* using log directory ‘/data/gannet/ripley/R/packages/tests-LENGTH1/nnetpredint.Rcheck’
* using R Under development (unstable) (2022-04-26 r82260)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-stop-on-test-error’
* checking for file ‘nnetpredint/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘nnetpredint’ version ‘1.2’
* 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 executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘nnetpredint’ can be installed ... 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
* 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 loading without being on the library search path ... OK
* checking use of S3 registration ... 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 ... [7s/18s] OK
* checking Rd files ... 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
* checking Rd metadata ... OK
* checking Rd line widths ... 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 examples ... [4s/10s] 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)
 ----------- FAILURE REPORT -------------- 
 --- failure: length > 1 in coercion to logical ---
 --- srcref --- 
: 
 --- package (from environment) --- 
nnetpredint
 --- call from context --- 
checkInput(xTrain, yTrain, yFit, node, wts, newData)
 --- call from argument --- 
(class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% 
    c("matrix", "data.frame", "numeric")) && (class(yFit) %in% 
    c("matrix", "data.frame", "numeric"))
 --- R stacktrace ---
where 1: checkInput(xTrain, yTrain, yFit, node, wts, newData)
where 2: getPredInt(xTrain, yTrain, yFit, nodeNum, wts, newData, alpha = alpha, 
    lambda = lambda, funName = funName)
where 3: nnetPredInt.nn(nn, x, y, newData)
where 4: nnetPredInt(nn, x, y, newData)

 --- value of length: 2 type: logical ---
[1]  TRUE FALSE
 --- function from context --- 
function (xTrain, yTrain, yFit, node, wts, newData) 
{
    checkResult = 1
    argsCheck = ((is.null(xTrain) == FALSE)) && ((is.null(yTrain) == 
        FALSE)) && ((is.null(yFit) == FALSE)) && ((is.null(node) == 
        FALSE)) && ((is.null(wts) == FALSE)) && ((is.null(newData) == 
        FALSE))
    if (argsCheck == FALSE) {
        checkResult = checkResult * 0
        message("Error: Input Arguments are missing nor be NULL")
        return(0)
    }
    nodeCheck = (class(node) == "numeric" && (is.null(node) == 
        FALSE) && (length(node) >= 3))
    if (nodeCheck == FALSE) {
        checkResult = checkResult * 0
        message("Error: Node argument is not numeric vector with enough length at least 3")
        return(0)
    }
    wtsCheck = (class(wts) == "numeric" && (is.null(wts) == FALSE))
    if (wtsCheck == FALSE) {
        checkResult = checkResult * 0
        message("Error: wts argument is not numeric vector")
        return(0)
    }
    m = length(node) - 1
    nPara = 0
    for (i in 1:m) {
        nPara = nPara + (node[i] + 1) * node[i + 1]
    }
    nParaCheck = (nPara == length(wts))
    if (nParaCheck == FALSE) {
        checkResult = checkResult * 0
        message("Error: wts parameter total number is not correct based on the network structure defined by 'node'")
        return(0)
    }
    classCheck = (class(xTrain) %in% c("matrix", "data.frame")) && 
        (class(yTrain) %in% c("matrix", "data.frame", "numeric")) && 
        (class(yFit) %in% c("matrix", "data.frame", "numeric")) && 
        (class(newData) %in% c("matrix", "data.frame", "numeric"))
    if (classCheck == FALSE) {
        checkResult = checkResult * 0
        message("Error: xTrain,yTrain,yFit,newData should be class of either matrix,data.frame or numeric vector")
        return(0)
    }
    nObs = dim(xTrain)[1]
    nObsCheck = (nObs == dim(matrix(yTrain, ncol = 1))[1]) && 
        (nObs == dim(matrix(yFit, ncol = 1))[1])
    if (nObsCheck == FALSE) {
        checkResult = checkResult * 0
        message("Error: Number of observations for xTrain, yTrain, yFit are not the same")
        return(0)
    }
    nDim = node[1]
    dimCheck = (nDim == dim(xTrain)[2]) && (nDim == dim(newData)[2])
    if (dimCheck == FALSE) {
        checkResult = checkResult * 0
        message("Error: xTrain,newData input dimension are not the same as node structure")
        return(0)
    }
    return(checkResult)
}
<bytecode: 0x64923d0>
<environment: namespace:nnetpredint>
 --- function search by body ---
Function checkInput in namespace nnetpredint has this body.
