title: caret::train: specify training data parameters
body: <p>I am designing a neural network model that predicts estimation of van genuchten water retention parameters (theta_r, thera_s, alpha, n) using limited to more extended input data like texture, bulk density, and one or two water retention. Investigating neural networks in R project I found RSNNS package and I create and train multiple multi-layer perceptrons (MLPs) with tuning on the number of hidden units and the learning rate. The general performance characterized with training and testing RMSEs for these models is really poor and random, in fact, i used log-transformed values of alpha and n parameters to avoid bias and account for their approximately lognormal distributions but this does not help much :(. I was recommended to work with nnet and caret package but I've had trouble adapting the code i don't know what I'm doing wrong, any suggestion?</p>
  
  <pre><code>#input dataset
  basic &lt;- read.table(url("https://dl.dropboxusercontent.com/s/m8qe4k5swz1m3ij/basic.txt?dl=1&amp;token_hash=AAH6Z3d6fWTLoQZYi04Ys72sdufdERE5gm4v7eF0cgMlkQ"), header=T, sep=" ")
  #output dataset
  fitted &lt;- read.table(url("https://dl.dropboxusercontent.com/s/rjx745ej80osbbu/fitted.txt?dl=1&amp;token_hash=AAHP1zcPQyw4uSe8rw8swVm3Buqe3TP7I1j-4_SOeeUTvw"), header=T, sep=" ")
  
  # Use log-transformed values of alpha and n output parameters
  fitted$alpha &lt;- log(fitted$alpha)
  fitted$n &lt;- log(fitted$n)
  
  
  #Fit model with caret package
  library(caret)
  model &lt;- train(x = basic, y = fitted, method='nnet', linout=TRUE, trace = FALSE,
                 #Grid of tuning parameters to try:
                 tuneGrid=expand.grid(.size=c(1,5,10),.decay=c(0,0.001,0.1)))
  </code></pre>