smooth (version 1.6.3)

stepwise:

Description

Function selects variables that give linear regression with the lowest information criteria. The selection is done stepwise (forward) based on partial correlations. This should be a simpler and faster implementation than step() function from `stats' package.

Usage

stepwise(data, ic=c("AIC","AICc","BIC"), silent=TRUE, df=NULL)

Arguments

data

Data frame containing dependant variable in the first column and the others in the rest.

ic

Information criterion to use.

silent

If silent=FALSE, then nothing is silent, everything is printed out. silent=TRUE means that nothing is produced.

df

Number of degrees of freedom to add (should be used if stepwise is used on residuals).

Value

Function returns model - the final model of the class "lm".

Details

The algorithm uses lm() to fit different models and cor() to select the next regressor in the sequence.

Examples

xreg <- cbind(rnorm(100,10,3),rnorm(100,50,5))
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(100,0,3),xreg)

stepwise(xreg)