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### Running command:
###
### C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:spotSegmentation.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings spotSegmentation_1.62.0.tar.gz
###
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* using log directory 'C:/Users/biocbuild/bbs-3.11-bioc/meat/spotSegmentation.Rcheck'
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'spotSegmentation/DESCRIPTION' ... OK
* this is package 'spotSegmentation' version '1.62.0'
* 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 whether package 'spotSegmentation' 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
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... NOTE
Package in Depends field not imported from: 'mclust'
These packages need to be imported from (in the NAMESPACE file)
for when this namespace is loaded but not attached.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
plot.spotseg : plotSpotImage: no visible global function definition for
'par'
plot.spotseg : plotSpotImage: no visible global function definition for
'image'
plot.spotseg: no visible global function definition for 'postscript'
plotBlockImage: no visible global function definition for 'par'
plotBlockImage: no visible global function definition for 'image'
plotBlockImage: no visible global function definition for 'gray'
spotgrid : spotgridPeaks: no visible global function definition for
'runif'
spotgrid : spotgridPeaks: no visible global function definition for
'embed'
spotgrid: no visible global function definition for 'contour'
spotseg : spotseg1 : plotSpotImage: no visible global function
definition for 'par'
spotseg : spotseg1 : plotSpotImage: no visible global function
definition for 'image'
spotseg : spotseg1: no visible global function definition for
'mclustBIC'
spotseg : spotseg1: no visible global function definition for 'hcE'
spotseg : spotseg1: no visible global function definition for 'frame'
spotseg: no visible global function definition for 'par'
spotseg: no visible global function definition for 'median'
Undefined global functions or variables:
contour embed frame gray hcE image mclustBIC median par postscript
runif
Consider adding
importFrom("grDevices", "gray", "postscript")
importFrom("graphics", "contour", "frame", "image", "par")
importFrom("stats", "embed", "median", "runif")
to your NAMESPACE file.
* checking Rd files ... NOTE
prepare_Rd: plot.spotseg.Rd:23: Dropping empty section \author
prepare_Rd: plotBlockImage.Rd:20: Dropping empty section \author
prepare_Rd: spotgrid.Rd:21: Dropping empty section \details
prepare_Rd: spotgrid.Rd:33: Dropping empty section \note
prepare_Rd: spotgrid.Rd:34: Dropping empty section \author
prepare_Rd: spotseg.Rd:45: Dropping empty section \author
prepare_Rd: summary.spotseg.Rd:27: Dropping empty section \author
* 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' ... WARNING
Files in the 'vignettes' directory but no files in 'inst/doc':
'spotsegdoc.pdf'
Package has no Sweave vignette sources and no VignetteBuilder field.
* checking examples ...
** running examples for arch 'i386' ... ERROR
Running examples in 'spotSegmentation-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot.spotseg
> ### Title: Microarray Spot Segmentation Plot
> ### Aliases: plot.spotseg
> ### Keywords: methods
>
> ### ** Examples
>
> data(spotSegTest)
>
> # columns of spotSegTest:
> # 1 intensities from the Cy3 (green) channel
> # 2 intensities from the Cy5 (red) channel
>
> dataTransformation <- function(x) (256*256-1-x)^2*4.71542407E-05
>
> chan1 <- matrix(dataTransformation(spotSegTest[,1]), 144, 199)
> chan2 <- matrix(dataTransformation(spotSegTest[,2]), 144, 199)
>
