# R nnet (Caret) not giving results for size = 8 and above

This is my first post in CrossValidated hence please let me know if I may have inadvertently violated forum rules.

I am working with nnet using Caret in R and when I am running experiments using the tuning grid I am somehow not able to get any results with size = 8 and above.

My code is as follows:

   set.seed(seedVal)

### creating a grid of tuning parameters
nnetTunegrid <- expand.grid(.size = seq(min_tune,max_tune,step_tune),
.decay = seq(0,4,0.125))

# set seeds array for cross validation
seeds <- setSeeds(cv_count, cv_repeats, nrow(nnetTunegrid), seedVal)

# Define cross-validation experiment
numFolds = trainControl(method = "cv",
number = cv_count,
#repeats = cv_repeats,
seeds = seeds,
classProbs = TRUE,
summaryFunction = twoClassSummary)

registerDoParallel(cores = 6)

nnetFit <- train(x = train_matrix, y = catg_labels,
method = "nnet",
preProc = preProcessing,
trControl = numFolds,
tuneGrid = nnetTunegrid,
maxit = 500, # max iterations for nnet only
metric = metricVal)


My data set has 150 features and I am using nnet to do binary classification.

Any help or pointers to resolve this problem would be appreciated!

Thanks Ian