Neural Network resource

# set random seed to a cool number
set.seed(42)

# set up data
x1<-rnorm(100); x2<-rnorm(100); x3<-rnorm(100)
x4<-rnorm(100); x5<-rnorm(100); x6<-rnorm(100)
b1<-1; b2<-2; b3<-3
b4<-4; b5<-5; b6<-6
y<-b1*x1 + b2*x2 + b3*x3 + b4*x4 + b5*x5 + b6*x6
my.df <- data.frame(cbind(y, x1, x2, x3, x4, x5, x6))

# 1. linear regression
my.lm <- lm(y~., data=my.df)

# look at correlation
my.lm.predictions<-predict(my.lm)
cor(my.df["y"], my.lm.predictions)

# 2. nnet
library(nnet)
my.nnet<-nnet(y~., data=my.df, size=3,

my.nnet.predictions<-predict(my.nnet, my.df)
# look at correlation
cor(my.df["y"], my.nnet.predictions)

# to look at the values side by side
cbind(my.df["y"], my.nnet.predictions)

You are right there is a pdf file which describes the function. But let tell you where I am coming from.

Just to test if a neural network will work better than a ordinary least square regression, I created a dataset with one dependent variable and 6 other independent variables. Now I had deliberately created the dataset in such manner that we have an excellent regression model. Eg: Y = b0 + b1*x1 + b2*x2 + b3*x3.. + b6*x6 + e
where e is normal random variable. Naturally any statistical analysis system running regression would easily predict the values of b1, b2, b3, ..., b6 with around 30-40 observations.

I fed this data into a Neural network (3 hidden layers with 6 neurons in each layer) and trained the network. When I passed the input dataset and tried to get the predictions, all the predicted values were identical! This confused me a bit and was wondering whether my understanding of the Neural Network was wrong.

Have you ever faced anything like it?

Regards,
Indrajit