 ----------- END OF FAILURE REPORT -------------- 
Fatal error: length > 1 in coercion to logical
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘test1.R’
  Running ‘test2.R’
  Running ‘test3.R’
 [7s/20s] ERROR
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)
   ----------- FAILURE REPORT -------------- 
   --- failure: length > 1 in coercion to logical ---
   --- srcref --- 
  : 
   --- package (from environment) --- 
  nnetpredint
   --- call from context --- 
  checkInput(xTrain, yTrain, yFit, node, wts, newData)
   --- call from argument --- 
  (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% 
      c("matrix", "data.frame", "numeric")) && (class(yFit) %in% 
      c("matrix", "data.frame", "numeric"))
   --- R stacktrace ---
  where 1: checkInput(xTrain, yTrain, yFit, node, wts, newData)
  where 2: getPredInt(xTrain, yTrain, yFit, nodeNum, wts, newData, alpha = alpha, 
      lambda = lambda, funName = funName)
  where 3: nnetPredInt.nn(nn, x, y, newData)
  where 4: nnetPredInt(nn, x, y, newData)
  
   --- value of length: 2 type: logical ---
  [1]  TRUE FALSE
   --- function from context --- 
  function (xTrain, yTrain, yFit, node, wts, newData) 
  {
      checkResult = 1
      argsCheck = ((is.null(xTrain) == FALSE)) && ((is.null(yTrain) == 
          FALSE)) && ((is.null(yFit) == FALSE)) && ((is.null(node) == 
          FALSE)) && ((is.null(wts) == FALSE)) && ((is.null(newData) == 
          FALSE))
      if (argsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Input Arguments are missing nor be NULL")
          return(0)
      }
      nodeCheck = (class(node) == "numeric" && (is.null(node) == 
          FALSE) && (length(node) >= 3))
      if (nodeCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Node argument is not numeric vector with enough length at least 3")
          return(0)
      }
      wtsCheck = (class(wts) == "numeric" && (is.null(wts) == FALSE))
      if (wtsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: wts argument is not numeric vector")
          return(0)
      }
      m = length(node) - 1
      nPara = 0
      for (i in 1:m) {
          nPara = nPara + (node[i] + 1) * node[i + 1]
      }
      nParaCheck = (nPara == length(wts))
      if (nParaCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: wts parameter total number is not correct based on the network structure defined by 'node'")
          return(0)
      }
      classCheck = (class(xTrain) %in% c("matrix", "data.frame")) && 
          (class(yTrain) %in% c("matrix", "data.frame", "numeric")) && 
          (class(yFit) %in% c("matrix", "data.frame", "numeric")) && 
          (class(newData) %in% c("matrix", "data.frame", "numeric"))
      if (classCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: xTrain,yTrain,yFit,newData should be class of either matrix,data.frame or numeric vector")
          return(0)
      }
      nObs = dim(xTrain)[1]
      nObsCheck = (nObs == dim(matrix(yTrain, ncol = 1))[1]) && 
          (nObs == dim(matrix(yFit, ncol = 1))[1])
      if (nObsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Number of observations for xTrain, yTrain, yFit are not the same")
          return(0)
      }
      nDim = node[1]
      dimCheck = (nDim == dim(xTrain)[2]) && (nDim == dim(newData)[2])
      if (dimCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: xTrain,newData input dimension are not the same as node structure")
          return(0)
      }
      return(checkResult)
  }
  <bytecode: 0x2859368>
  <environment: namespace:nnetpredint>
   --- function search by body ---
  Function checkInput in namespace nnetpredint has this body.
