Last updated on 2020-02-19 10:49:04 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2-2 | 13.06 | 110.81 | 123.87 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.2-2 | 10.25 | 82.92 | 93.17 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.2-2 | 147.19 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.2-2 | 142.42 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 1.2-2 | 26.00 | 124.00 | 150.00 | NOTE | |
| r-devel-windows-ix86+x86_64-gcc8 | 1.2-2 | 53.00 | 176.00 | 229.00 | NOTE | |
| r-patched-linux-x86_64 | 1.2-2 | 10.82 | 97.61 | 108.43 | NOTE | |
| r-patched-solaris-x86 | 1.2-2 | 224.10 | NOTE | |||
| r-release-linux-x86_64 | 1.2-2 | 11.23 | 98.32 | 109.55 | NOTE | |
| r-release-windows-ix86+x86_64 | 1.2-2 | 29.00 | 115.00 | 144.00 | NOTE | |
| r-release-osx-x86_64 | 1.2-2 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 1.2-2 | 16.00 | 108.00 | 124.00 | NOTE | |
| r-oldrel-osx-x86_64 | 1.2-2 | NOTE |
Version: 1.2-2
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: 'GenABEL'
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64-gcc8, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-osx-x86_64
Version: 1.2-2
Check: R code for possible problems
Result: NOTE
ExampleModels: no visible global function definition for 'data'
ExampleModels: no visible binding for global variable 'ExampleData'
ORmultivariate: no visible global function definition for 'write.table'
ORmultivariate: no visible global function definition for 'predict'
ORunivariate: no visible global function definition for 'write.table'
fitLogRegModel: no visible global function definition for 'as.formula'
fitLogRegModel: no visible global function definition for 'glm'
fitLogRegModel: no visible global function definition for 'binomial'
plotCalibration: no visible global function definition for 'pchisq'
plotCalibration: no visible global function definition for 'lines'
plotCalibration: no visible global function definition for 'savePlot'
plotCalibration: no visible global function definition for 'dev.cur'
plotCalibration: no visible global function definition for
'write.table'
plotDiscriminationBox: no visible global function definition for
'boxplot'
plotDiscriminationBox: no visible global function definition for
'savePlot'
plotDiscriminationBox: no visible global function definition for
'dev.cur'
plotPredictivenessCurve: no visible binding for global variable 'lines'
plotPredictivenessCurve: no visible global function definition for
'mtext'
plotPredictivenessCurve: no visible global function definition for
'legend'
plotPredictivenessCurve: no visible global function definition for
'savePlot'
plotPredictivenessCurve: no visible global function definition for
'dev.cur'
plotPriorPosteriorRisk: no visible global function definition for
'abline'
plotPriorPosteriorRisk: no visible global function definition for
'savePlot'
plotPriorPosteriorRisk: no visible global function definition for
'dev.cur'
plotPriorPosteriorRisk: no visible global function definition for 'par'
plotPriorPosteriorRisk: no visible global function definition for
'title'
plotPriorPosteriorRisk: no visible global function definition for
'write.table'
plotROC: no visible global function definition for 'lines'
plotROC: no visible global function definition for 'legend'
plotROC: no visible global function definition for 'savePlot'
plotROC: no visible global function definition for 'dev.cur'
plotRiskDistribution: no visible global function definition for
'barplot'
plotRiskDistribution: no visible global function definition for
'savePlot'
plotRiskDistribution: no visible global function definition for
'dev.cur'
plotRiskscorePredrisk: no visible global function definition for 'lm'
plotRiskscorePredrisk: no visible global function definition for
'abline'
plotRiskscorePredrisk: no visible global function definition for
'savePlot'
plotRiskscorePredrisk: no visible global function definition for
'dev.cur'
plotRiskscorePredrisk: no visible global function definition for
'write.table'
predRisk: no visible global function definition for 'predict'
predRisk: no visible global function definition for 'write.table'
reclassification: no visible global function definition for 'pnorm'
simulatedDataset : func.data: no visible global function definition for
'runif'
simulatedDataset: no visible global function definition for
'write.table'
Undefined global functions or variables:
ExampleData abline as.formula barplot binomial boxplot data dev.cur
glm legend lines lm mtext par pchisq pnorm predict runif savePlot
title write.table
Consider adding
importFrom("grDevices", "dev.cur", "savePlot")
importFrom("graphics", "abline", "barplot", "boxplot", "legend",
"lines", "mtext", "par", "title")
importFrom("stats", "as.formula", "binomial", "glm", "lm", "pchisq",
"pnorm", "predict", "runif")
importFrom("utils", "data", "write.table")
to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-ix86+x86_64-gcc8, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64
Version: 1.2-2
Check: examples
Result: ERROR
Running examples in 'PredictABEL-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plotPredictivenessCurve
> ### Title: Function for predictiveness curve.
