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...roblem, which would become more > noticeable if logicals were treated as factors. > >> d <- data.frame(Age=c(2,4,6,8,10), Weight=c(878, 890, 930, 800, 750), > Diseased=c(FALSE,FALSE,FALSE,TRUE,TRUE)) >> coef(lm(data=d, Weight ~ Age + Diseased)) > (Intercept) Age DiseasedTRUE > 877.7333 5.4000 -151.3333 >> coef(lm(data=d, Weight ~ Age + factor(Diseased))) > (Intercept) Age factor(Diseased)TRUE > 877.7333 5.4000 -151.3333 >> coef(lm(data=d, Weight ~ Age + Diseased, subset=Age&l...

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values