Last updated on 2022-04-27 12:55:11 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2 | 3.10 | 50.88 | 53.98 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.2 | 2.39 | 37.62 | 40.01 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.2 | 58.09 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.2 | 56.40 | ERROR | |||
| r-devel-windows-x86_64 | 1.2 | 33.00 | 65.00 | 98.00 | NOTE | |
| r-patched-linux-x86_64 | 1.2 | 2.90 | 48.45 | 51.35 | OK | |
| r-release-linux-x86_64 | 1.2 | 2.57 | 48.49 | 51.06 | OK | |
| r-release-macos-arm64 | 1.2 | 21.00 | OK | |||
| r-release-macos-x86_64 | 1.2 | 34.00 | OK | |||
| r-release-windows-x86_64 | 1.2 | 30.00 | 67.00 | 97.00 | OK | |
| r-oldrel-macos-arm64 | 1.2 | 23.00 | OK | |||
| r-oldrel-macos-x86_64 | 1.2 | 32.00 | OK | |||
| r-oldrel-windows-ix86+x86_64 | 1.2 | 7.00 | 58.00 | 65.00 | OK |
Version: 1.2
Check: Rd files
Result: NOTE
checkRd: (7) activate.Rd:25-28: \item in \arguments must have non-empty label
checkRd: (7) activate.Rd:29-32: \item in \arguments must have non-empty label
checkRd: (7) nnetPredInt.Rd:51-53: \item in \arguments must have non-empty label
checkRd: (7) nnetPredInt.Rd:54-56: \item in \arguments must have non-empty label
checkRd: (7) nnetPredInt.Rd:57-59: \item in \arguments must have non-empty label
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-x86_64
Version: 1.2
Check: examples
Result: ERROR
Running examples in ‘nnetpredint-Ex.R’ failed
The error most likely occurred in:
> ### Name: nnetPredInt
> ### Title: Prediction Intervals of Neural Networks
> ### Aliases: nnetPredInt nnetPredInt.default nnetPredInt.nnet
> ### nnetPredInt.nn nnetPredInt.rsnns
>
> ### ** Examples
>
> # Example 1: Using the nn object trained by neuralnet package
> set.seed(500)
> library(MASS)
> data <- Boston
> maxs <- apply(data, 2, max)
> mins <- apply(data, 2, min)
> scaled <- as.data.frame(scale(data, center = mins, scale = maxs - mins)) # normalization
> index <- sample(1:nrow(data),round(0.75*nrow(data)))
> train_ <- scaled[index,]
> test_ <- scaled[-index,]
>
> library(neuralnet) # Training
> n <- names(train_)
> f <- as.formula(paste("medv ~", paste(n[!n %in% "medv"], collapse = " + ")))
> nn <- neuralnet(f,data = train_,hidden = c(5,3),linear.output = FALSE)
> plot(nn)
>
> library(nnetpredint) # Getting prediction confidence interval
> x <- train_[,-14]
> y <- train_[,14]
> newData <- test_[,-14]
>
> # S3 generic method: Object of nn
> yPredInt <- nnetPredInt(nn, x, y, newData)
Error in (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% :
'length = 2' in coercion to 'logical(1)'
Calls: nnetPredInt -> nnetPredInt.nn -> getPredInt -> checkInput
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Version: 1.2
Check: tests
Result: ERROR
Running ‘test1.R’
Running ‘test2.R’
Running ‘test3.R’
Running the tests in ‘tests/test1.R’ failed.
Complete output:
> # Example 1: Using the nn object trained by neuralnet package
> set.seed(500)
> library(MASS)
> data <- Boston
> maxs <- apply(data, 2, max)
> mins <- apply(data, 2, min)
> scaled <- as.data.frame(scale(data, center = mins, scale = maxs - mins)) # normalization
> index <- sample(1:nrow(data),round(0.75*nrow(data)))
> train_ <- scaled[index,]
> test_ <- scaled[-index,]
>
> library(neuralnet) # Training
> n <- names(train_)
> f <- as.formula(paste("medv ~", paste(n[!n %in% "medv"], collapse = " + ")))
> nn <- neuralnet(f,data = train_,hidden = c(5,3),linear.output = FALSE)
> plot(nn)
>
> library(nnetpredint) # Getting Prediction confidence interval
> x <- train_[,-14]
> y <- train_[,14]
> newData <- test_[,-14]
>
> # S3 generic method: Object of nn
> yPredInt <- nnetPredInt(nn, x, y, newData)
Error in (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% :
'length = 2' in coercion to 'logical(1)'
Calls: nnetPredInt -> nnetPredInt.nn -> getPredInt -> checkInput
Execution halted
Running the tests in ‘tests/test2.R’ failed.
