| Back to Multiple platform build/check report for BioC 3.18: simplified long |
|
This page was generated on 2024-04-17 11:37:30 -0400 (Wed, 17 Apr 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4676 |
| palomino4 | Windows Server 2022 Datacenter | x64 | 4.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" | 4414 |
| merida1 | macOS 12.7.1 Monterey | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4437 |
| 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 88/2266 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| aroma.light 3.32.0 (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 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | | ||||||||
| kjohnson1 | macOS 13.6.1 Ventura / arm64 | see weekly results here | ||||||||||||
|
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: 3.32.0 |
| 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_3.32.0.tar.gz |
| StartedAt: 2024-04-15 23:18:59 -0400 (Mon, 15 Apr 2024) |
| EndedAt: 2024-04-15 23:21:25 -0400 (Mon, 15 Apr 2024) |
| EllapsedTime: 146.5 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_3.32.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.18-bioc/meat/aroma.light.Rcheck’
* using R version 4.3.3 (2024-02-29)
* using platform: x86_64-apple-darwin20 (64-bit)
* R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘aroma.light/DESCRIPTION’ ... OK
* this is package ‘aroma.light’ version ‘3.32.0’
* 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 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 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 11.852 0.172 12.617
normalizeAffine 11.543 0.160 12.222
* 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-3.18-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/4.3-x86_64/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 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.514 0.136 0.625
aroma.light.Rcheck/tests/backtransformPrincipalCurve.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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,] 0.9999157
> stopifnot(rho > 0.999)
>
> proc.time()
user system elapsed
1.434 0.188 1.611
aroma.light.Rcheck/tests/callNaiveGenotypes.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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 -0.003129902 1.6762520809
2 valley 0.493706814 0.0004731272
3 peak 0.994940316 1.6916663186
> 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.
-0.446527 0.001378 0.472427 0.500437 0.999511 1.420750
[1] 20000
After:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-Inf 0.001378 0.472427 0.999511 Inf
[1] 16815
Censoring BAFs...done
Copy number level #1 (C=1) of 1...
Identified extreme points in density of BAF:
type x density
1 peak 0.01495573 1.63285844
2 valley 0.50171410 0.00409257
3 peak 0.97818885 1.64319337
Local minimas ("valleys") in BAF:
type x density
2 valley 0.5017141 0.00409257
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]]$tau
[1] 0.5017141
[[1]]$n
[1] 16815
[[1]]$fit
type x density
1 peak 0.01495573 1.63285844
2 valley 0.50171410 0.00409257
3 peak 0.97818885 1.64319337
[[1]]$fitValleys
type x density
2 valley 0.5017141 0.00409257
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] 0.5017141
$n
[1] 16815
$fit
type x density
1 peak 0.01495573 1.63285844
2 valley 0.50171410 0.00409257
3 peak 0.97818885 1.64319337
$fitValleys
type x density
2 valley 0.5017141 0.00409257
Genotype threshholds [1]: 0.501714100177555
TCN=1 => BAF in {0,1}.
Call regions: A = (-Inf,0.502], B = (0.502,+Inf)
Copy number level #1 (C=1) of 1...done
Calling genotypes from allele B fractions (BAFs)...done
> xc <- split(x, calls)
> print(table(calls))
calls
0 1
10000 10000
> xx <- c(list(x),xc)
> plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,BB)")
> abline(v=fit$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 -0.001763535 1.1638234
2 valley 0.247920853 0.1941489
3 peak 0.493443834 1.1760037
4 valley 0.743128222 0.1873210
5 peak 0.996974016 1.1803607
> calls <- callNaiveGenotypes(x)
> xc <- split(x, calls)
> print(table(calls))
calls
0 0.5 1
9556 9334 9654
> xx <- c(list(x),xc)
> plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA,AB,BB)")
> abline(v=fit$x)
>
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution with clear separation
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> xAA <- rnorm(n=10000, mean=0, sd=0.02)
