# 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(boston[,14],boston[,1:13],kex=7:8) ```

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