MoPS 1.21.0
Philipp Eser
| Snapshot Date: 2020-03-11 16:45:12 -0400 (Wed, 11 Mar 2020) |
| URL: https://git.bioconductor.org/packages/MoPS |
| Branch: master |
| Last Commit: 93eb249 |
| Last Changed Date: 2019-10-29 13:37:33 -0400 (Tue, 29 Oct 2019) |
| malbec2 | Linux (Ubuntu 18.04.4 LTS) / x86_64 | OK | OK | [ ERROR ] | | |
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | ERROR | OK | |
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### Running command:
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### /home/biocbuild/bbs-3.11-bioc/R/bin/R CMD check --install=check:MoPS.install-out.txt --library=/home/biocbuild/bbs-3.11-bioc/R/library --no-vignettes --timings MoPS_1.21.0.tar.gz
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* using log directory ‘/home/biocbuild/bbs-3.11-bioc/meat/MoPS.Rcheck’
* using R Under development (unstable) (2020-01-28 r77731)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘MoPS/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘MoPS’ version ‘1.21.0’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘MoPS’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... 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 dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
fit.periodic: no visible global function definition for ‘lm’
fit.periodic: no visible global function definition for ‘cor’
fun.lognormal: no visible global function definition for ‘dlnorm’
fun.lognormal.psi: no visible global function definition for ‘dlnorm’
makewave: no visible global function definition for ‘integrate’
pathfunction: no visible global function definition for ‘approxfun’
Undefined global functions or variables:
approxfun cor dlnorm integrate lm
Consider adding
importFrom("stats", "approxfun", "cor", "dlnorm", "integrate", "lm")
to your NAMESPACE file.
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... ERROR
Running examples in ‘MoPS-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: fit.periodic
> ### Title: Fitting of periodic curves to time series data
> ### Aliases: fit.periodic
>
> ### ** Examples
>
>
> x = seq(0,40,by=1) # time points
>
> ## create 10 periodic time series with added noise
> mat.p = matrix(rep(x,10),nrow=10,ncol=length(x),byrow=TRUE)
> y = -seq(1:10)
> mat.p = apply(mat.p,2,function(x){
+ y = sin(pi*(x/41*6)+y)+rnorm(length(x),sd=1)
+ })
>
> ## add 10 non-periodic noisy time series
> mat.nonP = matrix(rep(x,10),nrow=10,ncol=length(x),byrow=TRUE)
> mat.nonP = apply(mat.nonP,2,function(x){
+ y = rnorm(length(x),sd=1)
+ })
>
> mat = rbind(mat.p,mat.nonP)
>
> res = fit.periodic(mat,phi=seq(0,20,1),lambda=seq(1,20,1))
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
MoPS
--- call from context ---
fit.periodic(mat, phi = seq(0, 20, 1), lambda = seq(1, 20, 1))
--- call from argument ---
if (class(mat) == "ExpressionSet") {
mat = exprs(mat)
}
--- R stacktrace ---
where 1: fit.periodic(mat, phi = seq(0, 20, 1), lambda = seq(1, 20, 1))
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (mat, timepoints = NULL, phi = NULL, lambda = NULL,
sigma = NULL, psi = NULL, weights = NULL)
{
if (class(mat) == "ExpressionSet") {
mat = exprs(mat)
}
if (!is.null(weights)) {
if (!length(mat) == length(weights)) {
stop("Error: wrong dimension of weights matrix")
}
if (is.null(rownames(mat)) | is.null(rownames(weights)) |
!rownames(mat) == rownames(weights)) {
message("Warning: rownames of weight matrix not equal to rownames of data matrix.")
message("replacing rownames of both matrices with ID_1,ID_2 ... ID_n")
rownames(mat) = paste("ID", 1:dim(mat)[1], sep = "_")
rownames(weights) = paste("ID", 1:dim(weights)[1],
sep = "_")
}
}
if (is.null(dim(mat))) {
mat = matrix(mat, nrow = 1)
}
if (is.null(rownames(mat))) {
rownames(mat) = paste("ID", 1:dim(mat)[1], sep = "_")
}
if (is.null(timepoints)) {
timepoints = 1:ncol(mat)
}
if (is.null(phi)) {
phi = timepoints
n.phigrid = length(phi)
}
if (is.null(lambda)) {
lambda = timepoints
n.lambdagrid = length(lambda)
}
if (is.null(sigma)) {
sigma = 0
n.sigmagrid = length(sigma)
}
if (!is.null(psi)) {
n.unitgrid = psi + 1
unitgrid = seq(from = 0, to = 2 * pi, length = n.unitgrid +
1)
psigrid = matrix(unitgrid, nrow = 1)
for (j in 1:(psi - 1)) {
hilf = apply(psigrid, 2, extend, unitgrid)
hilf = unlist(hilf)
psigrid = matrix(hilf, nrow = j + 1)
}
n.psigrid = ncol(psigrid)
}
lower.standard = timepoints[1]
if (lower.standard == 0)
lower.standard = 1
upper.standard = timepoints[length(timepoints) - 1]
testfunmat.nonPeriodic = createLinearTimeCourses(timepoints)
if (is.null(psi)) {
Glist = list()
for (s in sigma) {
for (l in lambda) {
phi.l = phi[phi <= l]
for (p in phi.l) {
Glist[[length(Glist) + 1]] = list(phi = p,
psi = NULL, lambda = l, sigma = s)
}
}
}
}
else {
Glist = list()
for (s in sigma) {
for (l in lambda) {
phi.l = phi[phi <= l]
for (p in phi.l) {
for (ps in 1:ncol(psigrid)) {
Glist[[length(Glist) + 1]] = list(phi = p,
psi = psigrid[, ps], lambda = l, sigma = s)
}
}
}
}
}
nFunctions = length(Glist)
tests = nFunctions * dim(mat)[1]
vec = mat[1, ]
if (nFunctions > 1000 & tests > 10000) {
message("Estimating time required to do the analysis .... ")
testfmatrix = list()
if (is.null(psi)) {
t1 = system.time((for (x in 1:1000) {
testfmatrix[[length(testfmatrix) + 1]] = makewave(Glist[[x]],
timepoints, lower = lower.standard, upper = upper.standard,
fun = fun.lognormal)
}))
t = t1[[1]] * nFunctions/1000
}
else {
t1 = system.time((for (x in 1:100) {
testfmatrix[[length(testfmatrix) + 1]] = makewave(Glist[[x]],
timepoints, lower = lower.standard, upper = upper.standard,
fun = fun.lognormal.psi, psi = Glist[[x]]$psi)
}))
t = t1[[1]] * nFunctions/100
}
t2 = system.time((for (x in 1:10000) {
lm(vec ~ testfmatrix[[1]])
}))
cost = (t + t2[[1]] * tests/10000)/60
message(paste(tests, "linear regressions will be performed. This will take approx.",
round(cost, 1), "minutes."))
