R Code Example for Neural Networks

neuralnet.r

#  ------------------------------------------------------------------
#  |PROGRAM NAME: NEURALNET_PKG_R
#  |DATE: 12/3/10
#  |CREATED BY: MATT BOGARD 
#  |PROJECT FILE: http://econometricsense.blogspot.com/2010/12/r-code-example-for-neural-networks.html         
#  |----------------------------------------------------------------
#  | PURPOSE: DEMO OF THE 'neuralnet' PACKAGE AND OUTPUT INTERPRETATION              
#  | 
#  |  ADAPTED FROM:  neuralnet: Training of Neural Networks
#  |    by Frauke Günther and Stefan Fritsch The R Journal Vol. 2/1, June 2010 
#  |    ISSN 2073-4859  (LOCATED: P:\TOOLS AND REFERENCES (Copy)\R References\Neural Networks
#  | 
#  | 
#  |------------------------------------------------------------------
#  |DATA USED: 'infert' FROM THE 'datasets' LIBRARY              
#  |------------------------------------------------------------------
#  |CONTENTS:               
#  |
#  |  PART 1: get the data 
#  |  PART 2: train the network
#  |  PART 3: 
#  |  PART 4: 
#  |  PART 5: 
#  |------------------------------------------------------------------
#  |COMMENTS:               
#  |
#  |-----------------------------------------------------------------
#  |UPDATES:               
#  |
#  |
#  |------------------------------------------------------------------
 
#  *------------------------------------------------------------------*
#  | get the data
#  *------------------------------------------------------------------* 
 
library(datasets)
 
names(infert)
 
#  *------------------------------------------------------------------*
#  | train the network
#  *------------------------------------------------------------------* 
 
library(neuralnet)
 
nn <- neuralnet(
 case~age+parity+induced+spontaneous,
 data=infert, hidden=2, err.fct="ce",
 linear.output=FALSE)
 
#  *------------------------------------------------------------------*
#  | output training results
#  *------------------------------------------------------------------*  
 
# basic
nn
 
# reults options
names(nn)
 
 
# result matrix
 
nn$result.matrix
 
# The given data is saved in nn$covariate and
# nn$response as well as in nn$data for the whole data
# set inclusive non-used variables. The output of the
# neural network, i.e. the fitted values o(x), is provided
# by nn$net.result:
 
out <- cbind(nn$covariate,nn$net.result[[1]])
 
dimnames(out) <- list(NULL, c("age", "parity","induced","spontaneous","nn-output"))
 
head(out)
 
# generalized weights
 
# The generalized weight expresses the effect of each
# ovariate xi and thus has an analogous interpretation
# as the ith regression parameter in regression models.
# However, the generalized weight depends on all
# other covariates. Its distribution indicates whether
# the effect of the covariate is linear since a small variance
# suggests a linear effect
 
# The columns refer to the four covariates age (j =
# 1), parity (j = 2), induced (j = 3), and spontaneous (j=4)
 
head(nn$generalized.weights[[1]])
 
# visualization
 
plot(nn)