f1st: Stepwise selection of covariates In gausscov: The Gaussian Covariate Method for Variable Selection

Description

Stepwise selection of covariates

Usage

 1 f1st(y,x,p0=0.01,nu=1,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 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 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

 1 2 3 data(boston) bostint<-fgeninter(,1:13],2)[] a<-f1st(,14],bostint,kmn=10,sub=TRUE)

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