   ----------- END OF FAILURE REPORT -------------- 
  Fatal error: length > 1 in coercion to logical
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 33.262808 
  iter  10 value 14.279423
  iter  20 value 9.032988
  iter  30 value 6.353614
  iter  40 value 3.717825
  iter  50 value 2.749094
  iter  60 value 2.397000
  iter  70 value 2.065820
  iter  80 value 1.915181
  iter  90 value 1.806376
  iter 100 value 1.728587
  iter 110 value 1.713637
  iter 120 value 1.711891
  iter 130 value 1.711273
  iter 140 value 1.709737
  iter 150 value 1.698773
  iter 160 value 1.672452
  iter 170 value 1.306528
  iter 180 value 0.987920
  iter 190 value 0.953645
  iter 200 value 0.946768
  iter 210 value 0.943639
  iter 220 value 0.942424
  iter 230 value 0.941723
  iter 240 value 0.941593
  iter 250 value 0.941553
  iter 260 value 0.941536
  iter 270 value 0.941534
  iter 280 value 0.941531
  final  value 0.941531 
  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)
   ----------- FAILURE REPORT -------------- 
   --- failure: length > 1 in coercion to logical ---
   --- srcref --- 
  : 
   --- package (from environment) --- 
  nnetpredint
   --- call from context --- 
  checkInput(xTrain, yTrain, yFit, node, wts, newData)
   --- call from argument --- 
  (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% 
      c("matrix", "data.frame", "numeric"))
   --- R stacktrace ---
  where 1: checkInput(xTrain, yTrain, yFit, node, wts, newData)
  where 2: getPredInt(xTrain, yTrain, yFit, nodeNum, wts, newData, alpha = alpha, 
      lambda = lambda, funName = funName)
  where 3: nnetPredInt.nnet(net, xTrain, yTrain, newData)
  where 4: nnetPredInt(net, xTrain, yTrain, newData)
  
   --- value of length: 2 type: logical ---
  [1]  TRUE FALSE
   --- function from context --- 
  function (xTrain, yTrain, yFit, node, wts, newData) 
  {
      checkResult = 1
      argsCheck = ((is.null(xTrain) == FALSE)) && ((is.null(yTrain) == 
          FALSE)) && ((is.null(yFit) == FALSE)) && ((is.null(node) == 
          FALSE)) && ((is.null(wts) == FALSE)) && ((is.null(newData) == 
          FALSE))
      if (argsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Input Arguments are missing nor be NULL")
          return(0)
      }
      nodeCheck = (class(node) == "numeric" && (is.null(node) == 
          FALSE) && (length(node) >= 3))
      if (nodeCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Node argument is not numeric vector with enough length at least 3")
          return(0)
      }
      wtsCheck = (class(wts) == "numeric" && (is.null(wts) == FALSE))
      if (wtsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: wts argument is not numeric vector")
          return(0)
      }
      m = length(node) - 1
      nPara = 0
      for (i in 1:m) {
          nPara = nPara + (node[i] + 1) * node[i + 1]
      }
      nParaCheck = (nPara == length(wts))
      if (nParaCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: wts parameter total number is not correct based on the network structure defined by 'node'")
          return(0)
      }
      classCheck = (class(xTrain) %in% c("matrix", "data.frame")) && 
          (class(yTrain) %in% c("matrix", "data.frame", "numeric")) && 
          (class(yFit) %in% c("matrix", "data.frame", "numeric")) && 
          (class(newData) %in% c("matrix", "data.frame", "numeric"))
      if (classCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: xTrain,yTrain,yFit,newData should be class of either matrix,data.frame or numeric vector")
          return(0)
      }
      nObs = dim(xTrain)[1]
      nObsCheck = (nObs == dim(matrix(yTrain, ncol = 1))[1]) && 
          (nObs == dim(matrix(yFit, ncol = 1))[1])
      if (nObsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Number of observations for xTrain, yTrain, yFit are not the same")
          return(0)
      }
      nDim = node[1]
      dimCheck = (nDim == dim(xTrain)[2]) && (nDim == dim(newData)[2])
      if (dimCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: xTrain,newData input dimension are not the same as node structure")
          return(0)
      }
      return(checkResult)
  }
  <bytecode: 0x37b8ad8>
  <environment: namespace:nnetpredint>
   --- function search by body ---
  Function checkInput in namespace nnetpredint has this body.
