fr1st: Robust stepwise selection of covariates In gausscov: The Gaussian Covariate Method for Variable Selection

Description

Robust stepwise selection of covariates

Usage

 1 fr1st(y,x,cn=1,cnr=c(1,3,5),p0=0.01,sg=0,nu=1,kmx=0,mx=21,kex=0,sub=T,inr=T,xinr=F,red=F)

Arguments

 y Dependent variable x Covariates cn The constant for Huber's psi-function cnr The constants for Hampel's three part redescending psi function p0 The P-value cut-off sg Scale value of residuals nu The order for calculating the P-value kmx The maximum number of included covariates mx The maximum number of included covariates if the option subset =TRUE is used kex The excluded covariates sub Logical, if TRUE best subset selected inr Logical TRUE to include intercept xinr Logical TRUE if intercept already included red Logical If true Hampel's three part redescending psi function

Value

pv In order the subset ind, the regression coefficients, the Gaussian P-values, the standard P-values.

res The residuals

stpv The stepwise regression results: covariate, P-value and scale

Examples

 1 2 data(boston) a<-fr1st(,14],,1:13],kex=7:8)

gausscov documentation built on Jan. 17, 2022, 9:06 a.m.