Last updated on 2020-02-19 10:49:05 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.00 | 4.95 | 43.58 | 48.53 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.00 | 4.29 | 34.46 | 38.75 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.00 | 59.56 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.00 | 57.49 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 1.00 | 11.00 | 52.00 | 63.00 | OK | |
| r-devel-windows-ix86+x86_64-gcc8 | 1.00 | 13.00 | 53.00 | 66.00 | OK | |
| r-patched-linux-x86_64 | 1.00 | 3.57 | 38.94 | 42.51 | OK | |
| r-patched-solaris-x86 | 1.00 | 76.00 | OK | |||
| r-release-linux-x86_64 | 1.00 | 4.21 | 39.02 | 43.23 | OK | |
| r-release-windows-ix86+x86_64 | 1.00 | 10.00 | 47.00 | 57.00 | OK | |
| r-release-osx-x86_64 | 1.00 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 1.00 | 8.00 | 48.00 | 56.00 | OK | |
| r-oldrel-osx-x86_64 | 1.00 | OK |
Version: 1.00
Check: examples
Result: ERROR
Running examples in 'probsvm-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: probsvm
> ### Title: Main function that provides models for multiclass conditional
> ### probability estimation and label prediction
> ### Aliases: probsvm
>
> ### ** Examples
>
> # iris data #
>
> data(iris)
>
> iris.x=iris[c(1:20,51:70,101:120),-5]
>
> iris.y=iris[c(1:20,51:70,101:120),5]
>
> iris.test=iris[c(21:50,71:100,121:150),-5]
>
> a = probsvm(iris.x,iris.y,type="ovo",
+ Inum=10,fold=2,lambdas=2^seq(-10,10,by=3))
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
probsvm
--- call from context ---
predict_pipathresults(obj, x.test, pi = I)
--- call from argument ---
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
--- R stacktrace ---
where 1: predict_pipathresults(obj, x.test, pi = I)
where 2: prob.svm(x.train, y.train, x.test, lambda = lambda, Inum = Inum,
kernel = kernel, kparam = kparam)
where 3: loglik.svm(x.train.temp, y.train.temp, x.tune.temp, y.tune.temp,
lambda = lambda, Inum = Inum, kernel = kernel, kparam = kparam)
where 4: probsvm(iris.x, iris.y, type = "ovo", Inum = 10, fold = 2, lambdas = 2^seq(-10,
10, by = 3))
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (obj, new.x = NULL, pi = NULL)
{
obj.pi = obj$pi[1]
obj.alpha0 = obj$alpha0[1]
obj.alpha = obj$alpha[1, ]
for (ii in 2:length(obj$pi)) {
if (obj$pi[ii] != obj$pi[ii - 1]) {
obj.pi = c(obj.pi, obj$pi[ii])
obj.alpha0 = c(obj.alpha0, obj$alpha0[ii])
obj.alpha = rbind(obj.alpha, obj$alpha[ii, ])
}
}
kernel = obj$kernel
kparam = obj$kparam
if (is.null(new.x)) {
new.x = obj$x
}
if (is.null(pi)) {
pi = obj.pi
}
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
if (class(new.x) == "data.frame") {
new.x = as.matrix(new.x)
}
if (ncol(new.x) != ncol(obj$x)) {
stop("The new covariates matrix has a wrong dimension.")
}
if (!is.numeric(pi)) {
stop("The parameter pi must be numeric.")
}
if (min(pi) < 0 | max(pi) > 1) {
stop("The parameter pi must be in [0,1].")
