Linear regression with interaction fails in the rms-package

Question

I'm playing around with interaction in the formula. I wondered if it's possible to do a regression with interaction for one of the two dummy variables. This seems to work in regular linear regression using the lm() function but with the ols() function in the rms package the same formula fails. Anyone know why?

Here's my example

data(mtcars)

mtcars$gear <- factor(mtcars$gear)
regular_lm <- lm(mpg ~ wt + cyl + gear + cyl:gear, data=mtcars)
summary(regular_lm)

regular_lm <- lm(mpg ~ wt + cyl + gear + cyl:I(gear == "4"), data=mtcars)
summary(regular_lm)

And now the rms example

library(rms)

dd <- datadist(mtcars)
options(datadist = "dd")

regular_ols <- ols(mpg ~ wt + cyl + gear + cyl:gear, data=mtcars)
regular_ols

# Fails with:
#     Error in if (!length(fname) || !any(fname == zname)) { : 
#         missing value where TRUE/FALSE needed
regular_ols <- ols(mpg ~ wt + cyl + gear + cyl:I(gear == "4"), data=mtcars)

This experiment might not be the wisest statistic to do as it seems that the estimates change significantly but I'm a little curious to why ols() fails since it should do the "same fitting routines used by lm"

Answer 1

I don't know exactly, but it has to do with the way the formula is evaluated rather than with the way the fit is done once the model has been translated. Using traceback() shows that the problem occurs within Design(eval.parent(m)); using options(error=recover) gets you to the point where you can see that

Browse[1]> fname
[1] "wt"   "cyl"  "gear"
Browse[1]> zname
[1] NA

in other words, zname is some internal variable that hasn't been set right because the Design function can't quite handle defining the interaction between cylinders and the (gear==4) dummy on the fly.

This works though:

mtcars$cylgr <- with(mtcars,interaction(cyl,gear == "4"))
regular_ols <- ols(mpg ~ wt + cyl + gear + cylgr, data=mtcars)