Thanks - this has been on the back of mind for a while, so glad you brought it up. The hacky solution is to change the factor levels to something like "Feature: Level" rather than "Level" but this should be fixed so you don't have to do that!

This is also a problem when using mm_diffs().

In my case, I wanted to conduct a subgroup analysis conditional on the gender of the respondents (labeled as "Male" or "Female"). The conjoint experiment, however, also contained levels with these labels ("Male" and "Female"). Particularly problematic is that mm_diffs() did not throw an error message, but returned wrong estimates without any warnings.

Here is an artificial example using the immigration data:

Data

data("immigration")

Create subgroups

immigration$ethnosplit <- cut(immigration$ethnocentrism, 2)

Rename subgroup levels

immigration$subgroup <- as.factor(ifelse(as.numeric(immigration$ethnosplit) == 1,
"Male",
"Female"))

Estimate correct MMs by subgroup

mm_correct <- cj(na.omit(immigration),
ChosenImmigrant ~ Gender + Education + LanguageSkills,
estimate = "mm",
id = ~ CaseID,
by = ~ ethnosplit)

plot(mm_correct,
group = "ethnosplit",
vline = 0.5)

Differences between subgroups

mmdiff_correct <- mm_diffs(na.omit(immigration),
ChosenImmigrant ~ Gender + Education + LanguageSkills,
id = ~ CaseID,
by = ~ ethnosplit)

plot(mmdiff_correct)

Using subgroups with identical level names returns wrong estimates

mmdiff_problem <- mm_diffs(na.omit(immigration),
ChosenImmigrant ~ Gender + Education + LanguageSkills,
id = ~ CaseID,
by = ~ subgroup)

plot(mmdiff_problem)

Okay, that's definitely bad but actually a separate issue. @m-jankowski I'm going to move your comment to a new issue to keep track of things: #22

The issue here remains simply that things get wonky when multiple features have the same factor levels.