}
K <- Kmat(new.x, obj$x, kernel, kparam)
pred.y = numeric(0)
alpha0 = numeric(0)
alpha = numeric(0)
f.hat = numeric(0)
for (i in 1:length(pi)) {
temp = pi[i]
index = which(obj.pi == temp)
if (length(index) == 1) {
temp.alpha = obj.alpha[index, ]
temp.alpha0 = obj.alpha0[index]
new.y1 = K %*% temp.alpha + temp.alpha0
}
if (length(index) == 0) {
if (temp < (obj.pi[1])) {
temp.alpha = obj.alpha[1, ]
temp.alpha0 = obj.alpha0[1]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp > (obj.pi[length(obj.pi)])) {
temp.alpha = obj.alpha[length(obj.pi), ]
temp.alpha0 = obj.alpha0[length(obj.pi)]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp < (obj.pi[length(obj.pi)]) & temp > (obj.pi[1])) {
index2 = max(which(obj.pi < temp))
temp.alpha = obj.alpha[index2, ] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha[(index2 +
1), ] - obj.alpha[index2, ])
temp.alpha0 = obj.alpha0[index2] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha0[(index2 +
1)] - obj.alpha0[index2])
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
}
f.hat = cbind(f.hat, new.y1)
new.y1 = as.numeric(new.y1 > 0) * 2 - 1
pred.y = cbind(pred.y, new.y1)
alpha = cbind(alpha, temp.alpha)
alpha0 = c(alpha0, temp.alpha0)
}
colnames(f.hat) = NULL
colnames(alpha) = NULL
colnames(pred.y) = NULL
z = list(pi = obj.pi, fitted.alpha0 = alpha0, fitted.alpha = alpha,
fitted.f = f.hat, predicted.y = pred.y)
return(z)
}
<bytecode: 0x320b9e0>
<environment: namespace:probsvm>
--- function search by body ---
Function predict_pipathresults in namespace probsvm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(new.x) != "matrix" & class(new.x) != "data.frame") { :
the condition has length > 1
Calls: probsvm -> loglik.svm -> prob.svm -> predict_pipathresults
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.00
Check: examples
Result: ERROR
Running examples in ‘probsvm-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: probsvm
> ### Title: Main function that provides models for multiclass conditional
> ### probability estimation and label prediction
> ### Aliases: probsvm
>
> ### ** Examples
>
> # iris data #
>
> data(iris)
>
> iris.x=iris[c(1:20,51:70,101:120),-5]
>
> iris.y=iris[c(1:20,51:70,101:120),5]
>
> iris.test=iris[c(21:50,71:100,121:150),-5]
>
> a = probsvm(iris.x,iris.y,type="ovo",
+ Inum=10,fold=2,lambdas=2^seq(-10,10,by=3))
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
probsvm
--- call from context ---
predict_pipathresults(obj, x.test, pi = I)
--- call from argument ---
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
--- R stacktrace ---
where 1: predict_pipathresults(obj, x.test, pi = I)
where 2: prob.svm(x.train, y.train, x.test, lambda = lambda, Inum = Inum,
kernel = kernel, kparam = kparam)
where 3: loglik.svm(x.train.temp, y.train.temp, x.tune.temp, y.tune.temp,
lambda = lambda, Inum = Inum, kernel = kernel, kparam = kparam)
where 4: probsvm(iris.x, iris.y, type = "ovo", Inum = 10, fold = 2, lambdas = 2^seq(-10,
10, by = 3))
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (obj, new.x = NULL, pi = NULL)
{
obj.pi = obj$pi[1]
obj.alpha0 = obj$alpha0[1]
obj.alpha = obj$alpha[1, ]
for (ii in 2:length(obj$pi)) {
if (obj$pi[ii] != obj$pi[ii - 1]) {
obj.pi = c(obj.pi, obj$pi[ii])
obj.alpha0 = c(obj.alpha0, obj$alpha0[ii])
obj.alpha = rbind(obj.alpha, obj$alpha[ii, ])
}
}
kernel = obj$kernel
kparam = obj$kparam
if (is.null(new.x)) {
new.x = obj$x
}
if (is.null(pi)) {
pi = obj.pi
}
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
if (class(new.x) == "data.frame") {
new.x = as.matrix(new.x)
}
if (ncol(new.x) != ncol(obj$x)) {
stop("The new covariates matrix has a wrong dimension.")
}
if (!is.numeric(pi)) {
stop("The parameter pi must be numeric.")
}
if (min(pi) < 0 | max(pi) > 1) {
stop("The parameter pi must be in [0,1].")