Complete output:
> # Example 2: Using the nnet object trained by nnet package
> library(nnet)
> xTrain <- rbind(cbind(runif(150,min = 0, max = 0.5),runif(150,min = 0, max = 0.5)) ,
+ cbind(runif(150,min = 0.5, max = 1),runif(150,min = 0.5, max = 1))
+ )
> nObs <- dim(xTrain)[1]
> yTrain <- 0.5 + 0.4 * sin(2* pi * xTrain %*% c(0.4,0.6)) +rnorm(nObs,mean = 0, sd = 0.05)
> plot(xTrain %*% c(0.4,0.6),yTrain)
>
> # Training nnet models
> net <- nnet(yTrain ~ xTrain,size = 3, rang = 0.1,decay = 5e-4, maxit = 500)
# weights: 13
initial value 35.259828
iter 10 value 9.646561
iter 20 value 4.728526
iter 30 value 3.779644
iter 40 value 2.472166
iter 50 value 2.382817
iter 60 value 2.246882
iter 70 value 2.189990
iter 80 value 1.727944
iter 90 value 1.622836
iter 100 value 1.609302
iter 110 value 1.595428
iter 120 value 1.529652
iter 130 value 1.418099
iter 140 value 1.084052
iter 150 value 0.951194
iter 160 value 0.935696
iter 170 value 0.925880
iter 180 value 0.924930
iter 190 value 0.924115
iter 200 value 0.923442
iter 210 value 0.923399
iter 220 value 0.923311
iter 230 value 0.923292
iter 240 value 0.923289
iter 250 value 0.923280
final value 0.923280
converged
> yFit <- c(net$fitted.values)
> nodeNum <- c(2,3,1)
> wts <- net$wts
>
> # New data for prediction intervals
> library(nnetpredint)
> newData <- cbind(seq(0,1,0.05),seq(0,1,0.05))
> yTest <- 0.5 + 0.4 * sin(2* pi * newData %*% c(0.4,0.6))+rnorm(dim(newData)[1],mean = 0, sd = 0.05)
>
> # S3 generic method: Object of nnet
> yPredInt <- nnetPredInt(net, xTrain, yTrain, newData)
Error in (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% :
'length = 2' in coercion to 'logical(1)'
Calls: nnetPredInt -> nnetPredInt.nnet -> getPredInt -> checkInput
Execution halted
Running the tests in ‘tests/test3.R’ failed.
Complete output:
> # Example 3: Using the rsnns object trained by RSNNS package
> library(RSNNS)
Loading required package: Rcpp
> data(iris)
> #shuffle the vector
> iris <- iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]
> irisValues <- iris[,1:4]
> irisTargets <- decodeClassLabels(iris[,5])[,'setosa']
>
> iris <- splitForTrainingAndTest(irisValues, irisTargets, ratio=0.15)
> iris <- normTrainingAndTestSet(iris)
> model <- mlp(iris$inputsTrain, iris$targetsTrain, size=5, learnFuncParams=c(0.1),
+ maxit=50, inputsTest=iris$inputsTest, targetsTest=iris$targetsTest)
> predictions <- predict(model,iris$inputsTest)
>
>
> # Generating prediction intervals
> library(nnetpredint)
>
> # S3 Method for rsnns class prediction intervals
> xTrain <- iris$inputsTrain
> yTrain <- iris$targetsTrain
> newData <- iris$inputsTest
> yPredInt <- nnetPredInt(model, xTrain, yTrain, newData)
Error in (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% :
'length = 2' in coercion to 'logical(1)'
Calls: nnetPredInt -> nnetPredInt.rsnns -> getPredInt -> checkInput
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.2
Check: tests
Result: ERROR
Running ‘test1.R’
Running ‘test2.R’
Running ‘test3.R’
Running the tests in ‘tests/test1.R’ failed.
