stepwise: Stepwise regression

In chemometrics: Multivariate Statistical Analysis in Chemometrics

Stepwise regression

Description

Stepwise regression, starting from the empty model, with scope to the full model

Usage

stepwise(formula, data, k, startM, maxTime = 1800, direction = "both", 
writeFile = FALSE, maxsteps = 500, ...)

Arguments

formula	formula, like y~X, i.e., dependent~response variables
data	data frame to be analyzed
k	sensible values are log(nrow(x)) for BIC or 2 for AIC; if not provided -> BIC
startM	optional, the starting model; provide a binary vector
maxTime	maximal time to be used for algorithm
direction	either "forward" or "backward" or "both"
writeFile	if TRUE results are shown on the screen
maxsteps	maximum number of steps
...	additional plot arguments

Details

This function is similar to the function step for stepwise regression. It is especially designed for cases where the number of regressor variables is much higher than the number of objects. The formula for the full model (scope) is automatically generated.

Value

usedTime	time that has been used for algorithm
bic	BIC values for different models
models	matrix with no. of models rows and no. of variables columns, and 0/1 entries defining the models

Author(s)

Leonhard Seyfang and (marginally) Peter Filzmoser <[e-mail]>

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

step

Examples

data(NIR)
X <- NIR$xNIR[1:30,]      # first 30 observations - for illustration
y <- NIR$yGlcEtOH[1:30,1] # only variable Glucose
NIR.Glc <- data.frame(X=X, y=y)
res <- stepwise(y~.,data=NIR.Glc,maxsteps=2)   

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.