> hivGrid <- spotgrid( chan1, chan2, rows = 4, cols = 6, show = TRUE)
----------- FAILURE REPORT --------------
--- failure: length > 1 in coercion to logical ---
--- srcref ---
:
--- package (from environment) ---
spotSegmentation
--- call from context ---
spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)
--- call from argument ---
show && !is.na(rowcut)
--- R stacktrace ---
where 1: spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)
--- value of length: 5 type: logical ---
[1] TRUE TRUE TRUE TRUE TRUE
--- function from context ---
function (chan1, chan2, rows = NULL, cols = NULL, span = NULL,
show = FALSE)
{
signal <- chan1 + chan2
s <- min(signal)
r <- min(signal[signal > 0])
if (s <= 0) {
signal <- signal - s + 1
}
else if (r < 1) {
signal <- signal + 1
}
signal <- log(signal)
spotgridPeaks <- function(series, span = 3, seed = 0) {
if (!(span%%2))
span <- span + 1
d <- sort(diff(sort(series)))
if (!d[1]) {
d <- d[as.logical(d)][1]
set.seed(seed)
noise <- runif(length(series), min = -d/2, max = d/2)
series <- series + noise
}
zmaxcol <- apply(embed(series, span)[, span:1], 1, function(x) {
if (length(y <- seq(along = x)[x == max(x)]) == 1)
y
else 0
})
halfspan <- (span - 1)/2
c(rep(FALSE, halfspan), zmaxcol == 1 + halfspan)
}
isum <- colSums(signal)
gridcomp <- function(isum, nspots, span) {
N <- length(isum)
if (!(span%%2))
span <- span + 1
Peaks <- spotgridPeaks(isum, span)
Vales <- spotgridPeaks(-isum, span)
iPeaks <- (1:N)[Peaks]
iVales <- (1:N)[Vales]
nPeaks <- length(iPeaks)
nVales <- length(iVales)
if (FALSE) {
plot(1:N, isum, type = "l", xlab = "", ylab = "")
points(iPeaks, isum[iPeaks], pch = "P")
points(iVales, isum[iVales], pch = "V")
}
V <- rep(0, nPeaks - 1)
for (i in 2:nPeaks) {
K <- iPeaks[i - 1]:iPeaks[i]
I <- isum[K]
J <- K[I == min(I)]
if (length(J) > 1) {
M <- match(J, iVales, nomatch = 0)
J <- if (any(M))
(iVales[M])[1]
else J[1]
}
V[i - 1] <- J
}
P <- rep(0, nVales - 1)
for (i in 2:nVales) {
K <- iVales[i - 1]:iVales[i]
I <- isum[K]
J <- K[I == max(I)]
if (length(J) > 1) {
M <- match(J, iPeaks, nomatch = 0)
J <- if (any(M))
(iPeaks[M])[1]
else J[1]
}
P[i - 1] <- J
}
iPeaks <- unique(sort(c(iPeaks, P)))
iVales <- unique(sort(c(iVales, V)))
nPeaks <- length(iPeaks)
nVales <- length(iVales)
if (nPeaks < nspots) {
nspots <- nPeaks
}
peakvals <- rep(0, nPeaks)
for (i in 1:nPeaks) {
m <- iPeaks[i]
J <- iVales[iVales < m]
K <- iVales[iVales > m]
lSum <- rSum <- 0
d <- 0
if (length(J)) {
lSum <- isum[J[length(J)]]
d <- d + 1
}
if (length(K)) {
rSum <- isum[K[1]]
d <- d + 1
}
peakvals[i] <- isum[m] - (lSum + rSum)/d
}
i <- 0
smax <- sum(sort(peakvals))
k <- nPeaks - nspots
if (k) {
smax <- s <- smax - sum(peakvals[-(1:nspots)])
for (j in 1:k) {
d <- (peakvals[j + nspots] - peakvals[j])
s <- s + d
if (s > smax) {
smax <- s
i <- j
}
}
}
span <- max(span, max(diff(iPeaks[i + 1:nspots])))
j <- 1
while (TRUE) {
if (iVales[j] > iPeaks[i + 1] && iVales[j] < iPeaks[i +
2])
break
j <- j + 1
}
index <- rep(0, nspots + 1)
index[2:nspots] <- iVales[j:(j + nspots - 2)]
halfspan <- floor(span/2)
index[1] <- iPeaks[i + 1] - halfspan
index[nspots + 1] <- iPeaks[nspots + i] + halfspan
if (FALSE) {
par(ask = T)
plot(1:N, isum, type = "l", xlab = "", ylab = "")
points(iPeaks, isum[iPeaks], pch = "P")
points(iVales, isum[iVales], pch = "V")
abline(v = index[1], col = "red")
abline(v = index[length(index)], col = "red")
for (i in 1:length(index)) {
abline(v = index[i], col = "red")
}
}
index
}
if (show) {
plotBlockImage(signal)
}
rowcut <- colcut <- NA
if (!is.null(rows) && rows > 0) {
if (is.null(span)) {
span <- floor(nrow(signal)/rows)
if (!(span%%2))
span <- span - 1
}
rowcut <- gridcomp(rowSums(signal), rows, if (length(span) ==
2)
span[1]
else span)
if (length(rowcut) < rows + 1) {
span <- min(diff(rowcut))
