/2)) + stopifnot(all(betaTN[isBB] > 1/2)) + + betaTNs[[flavor]] <- betaTN + } > > # Plot > layout(matrix(1:4, ncol=1)) > par(mar=c(2.5,4,0.5,1)+0.1) > ylim <- c(-0.05, 1.05) > col <- rep("#999999", length(muN)) > col[muN == 1/2] <- "#000000" > for (flavor in names(betaTNs)) { + betaTN <- betaTNs[[flavor]] + ylab <- sprintf("betaTN[%s]", flavor) + plot(pos, betaTN, col=col, ylim=ylim, ylab=ylab) + } > > proc.time() user system elapsed 0.294 0.064 0.356
aroma.light.Rcheck/tests/normalizeTumorBoost.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > library("R.utils") Loading required package: R.oo Loading required package: R.methodsS3 R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help. R.oo v1.26.0 (2024-01-24 05:12:50 UTC) successfully loaded. See ?R.oo for help. Attaching package: 'R.oo' The following object is masked from 'package:R.methodsS3': throw The following objects are masked from 'package:methods': getClasses, getMethods The following objects are masked from 'package:base': attach, detach, load, save R.utils v2.12.3 (2023-11-18 01:00:02 UTC) successfully loaded. See ?R.utils for help. Attaching package: 'R.utils' The following object is masked from 'package:utils': timestamp The following objects are masked from 'package:base': cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings > > # Load data > pathname <- system.file("data-ex/TumorBoost,fracB,exampleData.Rbin", package="aroma.light") > data <- loadObject(pathname) > attachLocally(data) > pos <- position/1e6 > muN <- genotypeN > > layout(matrix(1:4, ncol=1)) > par(mar=c(2.5,4,0.5,1)+0.1) > ylim <- c(-0.05, 1.05) > col <- rep("#999999", length(muN)) > col[muN == 1/2] <- "#000000" > > # Allele B fractions for the normal sample > plot(pos, betaN, col=col, ylim=ylim) > > # Allele B fractions for the tumor sample > plot(pos, betaT, col=col, ylim=ylim) > > # TumorBoost w/ naive genotype calls > betaTN <- normalizeTumorBoost(betaT=betaT, betaN=betaN, preserveScale=FALSE) > plot(pos, betaTN, col=col, ylim=ylim) > > # TumorBoost w/ external multi-sample genotype calls > betaTNx <- normalizeTumorBoost(betaT=betaT, betaN=betaN, muN=muN, preserveScale=FALSE) > plot(pos, betaTNx, col=col, ylim=ylim) > > proc.time() user system elapsed 0.569 0.084 0.639
aroma.light.Rcheck/tests/robustSmoothSpline.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > data(cars) > attach(cars) > plot(speed, dist, main = "data(cars) & robust smoothing splines") > > # Fit a smoothing spline using L_2 norm > cars.spl <- smooth.spline(speed, dist) > lines(cars.spl, col = "blue") > > # Fit a smoothing spline using L_1 norm > cars.rspl <- robustSmoothSpline(speed, dist) > lines(cars.rspl, col = "red") > > # Fit a smoothing spline using L_2 norm with 10 degrees of freedom > lines(smooth.spline(speed, dist, df=10), lty=2, col = "blue") > > # Fit a smoothing spline using L_1 norm with 10 degrees of freedom > lines(robustSmoothSpline(speed, dist, df=10), lty=2, col = "red") > > # Fit a smoothing spline using Tukey's biweight norm > cars.rspl <- robustSmoothSpline(speed, dist, method = "symmetric") > lines(cars.rspl, col = "purple") > > legend(5,120, c( + paste("smooth.spline [C.V.] => df =",round(cars.spl$df,1)), + paste("robustSmoothSpline L1 [C.V.] => df =",round(cars.rspl$df,1)), + paste("robustSmoothSpline symmetric [C.V.] => df =",round(cars.rspl$df,1)), + "standard with s( * , df = 10)", "robust with s( * , df = 10)" + ), + col = c("blue","red","purple","blue","red"), lty = c(1,1,1,2,2), + bg='bisque') > > proc.time() user system elapsed 0.360 0.073 0.422
