Running examples in ‘randomUniformForest-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: roc.curve
> ### Title: ROC and precision-recall curves for random Uniform Forests
> ### Aliases: roc.curve
> 
> ### ** Examples
> 
> ## Classification : "breast cancer" data 
> # http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
> 
> data(breastCancer)
> breastCancer.data <- breastCancer
> 
> # remove ID (first column) and divide data in train and test set
> breastCancer.data = breastCancer.data[,-1]
> 
> n <- nrow(breastCancer.data)
> p <- ncol(breastCancer.data)
> 
> trainTestIdx <- cut(sample(1:n, n), 2, labels= FALSE)
> 
> # train examples
> breastCancer.data.train <- breastCancer.data[trainTestIdx == 1, -p]
> breastCancer.class.train <- as.factor(breastCancer.data[trainTestIdx == 1, p])
> 
> # rename class in benign (class 2) and malignant (class 4) to have a better view
> levels(breastCancer.class.train) = c("benign", "malignant")
> 
> # test data
> breastCancer.data.test <- breastCancer.data[trainTestIdx == 2, -p]
> breastCancer.class.test <- as.factor(breastCancer.data[trainTestIdx == 2, p])
> 
> levels(breastCancer.class.test) = c("benign", "malignant")
> 
> # compute model : train then test in the same function and assign class weights 
> # to better match the distribution (OOB errors and Breiman's bounds can help to choose weights)
> # Note that in this case 'recall' (or sensitivity) is the objective, 
> # e.g. match all possible cases of malignant tumours even if false positive rate increase 
> #(in this latter case, further steps will reveal the truth). If malignant tumour is not detected, 
> # then diagnosis error is, by far, more critical.
> breastCancer.ruf  <- randomUniformForest(breastCancer.data.train, breastCancer.class.train, 
+ xtest = breastCancer.data.test, ytest = breastCancer.class.test, 
+ classwt = c(1, 3.5), threads = 2, ntree = 40, BreimanBounds = FALSE)
X is a data frame. String or factors have been converted to numeric values.
Error in t(as.numeric(as.vector(X))) : 
  'list' object cannot be coerced to type 'double'
Calls: randomUniformForest ... NAfactor2matrix -> factor2matrix -> as.true.matrix -> t
Execution halted