rowcut <- gridcomp(rowSums(signal), rows, span)
}
if (length(rowcut) < rows + 1)
warning("fewer peaks than spots")
if (show && (is.null(cols) || cols <= 0)) {
chan1[] <- 0
chan1[rowcut, ] <- 1
contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1,
levels = 1, drawlabels = FALSE, col = "red",
add = TRUE)
}
}
if (!is.null(cols) && cols > 0) {
if (is.null(span)) {
span <- floor(ncol(signal)/cols)
if (!(span%%2))
span <- span - 1
}
colcut <- gridcomp(colSums(signal), cols, if (length(span) ==
2)
span[2]
else span)
if (length(colcut) < cols + 1) {
span <- min(diff(colcut))
colcut <- gridcomp(colSums(signal), cols, span)
}
if (length(colcut) < cols + 1)
warning("fewer peaks than spots")
if (show && (is.null(rows) || rows <= 0)) {
chan1[] <- 0
chan1[, colcut] <- 1
contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1,
levels = 1, drawlabels = FALSE, col = "red",
add = TRUE)
}
}
if (show && !is.na(rowcut) && !is.na(colcut)) {
chan1[] <- 0
chan1[rowcut, colcut[1]:colcut[length(colcut)]] <- 1
chan1[rowcut[1]:rowcut[length(rowcut)], colcut] <- 1
contour(z = t(chan1[nrow(chan1):1, ]), nlevels = 1, levels = 1,
drawlabels = FALSE, col = "red", add = TRUE)
}
list(rowcut = rowcut, colcut = colcut)
}
<bytecode: 0x036560d8>
<environment: namespace:spotSegmentation>
--- function search by body ---
Function spotgrid in namespace spotSegmentation has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: length > 1 in coercion to logical
** running examples for arch 'x64' ... ERROR
Running examples in 'spotSegmentation-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot.spotseg
> ### Title: Microarray Spot Segmentation Plot
> ### Aliases: plot.spotseg
> ### Keywords: methods
>
> ### ** Examples
>
> data(spotSegTest)
>
> # columns of spotSegTest:
> # 1 intensities from the Cy3 (green) channel
> # 2 intensities from the Cy5 (red) channel
>
> dataTransformation <- function(x) (256*256-1-x)^2*4.71542407E-05
>
> chan1 <- matrix(dataTransformation(spotSegTest[,1]), 144, 199)
> chan2 <- matrix(dataTransformation(spotSegTest[,2]), 144, 199)
>
> hivGrid <- spotgrid( chan1, chan2, rows = 4, cols = 6, show = TRUE)
----------- FAILURE REPORT --------------
--- failure: length > 1 in coercion to logical ---
--- srcref ---
:
--- package (from environment) ---
spotSegmentation
--- call from context ---
spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)
--- call from argument ---
show && !is.na(rowcut)
--- R stacktrace ---
where 1: spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)
--- value of length: 5 type: logical ---
[1] TRUE TRUE TRUE TRUE TRUE
--- function from context ---
function (chan1, chan2, rows = NULL, cols = NULL, span = NULL,
show = FALSE)
{
signal <- chan1 + chan2
s <- min(signal)
r <- min(signal[signal > 0])
if (s <= 0) {
signal <- signal - s + 1
}
else if (r < 1) {
signal <- signal + 1
}
signal <- log(signal)
spotgridPeaks <- function(series, span = 3, seed = 0) {
if (!(span%%2))
span <- span + 1
d <- sort(diff(sort(series)))
if (!d[1]) {
d <- d[as.logical(d)][1]
set.seed(seed)
noise <- runif(length(series), min = -d/2, max = d/2)
series <- series + noise
}
zmaxcol <- apply(embed(series, span)[, span:1], 1, function(x) {
if (length(y <- seq(along = x)[x == max(x)]) == 1)
y
else 0
})
halfspan <- (span - 1)/2
c(rep(FALSE, halfspan), zmaxcol == 1 + halfspan)
}
isum <- colSums(signal)
gridcomp <- function(isum, nspots, span) {
N <- length(isum)
if (!(span%%2))
span <- span + 1
Peaks <- spotgridPeaks(isum, span)
Vales <- spotgridPeaks(-isum, span)
iPeaks <- (1:N)[Peaks]
iVales <- (1:N)[Vales]
nPeaks <- length(iPeaks)
nVales <- length(iVales)
if (FALSE) {
plot(1:N, isum, type = "l", xlab = "", ylab = "")
points(iPeaks, isum[iPeaks], pch = "P")
points(iVales, isum[iVales], pch = "V")
