Open jacob-long opened 6 years ago
Suggestions are welcome. Hopefully soon I'll better document to outsiders how to make a summ
method, but it's been pretty ad hoc to this point.
How about adding the support for packages supporting mixed models with specified residual covariance, like nlme glmmTMB, glmPQL? lme4 cannot do that, which eliminates it from the analysis of longitudinal trials, where the assumptions made by lme4 are totally unrealistic.
I will look into that, @some-r-user. I had in the past looked into nlme
support in particular and can't recall right now why I ended up not going forward with it. I have also gotten a couple of other requests for glmmTMB
and will check into it and glmmPQL
as well. This will depend somewhat on how easy their developers have made it to access useful information from their models.
It might be cool to see support for vglm
objects in VGAM
. I don't know if vgam
objects have a similar enough form (I've never used them).
Hello everybody,
I'm not sure it is the right place to post it. I'm surprised nobody mentionned basicivreg
objects: is this type of object already compatible? Because on my side, I'm not able to use jtools functions with my ivreg objects. Could someone give me an example of how to use jtools Library with such type of objects?
Best, Nagui
It would be great if you could support felm
from library(lfe)
. lfe
supports linear regression with high-dimensional fixed effects, instrumental variables regression and clustering. Having ivreg
support, as mentioned above, would also be nice. Thank you for your work on jtools!
My general philosophy goes like this:
lavaan
, which can be endlessly complicated and used for very different purposes)summ
will not handle other kinds of input, like data.frames or the like.skimr
is a package that does those things well.summ
should be able to offer added value above and beyondsummary
With that said, models I definitely plan to support are:
lme
Still thinking about/auditing:
brmsfit
— worried about variation in output due to wide variety of options, unsure ifsumm
can add benefit since refitting models isn't feasible.stanreg
— less concern about variation than withbrmsfit
, but "added value" concern remainspolr
— Need to look more closely at the interface, make sure I know enough to make a good summary. Need to think about how to plot predictions from these models (same goes forordinal
package models), but that isn't essential.Checklist of models I plan to add barring complications as I implement them (and outside contributors may feel free to do a pull request for one of these):
lme
(others innlme
?)glmmTMB