aroma.light.Rcheck/tests/rowAverages.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > X <- matrix(1:30, nrow=5L, ncol=6L) > mu <- rowMeans(X) > sd <- apply(X, MARGIN=1L, FUN=sd) > > y <- rowAverages(X) > stopifnot(all(y == mu)) > stopifnot(all(attr(y,"deviance") == sd)) > stopifnot(all(attr(y,"df") == ncol(X))) > > proc.time() user system elapsed 0.229 0.057 0.281
aroma.light.Rcheck/tests/sampleCorrelations.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # Simulate 20000 genes with 10 observations each > X <- matrix(rnorm(n=20000), ncol=10) > > # Calculate the correlation for 5000 random gene pairs > cor <- sampleCorrelations(X, npairs=5000) > print(summary(cor)) Min. 1st Qu. Median Mean 3rd Qu. Max. -0.899675 -0.237085 -0.003967 0.002192 0.242832 0.925294 > > > proc.time() user system elapsed 0.283 0.051 0.335
aroma.light.Rcheck/tests/sampleTuples.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > pairs <- sampleTuples(1:10, size=5, length=2) > print(pairs) [,1] [,2] [1,] 1 2 [2,] 7 2 [3,] 1 9 [4,] 7 8 [5,] 9 10 > > triples <- sampleTuples(1:10, size=5, length=3) > print(triples) [,1] [,2] [,3] [1,] 3 7 10 [2,] 10 9 2 [3,] 5 3 7 [4,] 4 8 7 [5,] 7 2 1 > > # Allow tuples with repeated elements > quadruples <- sampleTuples(1:3, size=5, length=4, replace=TRUE) > print(quadruples) [,1] [,2] [,3] [,4] [1,] 1 3 1 2 [2,] 1 3 3 3 [3,] 1 2 3 1 [4,] 3 3 1 3 [5,] 3 3 2 3 > > proc.time() user system elapsed 0.259 0.057 0.308
aroma.light.Rcheck/tests/wpca.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > for (zzz in 0) { + + # This example requires plot3d() in R.basic [http://www.braju.com/R/] + if (!require(pkgName <- "R.basic", character.only=TRUE)) break + + # ------------------------------------------------------------- + # A first example + # ------------------------------------------------------------- + # Simulate data from the model y <- a + bx + eps(bx) + x <- rexp(1000) + a <- c(2,15,3) + b <- c(2,3,15) + bx <- outer(b,x) + eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(x), mean=0, sd=0.1*x)) + y <- a + bx + eps + y <- t(y) + + # Add some outliers by permuting the dimensions for 1/3 of the observations + idx <- sample(1:nrow(y), size=1/3*nrow(y)) + y[idx,] <- y[idx,c(2,3,1)] + + # Down-weight the outliers W times to demonstrate how weights are used + W <- 10 + + # Plot the data with fitted lines at four different view points + N <- 4 + theta <- seq(0,180,length.out=N) + phi <- rep(30, length.out=N) + + # Use a different color for each set of weights + col <- topo.colors(W) + + opar <- par(mar=c(1,1,1,1)+0.1) + layout(matrix(1:N, nrow=2, byrow=TRUE)) + for (kk in seq(theta)) { + # Plot the data + plot3d(y, theta=theta[kk], phi=phi[kk]) + + # First, same weights for all observations + w <- rep(1, length=nrow(y)) + + for (ww in 1:W) { + # Fit a line using IWPCA through data + fit <- wpca(y, w=w, swapDirections=TRUE) + + # Get the first principal component + ymid <- fit$xMean + d0 <- apply(y, MARGIN=2, FUN=min) - ymid + d1 <- apply(y, MARGIN=2, FUN=max) - ymid + b <- fit$vt[1,] + y0 <- -b * max(abs(d0)) + y1 <- b * max(abs(d1)) + yline <- matrix(c(y0,y1), nrow=length(b), ncol=2) + yline <- yline + ymid + + points3d(t(ymid), col=col) + lines3d(t(yline), col=col) + + # Down-weight outliers only, because here we know which they are. + w[idx] <- w[idx]/2 + } + + # Highlight the last one + lines3d(t(yline), col="red", lwd=3) + } + + par(opar) + + } # for (zzz in 0) Loading required package: R.basic Warning message: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, : there is no package called 'R.basic' > rm(zzz) > > proc.time() user system elapsed 0.361 0.063 0.410