> xAB <- rnorm(n=10000, mean=1/2, sd=0.02)
> xBB <- rnorm(n=10000, mean=1, sd=0.02)
> x <- c(xAA,xAB,xBB)
> fit <- findPeaksAndValleys(x)
> print(fit)
type x density
1 peak -0.004535432 2.605630e+00
2 valley 0.246538544 3.315350e-05
3 peak 0.497612520 2.607926e+00
4 valley 0.745896785 3.386239e-05
5 peak 0.996970761 2.604734e+00
> calls <- callNaiveGenotypes(x)
> xc <- split(x, calls)
> print(table(calls))
calls
0 0.5 1
10000 10000 10000
> xx <- c(list(x),xc)
> plotDensity(xx, adjust=1.5, lwd=2, col=seq_along(xx), main="(AA',AB',BB')")
> abline(v=fit$x)
>
> proc.time()
user system elapsed
1.010 0.163 1.144
aroma.light.Rcheck/tests/distanceBetweenLines.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.744 0.144 0.856
aroma.light.Rcheck/tests/findPeaksAndValleys.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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 0.0622383 0.3893393
> plot(density(x), lwd=2, main="x1")
> abline(v=fit$x)
>
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> # A trimodal distribution
> # - - - - - - - - - - - - - - - - - - - - - - - - - - - -
> x2 <- rnorm(n=10000, mean=4, sd=1)
> x3 <- rnorm(n=10000, mean=8, sd=1)
> x <- c(x1,x2,x3)
> fit <- findPeaksAndValleys(x)
> print(fit)
type x density
1 peak -0.01948767 0.12180697
2 valley 1.99220100 0.04447355
3 peak 4.00388967 0.12364274
4 valley 5.98028555 0.04543916
5 peak 7.92138865 0.12122171
> plot(density(x), lwd=2, main="c(x1,x2,x3)")
> abline(v=fit$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 -0.03088945 3.417981e-01
2 valley 1.97340915 1.261631e-06
3 peak 3.97770776 3.427656e-01
4 valley 5.98200636 1.166407e-06
5 peak 7.98630496 3.429535e-01
> plot(d, lwd=2, main="c(x1b,x2b,x3b)")
> abline(v=fit$x)
>
> proc.time()
user system elapsed
0.656 0.146 0.820
aroma.light.Rcheck/tests/fitPrincipalCurve.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) successfully loaded. See ?aroma.light for help.
>
> # Simulate data from the model y <- a + bx + x^c + eps(bx)
> J <- 1000
> x <- rexp(J)
> a <- c(2,15,3)
> b <- c(2,3,4)
> c <- c(1,2,1/2)
> bx <- outer(b,x)
> xc <- t(sapply(c, FUN=function(c) x^c))
> eps <- apply(bx, MARGIN=2, FUN=function(x) rnorm(length(b), mean=0, sd=0.1*x))
> y <- a + bx + xc + eps
> y <- t(y)
>
> # Fit principal curve through (y_1, y_2, y_3)
> fit <- fitPrincipalCurve(y, verbose=TRUE)
Fitting principal curve...
Data size: 1000x3
Identifying missing values...
Identifying missing values...done
Data size after removing non-finite data points: 1000x3
Calling principal_curve()...
Starting curve---distance^2: 2191298
Iteration 1---distance^2: 395.4208
Iteration 2---distance^2: 394.4198
Iteration 3---distance^2: 394.4252
Converged: TRUE
Number of iterations: 3
Processing time/iteration: 0.2s (0.1s/iteration)
Calling principal_curve()...done
Fitting principal curve...done
>
> # Flip direction of 'lambda'?
> rho <- cor(fit$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
1.751 0.218 2.153
aroma.light.Rcheck/tests/fitXYCurve.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.730 0.158 0.871
aroma.light.Rcheck/tests/iwpca.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.693 0.139 0.806
aroma.light.Rcheck/tests/likelihood.smooth.spline.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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: -301213.3
Log base: 2.718282
Weighted residuals sum of square: 301213.4
Penalty: -0.1237606
Smoothing parameter lambda: 0.0009257147
Roughness score: 133.6919
>
> # 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: -300872.5
Log base: 2.718282
Weighted residuals sum of square: 300872.6
Penalty: -0.1238165
Smoothing parameter lambda: 0.0009261969
Roughness score: 133.6827
>
> # 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: -301212
Log base: 2.718282
Weighted residuals sum of square: 301212.1
Penalty: -0.123816
Smoothing parameter lambda: 0.0009261969
Roughness score: 133.6822
>
>
>
>
>
>
> proc.time()
user system elapsed
0.745 0.146 0.867
aroma.light.Rcheck/tests/medianPolish.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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$resid))
>
> proc.time()
user system elapsed
0.516 0.136 0.617
aroma.light.Rcheck/tests/normalizeAffine.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) successfully loaded. See ?aroma.light for help.