> ### Aliases: plotPredictivenessCurve
> ### Keywords: hplot
>
> ### ** Examples
> # specify dataset with outcome and predictor variables
> data(ExampleData)
>
> # fit logistic regression models
> # all steps needed to construct a logistic regression model are written in a function
> # called 'ExampleModels', which is described on page 4-5
> riskmodel1 <- ExampleModels()$riskModel1
> riskmodel2 <- ExampleModels()$riskModel2
>
> # obtain predicted risks
> predRisk1 <- predRisk(riskmodel1)
> predRisk2 <- predRisk(riskmodel2)
>
> # specify range of y-axis
> rangeyaxis <- c(0,1)
> # specify labels of the predictiveness curves
> labels <- c("without genetic factors", "with genetic factors")
>
> # produce predictiveness curves
> plotPredictivenessCurve(predrisk=cbind(predRisk1,predRisk2),
+ rangeyaxis=rangeyaxis, labels=labels)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
PredictABEL
--- call from context ---
plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- call from argument ---
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
--- R stacktrace ---
where 1: plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (predrisk, rangeyaxis, labels, plottitle, xlabel, ylabel,
fileplot, plottype)
{
if (missing(plottitle)) {
plottitle <- "Predictiveness curve"
}
if (missing(xlabel)) {
xlabel <- "Cumulative percentage"
}
if (missing(ylabel)) {
ylabel <- "Predicted risks"
}
if (missing(rangeyaxis)) {
rangeyaxis <- c(0, 1)
}
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
a <- c(1:dim(predrisk)[2])
for (i in 1:dim(predrisk)[2]) {
x <- predrisk[, i]
if (i == 1) {
xlim = c(0, 1)
ylim = c(0, 1)
xlab = xlabel
ylab = ylabel
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
resetGraph()
evalCall(plot, argu = list(x = y[z], y = x[z], type = "n",
xlab = "", ylab = "", las = 1, mgp = c(0, 0.6,
0), cex.axis = 1.1, col = 16 + i, lty = i,
ylim = rangeyaxis, main = plottitle), checkdef = TRUE,
checkpar = TRUE)
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2), checkdef = TRUE, checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
else {
xlim = c(0, 1)
ylim = c(0, 1)
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2, add = TRUE), checkdef = TRUE,
checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
}
if (!missing(labels)) {
legend("bottomright", legend = labels, col = c(17:(16 +
dim(predrisk)[2])), lty = c(1:(dim(predrisk)[2])),
lwd = 2, cex = 1)
}
if (missing(plottype)) {
plottype <- "jpg"
}
if (!missing(fileplot))
savePlot(filename = fileplot, type = plottype, device = dev.cur())
}
<bytecode: 0xcc4ac10>
<environment: namespace:PredictABEL>
--- function search by body ---
Function plotPredictivenessCurve in namespace PredictABEL has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(predrisk) == "numeric") { :
the condition has length > 1
Calls: plotPredictivenessCurve
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.2-2
Check: for non-standard things in the check directory
Result: NOTE
Found the following files/directories:
'AlleleOR.txt' 'GenoOR.txt' 'multiOR.txt'
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.2-2
Check: examples
Result: ERROR
Running examples in ‘PredictABEL-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plotPredictivenessCurve
> ### Title: Function for predictiveness curve.
