Peter wrote:
A related issue that has bugged me "forever" is that we treat ordered factors
as if their levels are equidistant even when that is patently untrue. The use
of polynomial contrasts for ordered factors reflects this - it would really be
more sensible to use e.g. successive differences or (ick!) Helmert contrasts
for the ordered case and reserve poly() for factors with actual numerical
levels. To me, this effectively makes ordered factors conceptually useless.
I actually fall into the opposite camp. We teach students that they *must*
code factors as having non-equal increments but that continuous variables are
okay s is (the old interval vs. ordinal dichotomy). In my medical work, a
lot of the categorical variables actually are close to evenly spaced, a disease
grade for instance, since that is what the original authors of the scale were
trying to do. It is the continuous variables that violate equal spacing most
violently. A cardiac ejection fraction drop from 70 to 60 is "meh", a drop
from 30 to 20 is "I hope your affairs are in order". We don't check this
nearly often enough.
Also, I use as.integer(factor) quite when creating a an analysis data set from
input data. It's just another tool for creating new variables.
Terry T.
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