Error: nrow(x) == n is not TRUE when using Train in Caret

Question

I have a training set that looks like

Name       Day         Area         X    Y    Month Night
ATTACK    Monday   LA           -122.41 37.78   8      0
VEHICLE  Saturday  CHICAGO      -1.67    3.15   2      0
MOUSE     Monday   TAIPEI       -12.5    3.1    9      1

Name is the outcome/dependent variable. I converted Name, Area and Day into factors, but I wasn't sure if I was supposed to for Month and Night, which only take on integer values 1-12 and 0-1, respectively.

I then convert the data into matrix

ynn <- model.matrix(~Name , data = trainDF)
mnn <- model.matrix(~ Day+Area +X + Y + Month + Night, data = trainDF)

I then setup tuning the parameters

nnTrControl=trainControl(method = "repeatedcv",number = 3,repeats=5,verboseIter = TRUE, returnData = FALSE, returnResamp = "all", classProbs = TRUE, summaryFunction = multiClassSummary,allowParallel = TRUE)
nnGrid = expand.grid(.size=c(1,4,7),.decay=c(0,0.001,0.1))
model <- train(y=ynn, x=mnn, method='nnet',linout=TRUE, trace = FALSE, trControl = nnTrControl,metric="logLoss", tuneGrid=nnGrid)

However, I get the error Error: nrow(x) == n is not TRUE for the model<-train

I also get a similar error if I use xgboost instead of nnet

Anyone know whats causing this?

Answer 1

y should be a numeric or factor vector containing the outcome for each sample, not a matrix. Using

train(y = make.names(trainDF$Name), ...)

helps, where make.names modifies values so that they could be valid variable names.

Answer 2

Even though in the help file of train said either maxtrix or data frame would be expected, but you can try to convert the matrix to a data frame:

model <- train(y=ynn, x=as.data.frame(mnn), method='nnet',linout=TRUE, trace = FALSE, trControl = nnTrControl,metric="logLoss", tuneGrid=nnGrid)