45  Tables

45.1 tinytable

By default, marginaleffects will print results to the console. However, it is easy to print in many other formats using the tinytable package, which supports a variety of output formats, including HTML, LaTeX, markdown, Word, and Typst.

https://vincentarelbundock.github.io/tinytable/

For example,

library(marginaleffects)
mod <- glm(am ~ wt + drat, family = binomial, data = mtcars)
mfx <- avg_slopes(mod)
tab <- print(mfx, "tinytable")
class(tab)
#> [1] "tinytable"
#> attr(,"package")
#> [1] "tinytable"

We can then style this table using tinytable’s style_tt() function, add grouping columns or rows with group_tt(), format the content with format_tt(), etc. We can also save the table to file using save_tt(). For instance, to save to a Word document:

save_tt(tab, "/path/to/table.docx")

45.2 modelsummary

We can summarize the results of the comparisons() or slopes() functions using the modelsummary package.

library(modelsummary)
library(marginaleffects)

mod <- glm(am ~ wt + drat, family = binomial, data = mtcars)
mfx <- avg_slopes(mod)

modelsummary(mfx)
(1)
drat 0.278
(0.168)
wt -0.217
(0.080)
Num.Obs. 32
AIC 22.0
BIC 26.4
Log.Lik. -8.011
F 3.430
RMSE 0.28

The same results can be visualized with modelplot():

When using the avg_comparisons() function (or the avg_slopes() function with categorical variables), the output will include two columns to uniquely identify the quantities of interest: term and contrast.

dat <- mtcars
dat$gear <- as.factor(dat$gear)
mod <- glm(vs ~ gear + mpg, data = dat, family = binomial)

cmp <- avg_comparisons(mod)
get_estimates(cmp)
#> # A tibble: 3 × 9
#>   term  contrast estimate std.error statistic    p.value s.value conf.low conf.high
#>   <chr> <chr>       <dbl>     <dbl>     <dbl>      <dbl>   <dbl>    <dbl>     <dbl>
#> 1 gear  4 - 3      0.0372    0.137      0.272 0.785        0.348  -0.230     0.305 
#> 2 gear  5 - 3     -0.340     0.0988    -3.44  0.000588    10.7    -0.533    -0.146 
#> 3 mpg   +1         0.0609    0.0128     4.78  0.00000178  19.1     0.0359    0.0859

We can use the shape argument of the modelsummary function to structure the table properly:

modelsummary(cmp, shape = term + contrast ~ model)
(1)
gear 4 - 3 0.037
(0.137)
5 - 3 -0.340
(0.099)
mpg +1 0.061
(0.013)
Num.Obs. 32
AIC 26.2
BIC 32.1
Log.Lik. -9.101
F 2.389
RMSE 0.31

Cross-contrasts can be a bit trickier, since there are multiple simultaneous groups. Consider this example:

mod <- lm(mpg ~ factor(cyl) + factor(gear), data = mtcars)
cmp <- avg_comparisons(
  mod,
  variables = c("gear", "cyl"),
  cross = TRUE)
get_estimates(cmp)
#> # A tibble: 4 × 10
#>   term  contrast_cyl contrast_gear estimate std.error statistic p.value s.value conf.low conf.high
#>   <chr> <chr>        <chr>            <dbl>     <dbl>     <dbl>   <dbl>   <dbl>    <dbl>     <dbl>
#> 1 cross 6 - 4        4 - 3            -5.33      2.77     -1.93 0.0542     4.21    -10.8  0.0953  
#> 2 cross 6 - 4        5 - 3            -5.16      2.63     -1.96 0.0500     4.32    -10.3  0.000166
#> 3 cross 8 - 4        4 - 3            -9.22      3.62     -2.55 0.0108     6.53    -16.3 -2.13    
#> 4 cross 8 - 4        5 - 3            -9.04      3.19     -2.84 0.00453    7.79    -15.3 -2.80

As we can see above, there are two relevant grouping columns: contrast_gear and contrast_cyl. We can simply plug those names in the shape argument:

modelsummary(
  cmp,
  shape = contrast_gear + contrast_cyl ~ model)
gear cyl (1)
4 - 3 6 - 4 -5.332
(2.769)
8 - 4 -9.218
(3.618)
5 - 3 6 - 4 -5.156
(2.631)
8 - 4 -9.042
(3.185)
Num.Obs. 32
R2 0.740
R2 Adj. 0.701
AIC 173.7
BIC 182.5
Log.Lik. -80.838
F 19.190
RMSE 3.03