}
K <- Kmat(new.x, obj$x, kernel, kparam)
pred.y = numeric(0)
alpha0 = numeric(0)
alpha = numeric(0)
f.hat = numeric(0)
for (i in 1:length(pi)) {
temp = pi[i]
index = which(obj.pi == temp)
if (length(index) == 1) {
temp.alpha = obj.alpha[index, ]
temp.alpha0 = obj.alpha0[index]
new.y1 = K %*% temp.alpha + temp.alpha0
}
if (length(index) == 0) {
if (temp < (obj.pi[1])) {
temp.alpha = obj.alpha[1, ]
temp.alpha0 = obj.alpha0[1]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp > (obj.pi[length(obj.pi)])) {
temp.alpha = obj.alpha[length(obj.pi), ]
temp.alpha0 = obj.alpha0[length(obj.pi)]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp < (obj.pi[length(obj.pi)]) & temp > (obj.pi[1])) {
index2 = max(which(obj.pi < temp))
temp.alpha = obj.alpha[index2, ] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha[(index2 +
1), ] - obj.alpha[index2, ])
temp.alpha0 = obj.alpha0[index2] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha0[(index2 +
1)] - obj.alpha0[index2])
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
}
f.hat = cbind(f.hat, new.y1)
new.y1 = as.numeric(new.y1 > 0) * 2 - 1
pred.y = cbind(pred.y, new.y1)
alpha = cbind(alpha, temp.alpha)
alpha0 = c(alpha0, temp.alpha0)
}
colnames(f.hat) = NULL
colnames(alpha) = NULL
colnames(pred.y) = NULL
z = list(pi = obj.pi, fitted.alpha0 = alpha0, fitted.alpha = alpha,
fitted.f = f.hat, predicted.y = pred.y)
return(z)
}
<bytecode: 0x55592fb211a0>
<environment: namespace:probsvm>
--- function search by body ---
Function predict_pipathresults in namespace probsvm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(new.x) != "matrix" & class(new.x) != "data.frame") { :
the condition has length > 1
Calls: probsvm -> loglik.svm -> prob.svm -> predict_pipathresults
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.00
Check: examples
Result: ERROR
Running examples in ‘probsvm-Ex.R’ failed
The error most likely occurred in:
> ### Name: probsvm
> ### Title: Main function that provides models for multiclass conditional
> ### probability estimation and label prediction
> ### Aliases: probsvm
>
> ### ** Examples
>
> # iris data #
>
> data(iris)
>
> iris.x=iris[c(1:20,51:70,101:120),-5]
>
> iris.y=iris[c(1:20,51:70,101:120),5]
>
> iris.test=iris[c(21:50,71:100,121:150),-5]
>
> a = probsvm(iris.x,iris.y,type="ovo",
+ Inum=10,fold=2,lambdas=2^seq(-10,10,by=3))
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
probsvm
--- call from context ---
predict_pipathresults(obj, x.test, pi = I)
--- call from argument ---
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
--- R stacktrace ---
where 1: predict_pipathresults(obj, x.test, pi = I)
where 2: prob.svm(x.train, y.train, x.test, lambda = lambda, Inum = Inum,
kernel = kernel, kparam = kparam)
where 3: loglik.svm(x.train.temp, y.train.temp, x.tune.temp, y.tune.temp,
lambda = lambda, Inum = Inum, kernel = kernel, kparam = kparam)
where 4: probsvm(iris.x, iris.y, type = "ovo", Inum = 10, fold = 2, lambdas = 2^seq(-10,
10, by = 3))
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (obj, new.x = NULL, pi = NULL)
{
obj.pi = obj$pi[1]
obj.alpha0 = obj$alpha0[1]
obj.alpha = obj$alpha[1, ]
for (ii in 2:length(obj$pi)) {
if (obj$pi[ii] != obj$pi[ii - 1]) {
obj.pi = c(obj.pi, obj$pi[ii])
obj.alpha0 = c(obj.alpha0, obj$alpha0[ii])
obj.alpha = rbind(obj.alpha, obj$alpha[ii, ])
}
}
kernel = obj$kernel
kparam = obj$kparam
if (is.null(new.x)) {
new.x = obj$x
}
if (is.null(pi)) {
pi = obj.pi
}
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
if (class(new.x) == "data.frame") {
new.x = as.matrix(new.x)
}
if (ncol(new.x) != ncol(obj$x)) {
stop("The new covariates matrix has a wrong dimension.")