   ----------- END OF FAILURE REPORT -------------- 
  Fatal error: length > 1 in coercion to logical
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)
   ----------- FAILURE REPORT -------------- 
   --- failure: length > 1 in coercion to logical ---
   --- srcref --- 
  : 
   --- package (from environment) --- 
  nnetpredint
   --- call from context --- 
  checkInput(xTrain, yTrain, yFit, node, wts, newData)
   --- call from argument --- 
  (class(xTrain) %in% c("matrix", "data.frame")) && (class(yTrain) %in% 
      c("matrix", "data.frame", "numeric"))
   --- R stacktrace ---
  where 1: checkInput(xTrain, yTrain, yFit, node, wts, newData)
  where 2: getPredInt(xTrain, yTrain, yFit, nodeNum, wts, newData, alpha = alpha, 
      lambda = lambda, funName = funName)
  where 3: nnetPredInt.rsnns(model, xTrain, yTrain, newData)
  where 4: nnetPredInt(model, xTrain, yTrain, newData)
  
   --- value of length: 2 type: logical ---
  [1]  TRUE FALSE
   --- function from context --- 
  function (xTrain, yTrain, yFit, node, wts, newData) 
  {
      checkResult = 1
      argsCheck = ((is.null(xTrain) == FALSE)) && ((is.null(yTrain) == 
          FALSE)) && ((is.null(yFit) == FALSE)) && ((is.null(node) == 
          FALSE)) && ((is.null(wts) == FALSE)) && ((is.null(newData) == 
          FALSE))
      if (argsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Input Arguments are missing nor be NULL")
          return(0)
      }
      nodeCheck = (class(node) == "numeric" && (is.null(node) == 
          FALSE) && (length(node) >= 3))
      if (nodeCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Node argument is not numeric vector with enough length at least 3")
          return(0)
      }
      wtsCheck = (class(wts) == "numeric" && (is.null(wts) == FALSE))
      if (wtsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: wts argument is not numeric vector")
          return(0)
      }
      m = length(node) - 1
      nPara = 0
      for (i in 1:m) {
          nPara = nPara + (node[i] + 1) * node[i + 1]
      }
      nParaCheck = (nPara == length(wts))
      if (nParaCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: wts parameter total number is not correct based on the network structure defined by 'node'")
          return(0)
      }
      classCheck = (class(xTrain) %in% c("matrix", "data.frame")) && 
          (class(yTrain) %in% c("matrix", "data.frame", "numeric")) && 
          (class(yFit) %in% c("matrix", "data.frame", "numeric")) && 
          (class(newData) %in% c("matrix", "data.frame", "numeric"))
      if (classCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: xTrain,yTrain,yFit,newData should be class of either matrix,data.frame or numeric vector")
          return(0)
      }
      nObs = dim(xTrain)[1]
      nObsCheck = (nObs == dim(matrix(yTrain, ncol = 1))[1]) && 
          (nObs == dim(matrix(yFit, ncol = 1))[1])
      if (nObsCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: Number of observations for xTrain, yTrain, yFit are not the same")
          return(0)
      }
      nDim = node[1]
      dimCheck = (nDim == dim(xTrain)[2]) && (nDim == dim(newData)[2])
      if (dimCheck == FALSE) {
          checkResult = checkResult * 0
          message("Error: xTrain,newData input dimension are not the same as node structure")
          return(0)
      }
      return(checkResult)
  }
  <bytecode: 0x26ed398>
  <environment: namespace:nnetpredint>
   --- function search by body ---
  Function checkInput in namespace nnetpredint has this body.
   ----------- END OF FAILURE REPORT -------------- 
  Fatal error: length > 1 in coercion to logical
* checking PDF version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 2 ERRORs, 1 NOTE
See
  ‘/data/gannet/ripley/R/packages/tests-LENGTH1/nnetpredint.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 2:19.40, 45.09 + 7.48