f3st: Stepwise selection of covariates

In gausscov: The Gaussian Covariate Method for Variable Selection

Stepwise selection of covariates

Description

Stepwise selection of covariates

Usage

f3st(y,x,m,kmn=10,p0=0.01,kmx=0,mx=21,lm=100,kex=0,sub=T,inr=T,xinr=F,qq=0,kexmx=100)

Arguments

y	Dependent variable
x	Covariates
m	The number of iterations
kexmx	The maximum number of covariates in an approximation
p0	The P-value cut-off
kmn	The minimum number of included covariates irrespective of cut-off P-value
kmx	The maximum number of included covariates irrespective of cut-off P-value.
mx	The maximum number covariates for an all subset search
lm	The maximum number of approximations.
kex	The excluded covariates
sub	Logical if TRUE best subset selected
inr	Logical if TRUE include intercept if not present
xinr	Logical if TRUE intercept already present
qq	The number of covariates to choose from. If qq=0 the number of covariates of x is used.

Value

covch The sum of squared residuals and the selected covariates ordered in increasing size of sum of squared residuals.

lai The number of rows of covch

Examples

data(leukemia)
a<-f3st(leukemia[,3572],leukemia[,1:3571],m=2,kmn=5,sub=TRUE,kexmx=5)

gausscov documentation built on Oct. 12, 2023, 1:06 a.m.