robustbase (version 0.99-4-1)

splitFrame: Split Continuous and Categorical Predictors

Description

Splits the design matrix into categorical and continuous predictors. Categorical variables are variables that are factors, ordered factors, or character.

Usage

splitFrame(mf, x = model.matrix(mt, mf),
 	   type = c("f","fi", "fii"))

Value

A list that includes the following components:

x1

design matrix containing only categorical variables

x1.idx

logical vectors of the variables considered categorical in the original design matrix

x2

design matrix containing the continuous variables

Arguments

mf

model frame (as returned by model.frame).

x

(optional) design matrix, defaulting to the derived model.matrix.

type

a character string specifying the split type (see details).

Author

Manuel Koller

Details

Which split type is used can be controlled with the setting split.type in lmrob.control.

There are three split types. The only differences between the types are how interactions between categorical and continuous variables are handled. The extra types of splitting can be used to avoid Too many singular resamples errors.

Type "f", the default, assigns only the intercept, categorical and interactions of categorical variables to x1. Interactions of categorical and continuous variables are assigned to x2.

Type "fi" assigns also interactions between categorical and continuous variables to x1.

Type "fii" assigns not only interactions between categorical and continuous variables to x1, but also the (corresponding) continuous variables themselves.

References

Maronna, R. A., and Yohai, V. J. (2000). Robust regression with both continuous and categorical predictors. Journal of Statistical Planning and Inference 89, 197--214.

See Also

lmrob.M.S

Examples

data(education)
education <- within(education, Region <- factor(Region))
educaCh   <- within(education, Region <- as.character(Region))

## no interactions -- same split for all types:
fm1 <- lm(Y ~ Region + X1 + X2 + X3, education)
fmC <- lm(Y ~ Region + X1 + X2 + X3, educaCh  )
splt <- splitFrame(fm1$model) ; str(splt)
splC <- splitFrame(fmC$model)
stopifnot(identical(splt, splC))

## with interactions:
fm2 <- lm(Y ~ Region:X1:X2 + X1*X2, education)
s1 <- splitFrame(fm2$model, type="f"  )
s2 <- splitFrame(fm2$model, type="fi" )
s3 <- splitFrame(fm2$model, type="fii")
cbind(s1$x1.idx,
      s2$x1.idx,
      s3$x1.idx)
rbind(p.x1 = c(ncol(s1$x1), ncol(s2$x1), ncol(s3$x1)),
      p.x2 = c(ncol(s1$x2), ncol(s2$x2), ncol(s3$x2)))