aroma.light.Rcheck/tests/wpca2.matrix.Rout
R version 4.4.0 Patched (2024-04-24 r86482) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("aroma.light") aroma.light v3.35.0 (2024-06-09) successfully loaded. See ?aroma.light for help. > > # ------------------------------------------------------------- > # A second example > # ------------------------------------------------------------- > # Data > x <- c(1,2,3,4,5) > y <- c(2,4,3,3,6) > > opar <- par(bty="L") > opalette <- palette(c("blue", "red", "black")) > xlim <- ylim <- c(0,6) > > # Plot the data and the center mass > plot(x,y, pch=16, cex=1.5, xlim=xlim, ylim=ylim) > points(mean(x), mean(y), cex=2, lwd=2, col="blue") > > > # Linear regression y ~ x > fit <- lm(y ~ x) > abline(fit, lty=1, col=1) > > # Linear regression y ~ x through without intercept > fit <- lm(y ~ x - 1) > abline(fit, lty=2, col=1) > > > # Linear regression x ~ y > fit <- lm(x ~ y) > c <- coefficients(fit) > b <- 1/c[2] > a <- -b*c[1] > abline(a=a, b=b, lty=1, col=2) > > # Linear regression x ~ y through without intercept > fit <- lm(x ~ y - 1) > b <- 1/coefficients(fit) > abline(a=0, b=b, lty=2, col=2) > > > # Orthogonal linear "regression" > fit <- wpca(cbind(x,y)) > > b <- fit$vt[1,2]/fit$vt[1,1] > a <- fit$xMean[2]-b*fit$xMean[1] > abline(a=a, b=b, lwd=2, col=3) > > # Orthogonal linear "regression" without intercept > fit <- wpca(cbind(x,y), center=FALSE) > b <- fit$vt[1,2]/fit$vt[1,1] > a <- fit$xMean[2]-b*fit$xMean[1] > abline(a=a, b=b, lty=2, lwd=2, col=3) > > legend(xlim[1],ylim[2], legend=c("lm(y~x)", "lm(y~x-1)", "lm(x~y)", + "lm(x~y-1)", "pca", "pca w/o intercept"), lty=rep(1:2,3), + lwd=rep(c(1,1,2),each=2), col=rep(1:3,each=2)) > > palette(opalette) > par(opar) > > proc.time() user system elapsed 0.346 0.065 0.395
aroma.light.Rcheck/aroma.light-Ex.timings
name | user | system | elapsed | |
backtransformAffine | 0.002 | 0.002 | 0.005 | |
backtransformPrincipalCurve | 0.489 | 0.018 | 0.508 | |
calibrateMultiscan | 0.000 | 0.000 | 0.001 | |
callNaiveGenotypes | 0.243 | 0.019 | 0.268 | |
distanceBetweenLines | 0.092 | 0.005 | 0.097 | |
findPeaksAndValleys | 0.043 | 0.004 | 0.046 | |
fitPrincipalCurve | 0.651 | 0.023 | 0.674 | |
fitXYCurve | 0.301 | 0.005 | 0.306 | |
iwpca | 0.062 | 0.001 | 0.065 | |
likelihood.smooth.spline | 0.155 | 0.006 | 0.171 | |
medianPolish | 0.006 | 0.001 | 0.007 | |
normalizeAffine | 8.305 | 0.079 | 8.387 | |
normalizeCurveFit | 8.494 | 0.096 | 8.593 | |
normalizeDifferencesToAverage | 0.336 | 0.012 | 0.348 | |
normalizeFragmentLength | 1.798 | 0.069 | 1.881 | |
normalizeQuantileRank | 1.295 | 0.013 | 1.311 | |
normalizeQuantileRank.matrix | 0.055 | 0.004 | 0.060 | |
normalizeQuantileSpline | 0.801 | 0.064 | 0.866 | |
normalizeTumorBoost | 0.296 | 0.021 | 0.320 | |
robustSmoothSpline | 0.581 | 0.011 | 0.593 | |
sampleCorrelations | 0.219 | 0.008 | 0.229 | |
sampleTuples | 0.001 | 0.000 | 0.001 | |
wpca | 0.060 | 0.004 | 0.065 | |