f2st: Repeated stepwise selection of covariates

In gausscov: The Gaussian Covariate Method for Variable Selection

Repeated stepwise selection of covariates

Description

Repeated stepwise selection of covariates

Usage

f2st(y,x,p0=0.01,kmn=0,kmx=0,kex=0,mx=21,lm=9^9,

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.
kex	The excluded covariates
mx	The maximum number of covariates for an all subset search
lm	The maximum number of linear approximations
sub	Logical if TRUE select the best subset
inr	Logical if TRUE include an intercept
xinr	Logical if TRUE intercept already included
qq	The number of covariates to choose from. If qq=0 the number of covariates of x is used.

Value

pv In order the linear approximation, 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.

Examples

data(boston)
bostint<-fgeninter(boston[,1:13],2)[[1]]
a<-f2st(boston[,14],bostint,lm=3)

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