Comments (3)
larmarange
commented on July 19, 2024
The purpose of tidy_add_variable_labels() is and only is to add the appropriate variable labels.
If it was not called before in the pipeline, tidy_add_variable_labels() calls tidy_identify_variables() whose purpose is to identify the corresponding variable of each term.
Regarding extracting the factor level, it is the purpose of tidy_add_term_labels().
You also have tidy_add_reference_rows() to add a row with the reference level.
In broom.helpers, each single operation is done by a different function, and all these functions can be combined in a pipeline.
Most users do not need these individual functions and would rather use tidy_plus_plus() which combines all features of broom.helpers in one function (see documentation for the full list of arguments).
See examples below
I'm closing the issue as the feature you refer to is already implemented. But feel free to reopen if you identify something missing.
library(broom.helpers)
mtcars$f_cyl <- factor(mtcars$cyl)
m <- lm(mpg ~ f_cyl, mtcars)
tidy_and_attach(m) |>
tidy_add_variable_labels()
#> # A tibble: 3 × 12
#> term variable var_label var_class var_type var_nlevels estimate std.error
#> <chr> <chr> <chr> <chr> <chr> <int> <dbl> <dbl>
#> 1 (Interce… (Interc… (Interce… <NA> interce… NA 26.7 0.972
#> 2 f_cyl6 f_cyl f_cyl factor categor… 3 -6.92 1.56
#> 3 f_cyl8 f_cyl f_cyl factor categor… 3 -11.6 1.30
#> # ℹ 4 more variables: statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> # conf.high <dbl>
tidy_and_attach(m) |>
tidy_add_term_labels()
#> # A tibble: 3 × 15
#> term variable var_label var_class var_type var_nlevels contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 (Intercept) (Intercept) (Intercept) <NA> intercept NA <NA>
#> 2 f_cyl6 f_cyl f_cyl factor categoric… 3 contr.tr…
#> 3 f_cyl8 f_cyl f_cyl factor categoric… 3 contr.tr…
#> # ℹ 8 more variables: contrasts_type <chr>, label <chr>, estimate <dbl>,
#> # std.error <dbl>, statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> # conf.high <dbl>
tidy_and_attach(m) |>
tidy_add_reference_rows() |>
tidy_add_term_labels()
#> # A tibble: 4 × 16
#> term variable var_label var_class var_type var_nlevels contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 (Intercept) (Intercept) (Intercept) <NA> intercept NA <NA>
#> 2 f_cyl4 f_cyl f_cyl factor categoric… 3 contr.tr…
#> 3 f_cyl6 f_cyl f_cyl factor categoric… 3 contr.tr…
#> 4 f_cyl8 f_cyl f_cyl factor categoric… 3 contr.tr…
#> # ℹ 9 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> # estimate <dbl>, std.error <dbl>, statistic <dbl>, p.value <dbl>,
#> # conf.low <dbl>, conf.high <dbl>
tidy_plus_plus(m)
#> # A tibble: 3 × 17
#> term variable var_label var_class var_type var_nlevels contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 f_cyl4 f_cyl f_cyl factor categorical 3 contr.treatment
#> 2 f_cyl6 f_cyl f_cyl factor categorical 3 contr.treatment
#> 3 f_cyl8 f_cyl f_cyl factor categorical 3 contr.treatment
#> # ℹ 10 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> # n_obs <dbl>, estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> # p.value <dbl>, conf.low <dbl>, conf.high <dbl>Created on 2023-03-31 with reprex v2.0.2
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larmarange
commented on July 19, 2024
Same examples but with full tables visible. The levels are visible in the label column.