>
> pathname <- system.file("data-ex", "PMT-RGData.dat", package="aroma.light")
> rg <- read.table(pathname, header=TRUE, sep="\t")
> nbrOfScans <- max(rg$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
4.327 0.341 4.676
aroma.light.Rcheck/tests/normalizeAverage.list.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.758 0.160 0.894
aroma.light.Rcheck/tests/normalizeAverage.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.634 0.143 0.743
aroma.light.Rcheck/tests/normalizeCurveFit.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
12.352 0.358 12.910
aroma.light.Rcheck/tests/normalizeDifferencesToAverage.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.771 0.153 0.893
aroma.light.Rcheck/tests/normalizeFragmentLength-ex1.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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. :3.884 Min. :3.720 Min. :6.230 Min. :6.101
1st Qu.:4.512 1st Qu.:4.491 1st Qu.:6.708 1st Qu.:6.512
Median :5.281 Median :5.229 Median :7.186 Median :6.977
Mean :5.437 Mean :5.428 Mean :7.227 Mean :7.103
3rd Qu.:6.295 3rd Qu.:6.316 3rd Qu.:7.722 3rd Qu.:7.655
Max. :7.568 Max. :7.761 Max. :8.403 Max. :8.522
V5 V6 V7 V8
Min. :3.559 Min. :5.659 Min. :7.897 Min. :2.823
1st Qu.:4.139 1st Qu.:6.042 1st Qu.:8.118 1st Qu.:3.548
Median :4.875 Median :6.564 Median :8.370 Median :4.373
Mean :5.161 Mean :6.730 Mean :8.441 Mean :4.635
3rd Qu.:6.128 3rd Qu.:7.365 3rd Qu.:8.735 3rd Qu.:5.660
Max. :7.628 Max. :8.353 Max. :9.266 Max. :7.263
V9
Min. :6.197
1st Qu.:6.716
Median :7.176
Mean :7.259
3rd Qu.:7.781
Max. :8.576
> # 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. :5.281 Min. :5.229 Min. :7.186 Min. :6.977
1st Qu.:5.281 1st Qu.:5.229 1st Qu.:7.186 1st Qu.:6.977
Median :5.281 Median :5.229 Median :7.186 Median :6.977
Mean :5.281 Mean :5.229 Mean :7.186 Mean :6.977
3rd Qu.:5.281 3rd Qu.:5.229 3rd Qu.:7.186 3rd Qu.:6.977
Max. :5.281 Max. :5.229 Max. :7.186 Max. :6.977
V5 V6 V7 V8 V9
Min. :4.875 Min. :6.564 Min. :8.37 Min. :4.373 Min. :7.176
1st Qu.:4.875 1st Qu.:6.564 1st Qu.:8.37 1st Qu.:4.373 1st Qu.:7.176
Median :4.875 Median :6.564 Median :8.37 Median :4.373 Median :7.176
Mean :4.875 Mean :6.564 Mean :8.37 Mean :4.373 Mean :7.176
3rd Qu.:4.875 3rd Qu.:6.564 3rd Qu.:8.37 3rd Qu.:4.373 3rd Qu.:7.176
Max. :4.875 Max. :6.564 Max. :8.37 Max. :4.373 Max. :7.176
>
> # 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
1.435 0.202 1.605
aroma.light.Rcheck/tests/normalizeFragmentLength-ex2.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
1.357 0.228 1.550
aroma.light.Rcheck/tests/normalizeQuantileRank.list.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.743 0.151 0.857
aroma.light.Rcheck/tests/normalizeQuantileRank.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.627 0.139 0.725
aroma.light.Rcheck/tests/normalizeQuantileSpline.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
1.756 0.242 1.966
aroma.light.Rcheck/tests/normalizeTumorBoost,flavors.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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, 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
1.034 0.199 1.211
aroma.light.Rcheck/tests/normalizeTumorBoost.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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, 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
0.937 0.195 1.108
aroma.light.Rcheck/tests/robustSmoothSpline.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.722 0.148 0.834
aroma.light.Rcheck/tests/rowAverages.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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
0.513 0.132 0.614
aroma.light.Rcheck/tests/sampleCorrelations.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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.
-0.870468 -0.242190 -0.005351 -0.001252 0.242357 0.890675
>
>
> proc.time()
user system elapsed
1.144 0.169 1.289
aroma.light.Rcheck/tests/sampleTuples.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) successfully loaded. See ?aroma.light for help.
