R - NeuralNet

require(foreign)
require(nnet)
require(ggplot2)
require(reshape2)
setwd("/Volumes/16 DOS/R_nbs")

m<-read.csv("DadosTeseLogit3.csv",sep=",",header=TRUE,fileEncoding="latin1")
head(m,2)
with(m,table(c0_dados,selected))
hist(m$c0_dados,col="green")
hist(m$selected,col="green")
hist(m$c0_recomend,col="green")
cor(m)


m<-m[,which(cor(m)[,31]>0.5)]
head(m)
m0<-m[,-4]
t<-paste(names(m0),collapse="+")
form<-formula(paste("selected~",t))
form
head(m)
range01 <- function(x){(x-min(x))/(max(x)-min(x))}
m[1]

for i in (1,dim(m)[[2]]):{
  m[i]<-lapply(m[1], range01)
test<-multinom(form,data=m[10:98,],MaxNWts=3000,maxit=15000)
summary(test)
summary(test)$coefficients
pred<-predict(test,m[1:9,])
pred
m$selected[1:9]
as.numeric(pred)
cc<-as.numeric(pred)-as.numeric(m$selected[1:9])

accuracy=length(which(cc==0))/length(cc)