Perform cross-validation on randomForest with R - r

Question

I am using the randomForest package for R to train a model for classification.
To compare it to other classifiers, I need a way to display all the information given by the rather verbose cross-validation method in Weka. Therefore, the R script should output somesthing like [a] from Weka.
Is there a way to validate an R model via RWeka to produce those measures?
If not, how is a cross-validation on a random forest done purely in R?
Is it possble to use rfcv from the randomForest package here? I could not get it to work.
I do know that the out-of-bag error (OOB) used in randomForest is some kind of a cross-validation. But I need the full information for a suited comparison.
What I tried so far using R is [b]. However, the code also produces an error on my setup [c] due to missing values.
So, can you help me with the cross-validation?
Appendix
[a] Weka:
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 3059 96.712 %
Incorrectly Classified Instances 104 3.288 %
Kappa statistic 0.8199
Mean absolute error 0.1017
Root mean squared error 0.1771
Relative absolute error 60.4205 %
Root relative squared error 61.103 %
Coverage of cases (0.95 level) 99.6206 %
Mean rel. region size (0.95 level) 78.043 %
Total Number of Instances 3163
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0,918 0,028 0,771 0,918 0,838 0,824 0,985 0,901 sick-euthyroid
0,972 0,082 0,991 0,972 0,982 0,824 0,985 0,998 negative
Weighted Avg. 0,967 0,077 0,971 0,967 0,968 0,824 0,985 0,989
=== Confusion Matrix ===
a b <-- classified as
269 24 | a = sick-euthyroid
80 2790 | b = negative
[b] Code so far:
library(randomForest) #randomForest() and rfImpute()
library(foreign) # read.arff()
library(caret) # train() and trainControl()
nTrees <- 2 # 200
myDataset <- 'D:\\your\\directory\\SE.arff' # http://hakank.org/weka/SE.arff
mydb = read.arff(myDataset)
mydb.imputed <- rfImpute(class ~ ., data=mydb, ntree = nTrees, importance = TRUE)
myres.rf <- randomForest(class ~ ., data=mydb.imputed, ntree = nTrees, importance = TRUE)
summary(myres.rf)
# specify type of resampling to 10-fold CV
fitControl <- trainControl(method = "rf",number = 10,repeats = 10)
set.seed(825)
# deal with NA | NULL values in categorical variables
#mydb.imputed[is.na(mydb.imputed)] <- 1
#mydb.imputed[is.null(mydb.imputed)] <- 1
rfFit <- train(class~ ., data=mydb.imputed,
method = "rf",
trControl = fitControl,
## This last option is actually one
## for rf() that passes through
ntree = nTrees, importance = TRUE, na.action = na.omit)
rfFit
The error is:
Error in names(resamples) <- gsub("^\\.", "", names(resamples)) :
attempt to set an attribute on NULL
Using traceback()
5: nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
method = models, ppOpts = preProcess, ctrl = trControl, lev = classLevels,
...)
4: train.default(x, y, weights = w, ...)
3: train(x, y, weights = w, ...)
2: train.formula(class~ ., data = mydb.imputed, method = "rf",
trControl = fitControl, ntree = nTrees, importance = TRUE,
sampsize = rep(minorityClassNum, 2), na.action = na.omit)
1: train(class~ ., data = mydb.imputed, method = "rf", trControl = fitControl,
ntree = nTrees, importance = TRUE, sampsize = rep(minorityClassNum,
2), na.action = na.omit) at #39
[c] R version information via sessionInfo()
R version 3.1.0 (2014-04-10)
Platform: i386-w64-mingw32/i386 (32-bit)
[...]
other attached packages:
[1] e1071_1.6-3 caret_6.0-30 ggplot2_1.0.0 foreign_0.8-61 randomForest_4.6-7 DMwR_0.4.1
[7] lattice_0.20-29 JGR_1.7-16 iplots_1.1-7 JavaGD_0.6-1 rJava_0.9-6

Answer 1

I dont know about weka, but i have done randomForest modelling in R and I have always used predict function in R to do this.
Try using this function
predict(Model,data)
Bind the output with original values and use table command to get the confusion matrix.