>
> pairs <- sampleTuples(1:10, size=5, length=2)
> print(pairs)
[,1] [,2]
[1,] 4 2
[2,] 5 3
[3,] 9 4
[4,] 4 5
[5,] 3 6
>
> triples <- sampleTuples(1:10, size=5, length=3)
> print(triples)
[,1] [,2] [,3]
[1,] 5 4 9
[2,] 4 5 6
[3,] 6 5 10
[4,] 5 7 4
[5,] 2 3 8
>
> # Allow tuples with repeated elements
> quadruples <- sampleTuples(1:3, size=5, length=4, replace=TRUE)
> print(quadruples)
[,1] [,2] [,3] [,4]
[1,] 1 2 2 2
[2,] 3 2 3 3
[3,] 2 1 1 2
[4,] 1 2 1 1
[5,] 1 1 1 1
>
> proc.time()
user system elapsed
0.499 0.132 0.597
aroma.light.Rcheck/tests/wpca.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) 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$vt[1,]
+ y0 <- -b * max(abs(d0))
+ y1 <- b * max(abs(d1))
+ yline <- matrix(c(y0,y1), nrow=length(b), ncol=2)
+ yline <- yline + ymid
+
+ points3d(t(ymid), col=col)
+ lines3d(t(yline), col=col)
+
+ # Down-weight outliers only, because here we know which they are.
+ w[idx] <- w[idx]/2
+ }
+
+ # Highlight the last one
+ lines3d(t(yline), col="red", lwd=3)
+ }
+
+ par(opar)
+
+ } # for (zzz in 0)
Loading required package: R.basic
Warning message:
In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
there is no package called 'R.basic'
> rm(zzz)
>
> proc.time()
user system elapsed
0.650 0.137 0.756
aroma.light.Rcheck/tests/wpca2.matrix.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
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> library("aroma.light")
aroma.light v3.32.0 (2024-04-15) successfully loaded. See ?aroma.light for help.
>
> # -------------------------------------------------------------
> # A second example
> # -------------------------------------------------------------
> # Data
> x <- c(1,2,3,4,5)
> y <- c(2,4,3,3,6)
>
> opar <- par(bty="L")
> opalette <- palette(c("blue", "red", "black"))
> xlim <- ylim <- c(0,6)
>
> # Plot the data and the center mass
> plot(x,y, pch=16, cex=1.5, xlim=xlim, ylim=ylim)
> points(mean(x), mean(y), cex=2, lwd=2, col="blue")
>
>
> # Linear regression y ~ x
> fit <- lm(y ~ x)
> abline(fit, lty=1, col=1)
>
> # Linear regression y ~ x through without intercept
> fit <- lm(y ~ x - 1)
> abline(fit, lty=2, col=1)
>
>
> # Linear regression x ~ y
> fit <- lm(x ~ y)
> c <- coefficients(fit)
> b <- 1/c[2]
> a <- -b*c[1]
> abline(a=a, b=b, lty=1, col=2)
>
> # Linear regression x ~ y through without intercept
> fit <- lm(x ~ y - 1)
> b <- 1/coefficients(fit)
> abline(a=0, b=b, lty=2, col=2)
>
>
> # Orthogonal linear "regression"
> fit <- wpca(cbind(x,y))
>
> b <- fit$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
0.623 0.141 0.730
aroma.light.Rcheck/aroma.light-Ex.timings
| name | user | system | elapsed | |
| backtransformAffine | 0.005 | 0.006 | 0.011 | |
| backtransformPrincipalCurve | 0.898 | 0.048 | 0.978 | |
| calibrateMultiscan | 0.000 | 0.000 | 0.001 | |
| callNaiveGenotypes | 0.421 | 0.031 | 0.476 | |
| distanceBetweenLines | 0.219 | 0.008 | 0.238 | |
| findPeaksAndValleys | 0.052 | 0.006 | 0.057 | |
| fitPrincipalCurve | 0.866 | 0.046 | 0.927 | |
| fitXYCurve | 0.299 | 0.014 | 0.318 | |
| iwpca | 0.140 | 0.003 | 0.145 | |
| likelihood.smooth.spline | 0.202 | 0.008 | 0.214 | |
| medianPolish | 0.009 | 0.002 | 0.012 | |
| normalizeAffine | 11.543 | 0.160 | 12.222 | |
| normalizeCurveFit | 11.852 | 0.172 | 12.617 | |
| normalizeDifferencesToAverage | 0.364 | 0.031 | 0.414 | |
| normalizeFragmentLength | 2.099 | 0.158 | 2.295 | |
| normalizeQuantileRank | 1.177 | 0.025 | 1.217 | |
| normalizeQuantileRank.matrix | 0.062 | 0.005 | 0.067 | |
| normalizeQuantileSpline | 1.143 | 0.074 | 1.278 | |
| normalizeTumorBoost | 0.371 | 0.048 | 0.446 | |
| robustSmoothSpline | 0.565 | 0.015 | 0.584 | |
| sampleCorrelations | 0.620 | 0.013 | 0.652 | |
| sampleTuples | 0.001 | 0.001 | 0.002 | |
| wpca | 0.123 | 0.007 | 0.133 | |