> ### Aliases: plotPredictivenessCurve
> ### Keywords: hplot
>
> ### ** Examples
> # specify dataset with outcome and predictor variables
> data(ExampleData)
>
> # fit logistic regression models
> # all steps needed to construct a logistic regression model are written in a function
> # called 'ExampleModels', which is described on page 4-5
> riskmodel1 <- ExampleModels()$riskModel1
> riskmodel2 <- ExampleModels()$riskModel2
>
> # obtain predicted risks
> predRisk1 <- predRisk(riskmodel1)
> predRisk2 <- predRisk(riskmodel2)
>
> # specify range of y-axis
> rangeyaxis <- c(0,1)
> # specify labels of the predictiveness curves
> labels <- c("without genetic factors", "with genetic factors")
>
> # produce predictiveness curves
> plotPredictivenessCurve(predrisk=cbind(predRisk1,predRisk2),
+ rangeyaxis=rangeyaxis, labels=labels)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
PredictABEL
--- call from context ---
plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- call from argument ---
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
--- R stacktrace ---
where 1: plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (predrisk, rangeyaxis, labels, plottitle, xlabel, ylabel,
fileplot, plottype)
{
if (missing(plottitle)) {
plottitle <- "Predictiveness curve"
}
if (missing(xlabel)) {
xlabel <- "Cumulative percentage"
}
if (missing(ylabel)) {
ylabel <- "Predicted risks"
}
if (missing(rangeyaxis)) {
rangeyaxis <- c(0, 1)
}
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
a <- c(1:dim(predrisk)[2])
for (i in 1:dim(predrisk)[2]) {
x <- predrisk[, i]
if (i == 1) {
xlim = c(0, 1)
ylim = c(0, 1)
xlab = xlabel
ylab = ylabel
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
resetGraph()
evalCall(plot, argu = list(x = y[z], y = x[z], type = "n",
xlab = "", ylab = "", las = 1, mgp = c(0, 0.6,
0), cex.axis = 1.1, col = 16 + i, lty = i,
ylim = rangeyaxis, main = plottitle), checkdef = TRUE,
checkpar = TRUE)
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2), checkdef = TRUE, checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
else {
xlim = c(0, 1)
ylim = c(0, 1)
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2, add = TRUE), checkdef = TRUE,
checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
}
if (!missing(labels)) {
legend("bottomright", legend = labels, col = c(17:(16 +
dim(predrisk)[2])), lty = c(1:(dim(predrisk)[2])),
lwd = 2, cex = 1)
}
if (missing(plottype)) {
plottype <- "jpg"
}
if (!missing(fileplot))
savePlot(filename = fileplot, type = plottype, device = dev.cur())
}
<bytecode: 0x558f25a60518>
<environment: namespace:PredictABEL>
--- function search by body ---
Function plotPredictivenessCurve in namespace PredictABEL has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(predrisk) == "numeric") { :
the condition has length > 1
Calls: plotPredictivenessCurve
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.2-2
Check: examples
Result: ERROR
Running examples in ‘PredictABEL-Ex.R’ failed
The error most likely occurred in:
> ### Name: plotPredictivenessCurve
> ### Title: Function for predictiveness curve.
> ### Aliases: plotPredictivenessCurve
> ### Keywords: hplot
>
> ### ** Examples
> # specify dataset with outcome and predictor variables
> data(ExampleData)
>
> # fit logistic regression models
> # all steps needed to construct a logistic regression model are written in a function
> # called 'ExampleModels', which is described on page 4-5
> riskmodel1 <- ExampleModels()$riskModel1
> riskmodel2 <- ExampleModels()$riskModel2
>
> # obtain predicted risks
> predRisk1 <- predRisk(riskmodel1)
> predRisk2 <- predRisk(riskmodel2)
>
> # specify range of y-axis
> rangeyaxis <- c(0,1)
> # specify labels of the predictiveness curves
> labels <- c("without genetic factors", "with genetic factors")
>
> # produce predictiveness curves
> plotPredictivenessCurve(predrisk=cbind(predRisk1,predRisk2),
+ rangeyaxis=rangeyaxis, labels=labels)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
PredictABEL
--- call from context ---
plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- call from argument ---
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
--- R stacktrace ---
