R neuralNet: “non-conformable arguments”

Question

Argh! I keep getting the following error when attempting to compute with my neural network:

> net.compute <- compute(net, matrix.train2)
Error in neurons[[i]] %*% weights[[i]] : non-conformable arguments

I can't figure out what the problem is. Below I'll provide you with an example data and formatting from my matrices and then I'll show you the code I'm attempting to run.

• matrix.train1 is used for training the network

  > matrix.train1
      (Intercept) survived pclass sexmale    age sibsp parch     fare embarkedC embarkedQ embarkedS
  1             1        0      3       1  22.00     1     0   7.2500         0         0             1
  2             1        1      1       0  38.00     1     0  71.2833         1         0         0
  3             1        1      3       0  26.00     0     0   7.9250         0         0         1
  4             1        1      1       0  35.00     1     0  53.1000         0         0         1
  5             1        0      3       1  35.00     0     0   8.0500         0         0         1
  6             1        0      3       1 999.00     0     0   8.4583         0         1         0
  7             1        0      1       1  54.00     0     0  51.8625         0         0         1
  8             1        0      3       1   2.00     3     1  21.0750         0         0         1
  9             1        1      3       0  27.00     0     2  11.1333         0         0         1
  10            1        1      2       0  14.00     1     0  30.0708         1         0         0
  11            1        1      3       0   4.00     1     1  16.7000         0         0         1
  

• matrix.train2 is a slice of the training data used for testing the model

  > matrix.train2
      (Intercept) pclass sexmale    age sibsp parch     fare embarkedC embarkedQ embarkedS
  1             1      1       1  49.00     1     1 110.8833         1         0         0
  2             1      3       1  42.00     0     0   7.6500         0         0         1
  3             1      1       0  18.00     1     0 227.5250         1         0         0
  4             1      1       1  35.00     0     0  26.2875         0         0         1
  5             1      3       0  18.00     0     1  14.4542         1         0         0
  6             1      3       1  25.00     0     0   7.7417         0         1         0
  7             1      3       1  26.00     1     0   7.8542         0         0         1
  8             1      2       1  39.00     0     0  26.0000         0         0         1
  9             1      2       0  45.00     0     0  13.5000         0         0         1
  10            1      1       1  42.00     0     0  26.2875         0         0         1
  11            1      1       0  22.00     0     0 151.5500         0         0         1
  

The only real difference between the two matrices is that matrix.train2 doesn't contain the survived column.

Here's the R code I'm attempting to run:

#Build a matrix from training data 
matrix.train1 <- model.matrix(
  ~ survived + pclass + sex + age + sibsp + parch + fare + embarked, 
  data=train1
)

library(neuralnet)

#Train the neural net
net <- neuralnet(
  survived ~ pclass + sexmale + age + sibsp + parch + fare + embarkedC + 
  embarkedQ + embarkedS, data=matrix.train1, hidden=10, threshold=0.01
)

#Build a matrix from test data
matrix.train2 <- model.matrix(
  ~ pclass + sex + age + sibsp + parch + fare + embarked, 
  data=train2
)

#Apply neural net to test matrix 
net.results <- compute(
  net, matrix.train2
)

Error in neurons[[i]] %*% weights[[i]] : non-conformable arguments

Can anyone tell me what I'm doing wrong here?

Thanks!

---

Updates based on comments so far:

1. Using the solution from "Predicting class for new data using neuralnet" doesn't seem to work.

   > net.compute <- compute(net, matrix.train2[,1:10])
   Error in neurons[[i]] %*% weights[[i]] : non-conformable arguments
   

2. I'm manually putting my train1 and train2 data frames into matrices via model.matrix because if I don't I get the following error:

   > Error in neurons[[i]] %*% weights[[i]] : 
   requires numeric/complex matrix/vector arguments
   

Note: see the following thread for more details on why I'm using model.matrix: "Working with neuralnet in R for the first time: get “requires numeric/complex matrix/vector arguments” but don't know how to correct".

Answer 1

It looks like you need to remove the predictor variable. Try this:

nn_pred<-compute(nn,test[,3:11])

Answer 2

Try the answer used for this questions,predicting class for new data using neuralnet

Answer 3

I haven't used the neuralnet ackage, but unless it's doing something weird you shouldn't be calling model.matrix like that. neuralnet has a formula interface, so it will call model.matrix for you. You just have to give it the training data frame train1.

This also applies for predicting on test data. Don't create a model matrix; just pass it the data frame train2.

Answer 4

I tried this with the neuralnet package as well. I think if you instead of

net.results <- compute(
  net, matrix.train2

do

net.result <- compute(  
     net, matrix.train2[,c("pclass",   
     "sexmale", "age", "sibsp", "parch",   
     "fare","embarkedC","embarkedQ","embaredS")])

it should work. The names of the variables needs to be in the exact order of the model.list$variables, so you can also type

net.result <- compute(  
     net, matrix.train2[, net.result$model.list$variables])

I hope this helps. The reason is - I think - that neuralnet has a problem finding out which variables are in your net and which in the matrix... so you match them explicitly instead.

Answer 5

change the neuralnet train to this

t <- neuralnet(
survived ~ pclass + sexmale + age + sibsp + parch + fare + embarkedC + 
embarkedQ + embarkedS)

change the predictor variable to

NN_pred<-compute(t,test[,1:9])

it should have the same order of the data taken in a model