title: predicting class for new data using neuralnet
body: <p>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. </p>
  
  <p>The data I have looks as follows:</p>
  
  <p>For train:</p>
  
  <pre><code>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
  </code></pre>
  
  <p>and more.</p>
  
  <p>The test set looks the exact same as the training data, just with different values (if need be I will post some samples).</p>
  
  <p>The code I use looks as follows:</p>
  
  <pre><code>&gt; library(neuralnet)
  &gt; nn &lt;- neuralnet(y ~ x1+x2+x3+x4, data=train, hidden=2, err.fct="ce", linear.output=FALSE)
  &gt; plot(nn)
  &gt; compute(nn, test)
  </code></pre>
  
  <p>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:</p>
  
  <pre><code>Error in neurons[[i]] %*% weights[[i]] : non-conformable arguments
  </code></pre>
  
  <p>So basically I'm trying to train a neural network to successfully classify the new test data.</p>
  
  <p>Any help is appreciated.</p>
  
  <p>Edit:</p>
  
  <p>A sampling of the test object is:</p>
  
  <pre><code>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
  </code></pre>
  
  <p>I've also tried it with the y column empty of any values.</p>