[R] nnet question

[Aimin Yan]

Hello,
I use nnet to do prediction for a continuous variable.
after that, I calculate correlation coefficient between predicted value and 
real observation.

I run my code(see following) several time, but I get different correlation 
coefficient each time.

Anyone know why?

In addition, How to calculate prediction accuracy for prediction of 
continuous variable?

Aimin
thanks,


 > m.nn.omega <- nnet(omega~aa_three+bas+bcu+aa_ss, data=training, size=2, 
linout=TRUE)
# weights:  57
initial  value 89153525.582093
iter  10 value 15036439.951888
iter  20 value 15010796.121891
iter  30 value 15000761.804392
iter  40 value 14955839.294531
iter  50 value 14934746.564215
iter  60 value 14933978.758615
iter  70 value 14555668.381007
iter  80 value 14553072.231507
iter  90 value 14031071.223996
iter 100 value 13709055.312482
final  value 13709055.312482
stopped after 100 iterations
 > pr.nn.train<-predict(m.nn.omega,training)
 > corr.pr.nn.train<-round(cor(pr.nn.train,training$omega),2)
 > pr.nn.test<-predict(m.nn.omega,test)
 > corr.pr.nn.test<-round(cor(pr.nn.test,test$omega),2)
 > cat("correlation coefficient for train using neural 
network:",corr.pr.nn.train,"\n")
correlation coefficient for train using neural network: 0.32
 > cat("correlation coefficient for test using neural 
network:",corr.pr.nn.test,"\n")
correlation coefficient for test using neural network: 0.39