}
if (!is.numeric(pi)) {
stop("The parameter pi must be numeric.")
}
if (min(pi) < 0 | max(pi) > 1) {
stop("The parameter pi must be in [0,1].")
}
K <- Kmat(new.x, obj$x, kernel, kparam)
pred.y = numeric(0)
alpha0 = numeric(0)
alpha = numeric(0)
f.hat = numeric(0)
for (i in 1:length(pi)) {
temp = pi[i]
index = which(obj.pi == temp)
if (length(index) == 1) {
temp.alpha = obj.alpha[index, ]
temp.alpha0 = obj.alpha0[index]
new.y1 = K %*% temp.alpha + temp.alpha0
}
if (length(index) == 0) {
if (temp < (obj.pi[1])) {
temp.alpha = obj.alpha[1, ]
temp.alpha0 = obj.alpha0[1]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp > (obj.pi[length(obj.pi)])) {
temp.alpha = obj.alpha[length(obj.pi), ]
temp.alpha0 = obj.alpha0[length(obj.pi)]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp < (obj.pi[length(obj.pi)]) & temp > (obj.pi[1])) {
index2 = max(which(obj.pi < temp))
temp.alpha = obj.alpha[index2, ] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha[(index2 +
1), ] - obj.alpha[index2, ])
temp.alpha0 = obj.alpha0[index2] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha0[(index2 +
1)] - obj.alpha0[index2])
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
}
f.hat = cbind(f.hat, new.y1)
new.y1 = as.numeric(new.y1 > 0) * 2 - 1
pred.y = cbind(pred.y, new.y1)
alpha = cbind(alpha, temp.alpha)
alpha0 = c(alpha0, temp.alpha0)
}
colnames(f.hat) = NULL
colnames(alpha) = NULL
colnames(pred.y) = NULL
z = list(pi = obj.pi, fitted.alpha0 = alpha0, fitted.alpha = alpha,
fitted.f = f.hat, predicted.y = pred.y)
return(z)
}
<bytecode: 0x42f46d0>
<environment: namespace:probsvm>
--- function search by body ---
Function predict_pipathresults in namespace probsvm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(new.x) != "matrix" & class(new.x) != "data.frame") { :
the condition has length > 1
Calls: probsvm -> loglik.svm -> prob.svm -> predict_pipathresults
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.00
Check: examples
Result: ERROR
Running examples in ‘probsvm-Ex.R’ failed
The error most likely occurred in:
> ### Name: probsvm
> ### Title: Main function that provides models for multiclass conditional
> ### probability estimation and label prediction
> ### Aliases: probsvm
>
> ### ** Examples
>
> # iris data #
>
> data(iris)
>
> iris.x=iris[c(1:20,51:70,101:120),-5]
>
> iris.y=iris[c(1:20,51:70,101:120),5]
>
> iris.test=iris[c(21:50,71:100,121:150),-5]
>
> a = probsvm(iris.x,iris.y,type="ovo",
+ Inum=10,fold=2,lambdas=2^seq(-10,10,by=3))
----------- FAILURE REPORT --------------
--- failure: the condition has length > 1 ---
--- srcref ---
:
--- package (from environment) ---
probsvm
--- call from context ---
predict_pipathresults(obj, x.test, pi = I)
--- call from argument ---
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
--- R stacktrace ---
where 1: predict_pipathresults(obj, x.test, pi = I)
where 2: prob.svm(x.train, y.train, x.test, lambda = lambda, Inum = Inum,
kernel = kernel, kparam = kparam)