library(broom.helpers)
mtcars$f_cyl <- factor(mtcars$cyl)
m <- lm(mpg ~ f_cyl, mtcars)
tidy_and_attach(m) |>
tidy_add_variable_labels() |>
knitr::kable()| term | variable | var_label | var_class | var_type | var_nlevels | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|---|
| (Intercept) | (Intercept) | (Intercept) | NA | intercept | NA | 26.663636 | 0.9718008 | 27.437347 | 0.0000000 | 24.67608 | 28.651192 |
| f_cyl6 | f_cyl | f_cyl | factor | categorical | 3 | -6.920779 | 1.5583482 | -4.441099 | 0.0001195 | -10.10796 | -3.733599 |
| f_cyl8 | f_cyl | f_cyl | factor | categorical | 3 | -11.563636 | 1.2986235 | -8.904533 | 0.0000000 | -14.21962 | -8.907653 |
tidy_and_attach(m) |>
tidy_add_term_labels() |>
knitr::kable()| term | variable | var_label | var_class | var_type | var_nlevels | contrasts | contrasts_type | label | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (Intercept) | (Intercept) | (Intercept) | NA | intercept | NA | NA | NA | (Intercept) | 26.663636 | 0.9718008 | 27.437347 | 0.0000000 | 24.67608 | 28.651192 |
| f_cyl6 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | 6 | -6.920779 | 1.5583482 | -4.441099 | 0.0001195 | -10.10796 | -3.733599 |
| f_cyl8 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | 8 | -11.563636 | 1.2986235 | -8.904533 | 0.0000000 | -14.21962 | -8.907653 |
tidy_and_attach(m) |>
tidy_add_reference_rows() |>
tidy_add_term_labels() |>
knitr::kable()| term | variable | var_label | var_class | var_type | var_nlevels | contrasts | contrasts_type | reference_row | label | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (Intercept) | (Intercept) | (Intercept) | NA | intercept | NA | NA | NA | NA | (Intercept) | 26.663636 | 0.9718008 | 27.437347 | 0.0000000 | 24.67608 | 28.651192 |
| f_cyl4 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | TRUE | 4 | NA | NA | NA | NA | NA | NA |
| f_cyl6 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | FALSE | 6 | -6.920779 | 1.5583482 | -4.441099 | 0.0001195 | -10.10796 | -3.733599 |
| f_cyl8 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | FALSE | 8 | -11.563636 | 1.2986235 | -8.904533 | 0.0000000 | -14.21962 | -8.907653 |
tidy_plus_plus(m) |>
knitr::kable()| term | variable | var_label | var_class | var_type | var_nlevels | contrasts | contrasts_type | reference_row | label | n_obs | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| f_cyl4 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | TRUE | 4 | 11 | 0.000000 | NA | NA | NA | NA | NA |
| f_cyl6 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | FALSE | 6 | 7 | -6.920779 | 1.558348 | -4.441099 | 0.0001195 | -10.10796 | -3.733599 |
| f_cyl8 | f_cyl | f_cyl | factor | categorical | 3 | contr.treatment | treatment | FALSE | 8 | 14 | -11.563636 | 1.298623 | -8.904533 | 0.0000000 | -14.21962 | -8.907653 |
Created on 2023-03-31 with reprex v2.0.2
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aghaynes
commented on July 19, 2024
perfect, thanks!
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Related Issues (20)
- Support for mmrm models HOT 8
- Support for MASS::contr.sdif() contrasts
- Support for zero-inflated models
- beta regression is not supported yet HOT 2
- Considering a `tidy_post_fun` argument to `tidy_plus_plus()` HOT 2
- Support with coxphf model HOT 9
- Bug fixes and improvements for mixed models HOT 1
- tbl_regression (package gtsummary) and ggcoef_model (package ggstats) not working on the output of a replicate svyglm model HOT 2
- fantastic support of multivariate quantile regression for any quantile HOT 1
- Support for survival::cch model? HOT 5
- Avg_comparisons and nlme::lme() models HOT 1
- order of variable levels with marginal tidiers HOT 8
- Release broom.helpers 1.15.0
- Take into account (id) when computing model_get_n() for coxph models
- `marginaleffects::datagridcf()` is deprecated
- Do you know the status of the {margins} pkg? HOT 1
- Explore better support of VGAM::vglm() models
- Execution time HOT 4
- marginaleffects::datagridcf() is deprecated
- pkgdown site has no search facility HOT 1
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