where 1: plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (predrisk, rangeyaxis, labels, plottitle, xlabel, ylabel,
fileplot, plottype)
{
if (missing(plottitle)) {
plottitle <- "Predictiveness curve"
}
if (missing(xlabel)) {
xlabel <- "Cumulative percentage"
}
if (missing(ylabel)) {
ylabel <- "Predicted risks"
}
if (missing(rangeyaxis)) {
rangeyaxis <- c(0, 1)
}
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
a <- c(1:dim(predrisk)[2])
for (i in 1:dim(predrisk)[2]) {
x <- predrisk[, i]
if (i == 1) {
xlim = c(0, 1)
ylim = c(0, 1)
xlab = xlabel
ylab = ylabel
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
resetGraph()
evalCall(plot, argu = list(x = y[z], y = x[z], type = "n",
xlab = "", ylab = "", las = 1, mgp = c(0, 0.6,
0), cex.axis = 1.1, col = 16 + i, lty = i,
ylim = rangeyaxis, main = plottitle), checkdef = TRUE,
checkpar = TRUE)
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2), checkdef = TRUE, checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
else {
xlim = c(0, 1)
ylim = c(0, 1)
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2, add = TRUE), checkdef = TRUE,
checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
}
if (!missing(labels)) {
legend("bottomright", legend = labels, col = c(17:(16 +
dim(predrisk)[2])), lty = c(1:(dim(predrisk)[2])),
lwd = 2, cex = 1)
}
if (missing(plottype)) {
plottype <- "jpg"
}
if (!missing(fileplot))
savePlot(filename = fileplot, type = plottype, device = dev.cur())
}
<bytecode: 0xc28a710>
<environment: namespace:PredictABEL>
--- function search by body ---
Function plotPredictivenessCurve in namespace PredictABEL has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(predrisk) == "numeric") { :
the condition has length > 1
Calls: plotPredictivenessCurve
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.2-2
Check: examples
Result: ERROR
Running examples in ‘PredictABEL-Ex.R’ failed
The error most likely occurred in:
> ### Name: plotPredictivenessCurve
> ### Title: Function for predictiveness curve.
> ### Aliases: plotPredictivenessCurve
> ### Keywords: hplot
>
> ### ** Examples
> # specify dataset with outcome and predictor variables
> data(ExampleData)
>
> # fit logistic regression models
> # all steps needed to construct a logistic regression model are written in a function
> # called 'ExampleModels', which is described on page 4-5
> riskmodel1 <- ExampleModels()$riskModel1
> riskmodel2 <- ExampleModels()$riskModel2
>
> # obtain predicted risks
> predRisk1 <- predRisk(riskmodel1)
> predRisk2 <- predRisk(riskmodel2)
>
> # specify range of y-axis
> rangeyaxis <- c(0,1)
> # specify labels of the predictiveness curves
> labels <- c("without genetic factors", "with genetic factors")
>
> # produce predictiveness curves
> plotPredictivenessCurve(predrisk=cbind(predRisk1,predRisk2),
+ rangeyaxis=rangeyaxis, labels=labels)
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
PredictABEL
--- call from context ---
plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- call from argument ---
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
--- R stacktrace ---
where 1: plotPredictivenessCurve(predrisk = cbind(predRisk1, predRisk2),
rangeyaxis = rangeyaxis, labels = labels)
--- value of length: 2 type: logical ---
[1] FALSE FALSE
--- function from context ---
function (predrisk, rangeyaxis, labels, plottitle, xlabel, ylabel,
fileplot, plottype)
{
if (missing(plottitle)) {
plottitle <- "Predictiveness curve"
}
if (missing(xlabel)) {
xlabel <- "Cumulative percentage"
}
if (missing(ylabel)) {
ylabel <- "Predicted risks"
}
if (missing(rangeyaxis)) {
rangeyaxis <- c(0, 1)
}
if (class(predrisk) == "numeric") {
predrisk <- cbind(predrisk)
}
a <- c(1:dim(predrisk)[2])
for (i in 1:dim(predrisk)[2]) {
x <- predrisk[, i]
if (i == 1) {
xlim = c(0, 1)
ylim = c(0, 1)
xlab = xlabel
ylab = ylabel
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
resetGraph()
evalCall(plot, argu = list(x = y[z], y = x[z], type = "n",
xlab = "", ylab = "", las = 1, mgp = c(0, 0.6,
0), cex.axis = 1.1, col = 16 + i, lty = i,
ylim = rangeyaxis, main = plottitle), checkdef = TRUE,
checkpar = TRUE)
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2), checkdef = TRUE, checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