where 3: loglik.svm(x.train.temp, y.train.temp, x.tune.temp, y.tune.temp,
lambda = lambda, Inum = Inum, kernel = kernel, kparam = kparam)
where 4: probsvm(iris.x, iris.y, type = "ovo", Inum = 10, fold = 2, lambdas = 2^seq(-10,
10, by = 3))
--- value of length: 2 type: logical ---
[1] FALSE TRUE
--- function from context ---
function (obj, new.x = NULL, pi = NULL)
{
obj.pi = obj$pi[1]
obj.alpha0 = obj$alpha0[1]
obj.alpha = obj$alpha[1, ]
for (ii in 2:length(obj$pi)) {
if (obj$pi[ii] != obj$pi[ii - 1]) {
obj.pi = c(obj.pi, obj$pi[ii])
obj.alpha0 = c(obj.alpha0, obj$alpha0[ii])
obj.alpha = rbind(obj.alpha, obj$alpha[ii, ])
}
}
kernel = obj$kernel
kparam = obj$kparam
if (is.null(new.x)) {
new.x = obj$x
}
if (is.null(pi)) {
pi = obj.pi
}
if (class(new.x) != "matrix" & class(new.x) != "data.frame") {
stop("The new covariates must be either a matrix or a data.frame.")
}
if (class(new.x) == "data.frame") {
new.x = as.matrix(new.x)
}
if (ncol(new.x) != ncol(obj$x)) {
stop("The new covariates matrix has a wrong dimension.")
}
if (!is.numeric(pi)) {
stop("The parameter pi must be numeric.")
}
if (min(pi) < 0 | max(pi) > 1) {
stop("The parameter pi must be in [0,1].")
}
K <- Kmat(new.x, obj$x, kernel, kparam)
pred.y = numeric(0)
alpha0 = numeric(0)
alpha = numeric(0)
f.hat = numeric(0)
for (i in 1:length(pi)) {
temp = pi[i]
index = which(obj.pi == temp)
if (length(index) == 1) {
temp.alpha = obj.alpha[index, ]
temp.alpha0 = obj.alpha0[index]
new.y1 = K %*% temp.alpha + temp.alpha0
}
if (length(index) == 0) {
if (temp < (obj.pi[1])) {
temp.alpha = obj.alpha[1, ]
temp.alpha0 = obj.alpha0[1]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp > (obj.pi[length(obj.pi)])) {
temp.alpha = obj.alpha[length(obj.pi), ]
temp.alpha0 = obj.alpha0[length(obj.pi)]
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
if (temp < (obj.pi[length(obj.pi)]) & temp > (obj.pi[1])) {
index2 = max(which(obj.pi < temp))
temp.alpha = obj.alpha[index2, ] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha[(index2 +
1), ] - obj.alpha[index2, ])
temp.alpha0 = obj.alpha0[index2] + (temp - obj.pi[index2])/(obj.pi[(index2 +
1)] - obj.pi[index2]) * (obj.alpha0[(index2 +
1)] - obj.alpha0[index2])
new.y1 = K %*% (temp.alpha * obj$y) + temp.alpha0
}
}
f.hat = cbind(f.hat, new.y1)
new.y1 = as.numeric(new.y1 > 0) * 2 - 1
pred.y = cbind(pred.y, new.y1)
alpha = cbind(alpha, temp.alpha)
alpha0 = c(alpha0, temp.alpha0)
}
colnames(f.hat) = NULL
colnames(alpha) = NULL
colnames(pred.y) = NULL
z = list(pi = obj.pi, fitted.alpha0 = alpha0, fitted.alpha = alpha,
fitted.f = f.hat, predicted.y = pred.y)
return(z)
}
<bytecode: 0x30fd1a8>
<environment: namespace:probsvm>
--- function search by body ---
Function predict_pipathresults in namespace probsvm has this body.
----------- END OF FAILURE REPORT --------------
Error in if (class(new.x) != "matrix" & class(new.x) != "data.frame") { :
the condition has length > 1
Calls: probsvm -> loglik.svm -> prob.svm -> predict_pipathresults
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc