larmarange
commented
1 year ago Just added basic support of pairwise contrasts for zeroinfl() models.
Still unsure if this is the correct approach. Right now, we use mode = "count" and mode = "zero" for each component. See https://cran.r-project.org/web/packages/emmeans/vignettes/models.html#C
Would the same approach be relevant for beta regression with mode = "response" and mode = "precision".
I'm not sufficiently familiar with these types of models.
A more conservative approach would be to not support pairwise contrast for such models (and throw an error), and letting users to call themselves emmeans. Support could be added later, once we have feedbacks about what should be expected.
@ddsjoberg What do you think?
library(pscl)
#> Classes and Methods for R developed in the
#> Political Science Computational Laboratory
#> Department of Political Science
#> Stanford University
#> Simon Jackman
#> hurdle and zeroinfl functions by Achim Zeileis
library(broom.helpers)
mod <- zeroinfl(art ~ fem + mar + phd | fem, data = bioChemists)
mod |>
tidy_plus_plus(add_pairwise_contrasts = TRUE, keep_model_terms = TRUE) |>
knitr::kable()
#> ℹ <zeroinfl> model detected.
#> ✔ `tidy_zeroinfl()` used instead.
#> ℹ Add `tidy_fun = broom.helpers::tidy_zeroinfl` to quiet these messages.| component | term | original_term | variable | var_label | var_class | var_type | var_nlevels | contrasts | contrasts_type | reference_row | label | n_obs | estimate | std.error | conf.level | conf.low | conf.high | statistic | df.error | p.value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| conditional | femMen | NA | fem | fem | factor | dichotomous | 2 | contr.treatment | treatment | TRUE | Men | 494 | 0.0000000 | NA | NA | NA | NA | NA | NA | NA |
| conditional | femWomen | count_femWomen | fem | fem | factor | dichotomous | 2 | contr.treatment | treatment | FALSE | Women | 421 | -0.2321456 | 0.0638427 | 0.95 | -0.3572750 | -0.1070162 | -3.6362116 | Inf | 0.0002767 |
| zero_inflated | femMen | NA | fem | fem | factor | dichotomous | 2 | contr.treatment | treatment | TRUE | Men | 494 | 0.0000000 | NA | NA | NA | NA | NA | NA | NA |
| zero_inflated | femWomen | zero_femWomen | fem | fem | factor | dichotomous | 2 | contr.treatment | treatment | FALSE | Women | 421 | 0.0219932 | 0.2429610 | 0.95 | -0.4542016 | 0.4981881 | 0.0905216 | Inf | 0.9278727 |
| NA | Women - Men | NA | fem | fem | factor | dichotomous | 2 | pairwise | pairwise | NA | Women - Men | NA | -0.3910931 | 0.1044908 | NA | -0.5958913 | -0.1862949 | -3.7428481 | NA | 0.0001819 |
| conditional | marSingle | NA | mar | mar | factor | dichotomous | 2 | contr.treatment | treatment | TRUE | Single | 309 | 0.0000000 | NA | NA | NA | NA | NA | NA | NA |
| conditional | marMarried | count_marMarried | mar | mar | factor | dichotomous | 2 | contr.treatment | treatment | FALSE | Married | 606 | 0.0349971 | 0.0619596 | 0.95 | -0.0864415 | 0.1564358 | 0.5648372 | Inf | 0.5721845 |
| NA | Married - Single | NA | mar | mar | factor | dichotomous | 2 | pairwise | pairwise | NA | Married - Single | NA | 0.0581360 | 0.1023191 | NA | -0.1424058 | 0.2586778 | 0.5681832 | NA | 0.5699106 |
| conditional | phd | count_phd | phd | phd | numeric | continuous | 2 | NA | NA | NA | phd | 915 | 0.0647841 | 0.0285166 | 0.95 | 0.0088925 | 0.1206756 | 2.2718005 | Inf | 0.0230986 |
Created on 2023-08-06 with reprex v2.0.2
ddsjoberg
codecov[bot]
yuryzablotski
fix #232 fix #234
betareg()