else {
xlim = c(0, 1)
ylim = c(0, 1)
x <- sort(x)
n <- length(x)
y <- (1:n)/n
z <- y >= ylim[1] & y <= ylim[2]
evalCall(lines, argu = list(x = y[z], y = x[z], col = 16 +
i, lty = i, lwd = 2, add = TRUE), checkdef = TRUE,
checkpar = TRUE)
mtext(xlab, side = 1, line = 2.75, cex = 1.2)
mtext(ylab, side = 2, line = 2.5, cex = 1.2)
invisible(data.frame(x = x, y = y))
}
}
if (!missing(labels)) {
legend("bottomright", legend = labels, col = c(17:(16 +
dim(predrisk)[2])), lty = c(1:(dim(predrisk)[2])),
lwd = 2, cex = 1)
}
if (missing(plottype)) {
plottype <- "jpg"
}
if (!missing(fileplot))
savePlot(filename = fileplot, type = plottype, device = dev.cur())
}
<bytecode: 0xdc4fc20>
<environment: namespace:PredictABEL>
--- function search by body ---
Function plotPredictivenessCurve in namespace PredictABEL has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(predrisk) == "numeric") { :
the condition has length > 1
Calls: plotPredictivenessCurve
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 1.2-2
Check: R code for possible problems
Result: NOTE
ExampleModels: no visible global function definition for ‘data’
ExampleModels: no visible binding for global variable ‘ExampleData’
ORmultivariate: no visible global function definition for ‘write.table’
ORmultivariate: no visible global function definition for ‘predict’
ORunivariate: no visible global function definition for ‘write.table’
fitLogRegModel: no visible global function definition for ‘as.formula’
fitLogRegModel: no visible global function definition for ‘glm’
fitLogRegModel: no visible global function definition for ‘binomial’
plotCalibration: no visible global function definition for ‘pchisq’
plotCalibration: no visible global function definition for ‘lines’
plotCalibration: no visible global function definition for ‘dev.cur’
plotCalibration: no visible global function definition for
‘write.table’
plotDiscriminationBox: no visible global function definition for
‘boxplot’
plotDiscriminationBox: no visible global function definition for
‘dev.cur’
plotPredictivenessCurve: no visible binding for global variable ‘lines’
plotPredictivenessCurve: no visible global function definition for
‘mtext’
plotPredictivenessCurve: no visible global function definition for
‘legend’
plotPredictivenessCurve: no visible global function definition for
‘dev.cur’
plotPriorPosteriorRisk: no visible global function definition for
‘abline’
plotPriorPosteriorRisk: no visible global function definition for
‘dev.cur’
plotPriorPosteriorRisk: no visible global function definition for ‘par’
plotPriorPosteriorRisk: no visible global function definition for
‘title’
plotPriorPosteriorRisk: no visible global function definition for
‘write.table’
plotROC: no visible global function definition for ‘lines’
plotROC: no visible global function definition for ‘legend’
plotROC: no visible global function definition for ‘dev.cur’
plotRiskDistribution: no visible global function definition for
‘barplot’
plotRiskDistribution: no visible global function definition for
‘dev.cur’
plotRiskscorePredrisk: no visible global function definition for ‘lm’
plotRiskscorePredrisk: no visible global function definition for
‘abline’
plotRiskscorePredrisk: no visible global function definition for
‘dev.cur’
plotRiskscorePredrisk: no visible global function definition for
‘write.table’
predRisk: no visible global function definition for ‘predict’
predRisk: no visible global function definition for ‘write.table’
reclassification: no visible global function definition for ‘pnorm’
simulatedDataset : func.data: no visible global function definition for
‘runif’
simulatedDataset: no visible global function definition for
‘write.table’
Undefined global functions or variables:
ExampleData abline as.formula barplot binomial boxplot data dev.cur
glm legend lines lm mtext par pchisq pnorm predict runif title
write.table
Consider adding
importFrom("grDevices", "dev.cur")
importFrom("graphics", "abline", "barplot", "boxplot", "legend",
"lines", "mtext", "par", "title")
importFrom("stats", "as.formula", "binomial", "glm", "lm", "pchisq",
"pnorm", "predict", "runif")
importFrom("utils", "data", "write.table")
to your NAMESPACE file.
Flavor: r-oldrel-osx-x86_64