predicting class for new data using neuralnet

I'm trying to predict the class (0 or 1) for a test dataset using a neural network trained using the neuralnet package in R. The data I have looks as follows: For train: ``` x1 x2 x3 x4 y 0.557 0.6217009 0.4839 0.5606936 0 0.6549 0.6826347 0.4424 0.4117647 1 0.529 0.5744681 0.5017 0.4148148 1 0.6016771 0.5737052 0.3526971 0.3369565 1 0.6353945 0.6445013 0.5404255 0.464 1 0.5735294 0.6440678 0.4385965 0.5698925 1 0.5252 0.5900621 0.4412 0.448 0 0.7258687 0.7022059 0.5347222 0.4498645 1 ``` and more. The test set looks the exact same as the training data, just with different values (if need be I will post some samples). The code I use looks as follows: ``` > library(neuralnet) > nn <- neuralnet(y ~ x1+x2+x3+x4, data=train, hidden=2, err.fct="ce", linear.output=FALSE) > plot(nn) > compute(nn, test) ``` The network trains and I can successfully plot the network, but compute doesn't work. When I run compute it gives me the following error: ``` Error in neurons[[i]] %*% weights[[i]] : non-conformable arguments ``` So basically I'm trying to train a neural network to successfully classify the new test data. Any help is appreciated. Edit: A sampling of the test object is: ``` x1 x2 x3 x4 y 0.5822 0.6591 0.6445013 0.464 1 0.4082 0.5388 0.5384616 0.4615385 0 0.4481 0.5438 0.6072289 0.5400844 1 0.4416 0.5034 0.5576923 0.3757576 1 0.5038 0.6878 0.7380952 0.5784314 1 0.4678 0.5219 0.5609756 0.3636364 1 0.5089 0.5775 0.6183844 0.5462555 1 0.4844 0.7117 0.6875 0.4823529 1 0.4098 0.711 0.6801471 0.4722222 1 ``` I've also tried it with the y column empty of any values.