splitFrame function

Split Continuous and Categorical Predictors

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

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

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).

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.

Returns

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

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.

Author(s)

Manuel Koller

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)))