[Rd] Factors

[Peter Dalgaard]

I don’t think these are actually opposite camps, just two related but different issues. Sure, functions can be nonlinear, but we shouldn’t create arbitrary nonlinearities due to the selection of x values.

What I was thinking of was measurements at baseline and 3,6,9,12,18,24 mo into treatment (as in ISwR::alkfos). Fitting a linear trend with the factor codes will de facto assume a kink at 12 months. The jump from time 0 to 3mo may well be deviate from a linear trend (AFAIR, it does), but that is another issue. 

- Peter

> On 25 Oct 2025, at 17.15, Therneau, Terry M., Ph.D. <therneau using mayo.edu> wrote:
> 
> 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.