regpro (version 0.1.1)

linear: Multivariate linear ridge regression estimator

Description

Computes the parameter estimates in a linear least squares ridge regression.

Usage

linear(x, y, eleg=TRUE, lambda=0)

Arguments

x

n*d data matrix; the matrix of the values of the explanatory variables

y

n vector; the values of the response variable

eleg

TRUE or FALSE; an internal parameter related to the method of calculation

lambda

nonnegative real number; the degree of penalization in ridge regression; if lambda=0, then the usual linear least squares estimates are calculated

Value

beta0 is the estimate of the intercept and beta1 is the vector containing the estimates of the coefficients

See Also

linear.quan,

Examples

set.seed(1)
n<-100
d<-2 
x<-8*matrix(runif(n*d),n,d)-3
C<-(2*pi)^(-d/2)
phi<-function(x){ return( C*exp(-sum(x^2)/2) ) }
D<-3; c1<-c(0,0); c2<-D*c(1,0); c3<-D*c(1/2,sqrt(3)/2)
func<-function(x){phi(x-c1)+phi(x-c2)+phi(x-c3)}
y<-matrix(0,n,1)
for (i in 1:n) y[i]<-func(x[i,])+0.01*rnorm(1)

linear(x,y)