if (cost > 30) {
message("See the MoPS vignette for help on speeding things up.")
}
}
else {
message(paste(tests, "linear regressions will be performed. This will take < 5 minutes."))
}
testfmatrix = list()
message("Creating test functions ....")
message("0 %")
percent = 10
if (!is.null(psi)) {
for (i in 1:length(Glist)) {
p = round(i/length(Glist) * 100, 0)
if (p >= percent) {
message(paste(p, "%"))
percent = percent + 10
}
testfmatrix[[length(testfmatrix) + 1]] = makewave(Glist[[i]],
timepoints, lower = lower.standard, upper = upper.standard,
fun = fun.lognormal.psi, psi = Glist[[i]]$psi)
}
}
else {
for (i in 1:length(Glist)) {
p = round(i/length(Glist) * 100, 0)
if (p >= percent) {
message(paste(p, "%"))
percent = percent + 10
}
testfmatrix[[length(testfmatrix) + 1]] = makewave(Glist[[i]],
timepoints, lower = lower.standard, upper = upper.standard,
fun = fun.lognormal)
}
}
ids = rownames(mat)
n = length(ids)
resultList = list()
message("Fitting of test functions to data ....")
message("0 %")
percent = 10
for (i in 1:n) {
p = round(i/n * 100, 0)
if (p >= percent) {
message(paste(p, "%"))
percent = percent + 10
}
vec = mat[i, ]
all.losses = rep(NA, length(testfmatrix))
for (j in 1:length(testfmatrix)) {
if (is.null(weights)) {
linmod = lm(vec ~ testfmatrix[[j]])
}
else {
w = weights[i, ]
linmod = lm(vec ~ testfmatrix[[j]], weights = w)
}
loss = sum(linmod$residuals^2)
R = cor(vec, testfmatrix[[j]], use = "pairwise.complete.obs")
if (R < 0 | is.na(R)) {
loss = Inf
}
all.losses[j] = loss
}
minPos.periodic = which.min(all.losses)
loss.periodic = all.losses[minPos.periodic]
if (is.null(weights)) {
linmod = lm(vec ~ testfmatrix[[minPos.periodic]])
}
else {
linmod = lm(vec ~ testfmatrix[[minPos.periodic]],
weights = w)
}
a = linmod$coefficients[[2]]
b = linmod$coefficients[[1]]
lossL.nonPeriodic = c()
for (j in 1:dim(testfunmat.nonPeriodic)[2]) {
if (is.null(weights)) {
if (sum(is.na(vec)) > 0) {
linmod = lm(vec ~ testfunmat.nonPeriodic[,
j])
}
else {
linmod = lm(vec ~ testfunmat.nonPeriodic[,
j])
}
}
else {
linmod = lm(vec ~ testfunmat.nonPeriodic[, j],
weights = w)
}
loss = sum(linmod$residuals^2)
lossL.nonPeriodic = c(lossL.nonPeriodic, loss)
}
minPos.nonPeriodic = which.min(lossL.nonPeriodic)
loss.nonPeriodic = lossL.nonPeriodic[minPos.nonPeriodic]
if (is.null(weights)) {
linmod = lm(vec ~ testfunmat.nonPeriodic[, minPos.nonPeriodic])
}
else {
linmod = lm(vec ~ testfunmat.nonPeriodic[, minPos.nonPeriodic],
weights = w)
}
loss.nonPeriodic = sum(linmod$residuals^2)
is.weakly.periodic = loss.periodic <= loss.nonPeriodic
score = log(loss.nonPeriodic/loss.periodic)
resultList[[i]] = list(ID = ids[i], score = score, minLossPeriodic = loss.periodic,
minLossNonPeriodic = loss.nonPeriodic, phi = Glist[[minPos.periodic]]$phi,
psi = Glist[[minPos.periodic]]$psi, lambda = Glist[[minPos.periodic]]$lambda,
sigma = Glist[[minPos.periodic]]$sigma, a.coef = a,
b.coef = b)
}
result = list(fitted = resultList, time = timepoints, cols.mat = ncol(mat),
phi = phi, lambda = lambda, sigma = sigma)
return(result)
}
<bytecode: 0x56202f13fcd0>
<environment: namespace:MoPS>
--- function search by body ---
Function fit.periodic in namespace MoPS has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: the condition has length > 1
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 1 ERROR, 1 NOTE
See
‘/home/biocbuild/bbs-3.11-bioc/meat/MoPS.Rcheck/00check.log’
for details.