# [R] Ridge regression [Repost]

Sabyasachi Patra sabya23 at gmail.com
Wed Aug 19 19:48:16 CEST 2009

```Dear all,

For an ordinary ridge regression problem, I followed three different
approaches:
1. estimate beta without any standardization
2. estimate standardized beta (standardizing X and y) and then again convert
back
3. estimate beta using lm.ridge() function

X<-matrix(c(1,2,9,3,2,4,7,2,3,5,9,1),4,3)
y<-as.matrix(c(2,3,4,5))

n<-nrow(X)
p<-ncol(X)

#Without standardization
intercept <- rep(1,n)
Xn <- cbind(intercept, X)
K<-diag(c(0,rep(1.5,p)))
beta1 <- solve(t(Xn)%*%Xn+K)%*%t(Xn)%*%y
beta1

#with standardization
ys<-scale(y)
Xs<-scale(X)
K<-diag(1.5,p)
bs <- solve(t(Xs)%*%Xs+K)%*%t(Xs)%*%ys
b<- sd(y)*(bs/sd(X))
intercept <- mean(y)-sum(as.matrix(colMeans(X))*b)
beta2<-rbind(intercept,b)
beta2

#Using lm.ridge function of MASS package
beta3<-lm.ridge(y~X,lambda=1.5)

I'm getting three different results using above described approaches:

> beta1
[,1]
intercept  3.4007944
0.3977462
0.2082025
-0.4829115
> beta2
[,1]
intercept  3.3399855
0.1639469
0.0262021
-0.1228987

> beta3
X1          X2          X3
3.35158977  0.19460958  0.03152778 -0.15546775

It will be very helpful to me if anybody can help me regarding why the
outputs are coming different.

I am extremely sorry for my previous anonymous post.

regards.

-----
Sabyasachi Patra
PhD Scholar
Indian institute of Technology Kanpur
India.
--
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