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

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