}
V <- rep(0, nPeaks - 1)
for (i in 2:nPeaks) {
K <- iPeaks[i - 1]:iPeaks[i]
I <- isum[K]
J <- K[I == min(I)]
if (length(J) > 1) {
M <- match(J, iVales, nomatch = 0)
J <- if (any(M))
(iVales[M])[1]
else J[1]
}
V[i - 1] <- J
}
P <- rep(0, nVales - 1)
for (i in 2:nVales) {
K <- iVales[i - 1]:iVales[i]
I <- isum[K]
J <- K[I == max(I)]
if (length(J) > 1) {
M <- match(J, iPeaks, nomatch = 0)
J <- if (any(M))
(iPeaks[M])[1]
else J[1]
}
P[i - 1] <- J
}
iPeaks <- unique(sort(c(iPeaks, P)))
iVales <- unique(sort(c(iVales, V)))
nPeaks <- length(iPeaks)
nVales <- length(iVales)
if (nPeaks < nspots) {
nspots <- nPeaks
}
peakvals <- rep(0, nPeaks)
for (i in 1:nPeaks) {
m <- iPeaks[i]
J <- iVales[iVales < m]
K <- iVales[iVales > m]
lSum <- rSum <- 0
d <- 0
if (length(J)) {
lSum <- isum[J[length(J)]]
d <- d + 1
}
if (length(K)) {
rSum <- isum[K[1]]
d <- d + 1
}
peakvals[i] <- isum[m] - (lSum + rSum)/d
}
i <- 0
smax <- sum(sort(peakvals))
k <- nPeaks - nspots
if (k) {
smax <- s <- smax - sum(peakvals[-(1:nspots)])
for (j in 1:k) {
d <- (peakvals[j + nspots] - peakvals[j])
s <- s + d
if (s > smax) {
smax <- s
i <- j
}
}
}
span <- max(span, max(diff(iPeaks[i + 1:nspots])))
j <- 1
while (TRUE) {
if (iVales[j] > iPeaks[i + 1] && iVales[j] < iPeaks[i +
2])
break
j <- j + 1
}
index <- rep(0, nspots + 1)
index[2:nspots] <- iVales[j:(j + nspots - 2)]
halfspan <- floor(span/2)
index[1] <- iPeaks[i + 1] - halfspan
index[nspots + 1] <- iPeaks[nspots + i] + halfspan
if (FALSE) {
par(ask = T)
plot(1:N, isum, type = "l", xlab = "", ylab = "")
points(iPeaks, isum[iPeaks], pch = "P")
points(iVales, isum[iVales], pch = "V")
abline(v = index[1], col = "red")
abline(v = index[length(index)], col = "red")
for (i in 1:length(index)) {
abline(v = index[i], col = "red")
}
}
index
}
if (show) {
plotBlockImage(signal)
}
rowcut <- colcut <- NA
if (!is.null(rows) && rows > 0) {
if (is.null(span)) {
span <- floor(nrow(signal)/rows)
if (!(span%%2))
span <- span - 1
}
rowcut <- gridcomp(rowSums(signal), rows, if (length(span) ==
2)
span[1]
else span)
if (length(rowcut) < rows + 1) {
span <- min(diff(rowcut))
rowcut <- gridcomp(rowSums(signal), rows, span)
}
if (length(rowcut) < rows + 1)
warning("fewer peaks than spots")
if (show && (is.null(cols) || cols <= 0)) {
chan1[] <- 0
chan1[rowcut, ] <- 1
contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1,
levels = 1, drawlabels = FALSE, col = "red",
add = TRUE)
}
}
if (!is.null(cols) && cols > 0) {
if (is.null(span)) {
span <- floor(ncol(signal)/cols)
if (!(span%%2))
span <- span - 1
}
colcut <- gridcomp(colSums(signal), cols, if (length(span) ==
2)
span[2]
else span)
if (length(colcut) < cols + 1) {
span <- min(diff(colcut))
colcut <- gridcomp(colSums(signal), cols, span)
}
if (length(colcut) < cols + 1)
warning("fewer peaks than spots")
if (show && (is.null(rows) || rows <= 0)) {
chan1[] <- 0
chan1[, colcut] <- 1
contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1,
levels = 1, drawlabels = FALSE, col = "red",
add = TRUE)
}
}
if (show && !is.na(rowcut) && !is.na(colcut)) {
chan1[] <- 0
chan1[rowcut, colcut[1]:colcut[length(colcut)]] <- 1
chan1[rowcut[1]:rowcut[length(rowcut)], colcut] <- 1
contour(z = t(chan1[nrow(chan1):1, ]), nlevels = 1, levels = 1,
drawlabels = FALSE, col = "red", add = TRUE)
}
list(rowcut = rowcut, colcut = colcut)
}
<bytecode: 0x00000000075e4688>
<environment: namespace:spotSegmentation>
--- function search by body ---
Function spotgrid in namespace spotSegmentation has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: length > 1 in coercion to logical
* checking PDF version of manual ... OK
* DONE
Status: 2 ERRORs, 1 WARNING, 3 NOTEs
See
'C:/Users/biocbuild/bbs-3.11-bioc/meat/spotSegmentation.Rcheck/00check.log'
for details.