Complete output:
> # Example 1: Using the nn object trained by neuralnet package
> set.seed(500)
> library(MASS)
> data <- Boston
> maxs <- apply(data, 2, max)
> mins <- apply(data, 2, min)
> scaled <- as.data.frame(scale(data, center = mins, scale = maxs - mins)) # normalization
> index <- sample(1:nrow(data),round(0.75*nrow(data)))
> train_ <- scaled[index,]
> test_ <- scaled[-index,]
>
> library(neuralnet) # Training
> n <- names(train_)
> f <- as.formula(paste("medv ~", paste(n[!n %in% "medv"], collapse = " + ")))
> nn <- neuralnet(f,data = train_,hidden = c(5,3),linear.output = FALSE)
> plot(nn)
>
> library(nnetpredint) # Getting Prediction confidence interval
> x <- train_[,-14]
> y <- train_[,14]
> newData <- test_[,-14]
>
> # S3 generic method: Object of nn
> yPredInt <- nnetPredInt(nn, x, y, newData)
Error in (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% :
'length = 2' in coercion to 'logical(1)'
Calls: nnetPredInt -> nnetPredInt.nn -> getPredInt -> checkInput
Execution halted
Running the tests in ‘tests/test2.R’ failed.
Complete output:
> # Example 2: Using the nnet object trained by nnet package
> library(nnet)
> xTrain <- rbind(cbind(runif(150,min = 0, max = 0.5),runif(150,min = 0, max = 0.5)) ,
+ cbind(runif(150,min = 0.5, max = 1),runif(150,min = 0.5, max = 1))
+ )
> nObs <- dim(xTrain)[1]
> yTrain <- 0.5 + 0.4 * sin(2* pi * xTrain %*% c(0.4,0.6)) +rnorm(nObs,mean = 0, sd = 0.05)
> plot(xTrain %*% c(0.4,0.6),yTrain)
>
> # Training nnet models
> net <- nnet(yTrain ~ xTrain,size = 3, rang = 0.1,decay = 5e-4, maxit = 500)
# weights: 13
initial value 34.732148
iter 10 value 6.712603
iter 20 value 5.367890
iter 30 value 4.478043
iter 40 value 2.460214
iter 50 value 1.846668
iter 60 value 1.635194
iter 70 value 1.606547
iter 80 value 1.533162
iter 90 value 1.512736
iter 100 value 1.502460
iter 110 value 1.490264
iter 120 value 1.446684
iter 130 value 1.308176
iter 140 value 1.277720
iter 150 value 1.267487
iter 160 value 1.258735
iter 170 value 1.239051
iter 180 value 1.215867
iter 190 value 1.168806
iter 200 value 1.140475
iter 210 value 1.114445
iter 220 value 1.082896
iter 230 value 1.062121
iter 240 value 1.042791
iter 250 value 1.021667
iter 260 value 1.018142
iter 270 value 1.011836
iter 280 value 1.010940
iter 290 value 1.010566
iter 300 value 1.010428
iter 310 value 1.010385
iter 320 value 1.010357
iter 330 value 1.010342
iter 340 value 1.010340
iter 350 value 1.010339
final value 1.010338
converged
> yFit <- c(net$fitted.values)
> nodeNum <- c(2,3,1)
> wts <- net$wts
>
> # New data for prediction intervals
> library(nnetpredint)
> newData <- cbind(seq(0,1,0.05),seq(0,1,0.05))
> yTest <- 0.5 + 0.4 * sin(2* pi * newData %*% c(0.4,0.6))+rnorm(dim(newData)[1],mean = 0, sd = 0.05)
>
> # S3 generic method: Object of nnet
> yPredInt <- nnetPredInt(net, xTrain, yTrain, newData)
Error in (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% :
'length = 2' in coercion to 'logical(1)'
Calls: nnetPredInt -> nnetPredInt.nnet -> getPredInt -> checkInput
Execution halted
Running the tests in ‘tests/test3.R’ failed.
Complete output:
> # Example 3: Using the rsnns object trained by RSNNS package
> library(RSNNS)
Loading required package: Rcpp
> data(iris)
> #shuffle the vector
> iris <- iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]
> irisValues <- iris[,1:4]
> irisTargets <- decodeClassLabels(iris[,5])[,'setosa']
>
> iris <- splitForTrainingAndTest(irisValues, irisTargets, ratio=0.15)
> iris <- normTrainingAndTestSet(iris)
> model <- mlp(iris$inputsTrain, iris$targetsTrain, size=5, learnFuncParams=c(0.1),
+ maxit=50, inputsTest=iris$inputsTest, targetsTest=iris$targetsTest)
> predictions <- predict(model,iris$inputsTest)
>
>
> # Generating prediction intervals
> library(nnetpredint)
>
> # S3 Method for rsnns class prediction intervals
> xTrain <- iris$inputsTrain
> yTrain <- iris$targetsTrain
> newData <- iris$inputsTest
> yPredInt <- nnetPredInt(model, xTrain, yTrain, newData)
Error in (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% :
'length = 2' in coercion to 'logical(1)'
Calls: nnetPredInt -> nnetPredInt.rsnns -> getPredInt -> checkInput
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc