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,kexmx=100,p0=0.01,nu=1,kmn=0,kmx=0,

 mx=21,lm=1000,kex=0,sub=T,inr=T,xinr=F,qq=0,lm0=0)

Arguments

y	Dependent variable
x	Covariates
covch	Sum of squared residuals and selected covariates
ind	The excluded covariates
m	Number of iterations
kexmx	The maximum number of covariates in an approximation.
p0	The P-value cut-off
nu	The order statistic of Gaussian covariates used for comparison
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.
lm0	The current number of approximations

Value

ind1 The excluded covariates

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

lm0 The current number of approximations.

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)

gausscov documentation built on April 26, 2022, 5:07 p.m.