f3sti: Selection of covariates with given excluded covariates

In gausscov: The Gaussian Covariate Method for Variable Selection

Selection of covariates with given excluded covariates

Description

Selection of covariates with given excluded covariates

Usage

f3sti(y,x,covch,ind,m,p0=0.01,kmn=0,kmx=0, kex=0,mx=21,sub=T,inr=F,xinr=F,qq=-1,kexmx=100)

Arguments

y	Dependent variable
x	Covariates
covch	Sum of squared residuals and selected covariates
ind	The excluded covariates
m	Number of iterations
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.
kex	The excluded covariates
mx	The maximum number covariates for an all subset search
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=-1 the number of covariates of x is used.
kexmx	The maximum number of covariates in an approximation.

Value

ind1 The excluded covariates

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

Examples

data(leukemia)
covch=c(2.023725,1182,1219,2888,0)
covch<-matrix(covch,nrow=1,ncol=5)
ind<-c(1182,1219,2888)
ind<-matrix(ind,nrow=3,ncol=1)
m<-1
a<-f3sti(leukemia[[1]],leukemia[[2]],covch,ind,m,kexmx=5)

gausscov documentation built on June 11, 2025, 5:09 p.m.