f1st: Stepwise selection of covariates

In gausscov: The Gaussian Covariate Method for Variable Selection

Stepwise selection of covariates

Description

Stepwise selection of covariates

Usage

f1st(y,x,p0=0.01,kmn=0,kmx=0,mx=21,kex=0,sub=T,inr=T,xinr=F,qq=0)

Arguments

y	Dependent variable
x	Covariates
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
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

pv In order the included covariates, the regression coefficient values, the Gaussian P-values, the standard P-values and the proportional reduction in the sum of squared residuals due to this covariate

res The residuals

stpv The in order stepwise P-values, sum of squared residuals and the proportional reduction in the sum of squared residuals due to this covariate.

Examples

data(boston)
bostint<-fgeninter(boston[,1:13],2)[[1]]
a<-f1st(boston[,14],bostint,kmn=10,sub=TRUE)

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