This function controls the text which is printed to the console when one of the core marginalefffects functions is called and the object is returned: predictions(), comparisons(), slopes(), hypotheses(), avg_predictions(), avg_comparisons(), avg_slopes().
All of those functions return standard data frames. Columns can be extracted by name, predictions(model)$estimate, and all the usual data manipulation functions work out-of-the-box: colnames(), head(), subset(), dplyr::filter(), dplyr::arrange(), etc.
Some of the data columns are not printed by default. You can disable pretty printing and print the full results as a standard data frame using the style argument or by applying as.data.frame() on the object. See examples below.
Usage
# S3 method for class 'marginaleffects'
print(
x,
style = getOption("marginaleffects_print_style", default = "summary"),
digits = getOption("marginaleffects_print_digits", default = 3),
p_eps = getOption("marginaleffects_print_p_eps", default = 0.001),
topn = getOption("marginaleffects_print_topn", default = 5),
nrows = getOption("marginaleffects_print_nrows", default = 30),
ncols = getOption("marginaleffects_print_ncols", default = 30),
type = getOption("marginaleffects_print_type", default = TRUE),
column_names = getOption("marginaleffects_print_column_names", default = FALSE),
...
)Arguments
- x
An object produced by one of the
marginaleffectspackage functions.- style
"summary", "data.frame", or "tinytable"
- digits
The number of digits to display.
- p_eps
p values smaller than this number are printed in "<0.001" style.
- topn
The number of rows to be printed from the beginning and end of tables with more than
nrowsrows.- nrows
The number of rows which will be printed before truncation.
- ncols
The maximum number of column names to display at the bottom of the printed output.
- type
boolean: should the type be printed?
- column_names
boolean: should the column names be printed?
- ...
Other arguments are currently ignored.
Examples
library(marginaleffects)
mod <- lm(mpg ~ hp + am + factor(gear), data = mtcars)
p <- predictions(mod, by = c("am", "gear"))
p
#>
#> am gear Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> 0 3 16.1 0.759 21.2 <0.001 329.6 14.6 17.6
#> 0 4 21.0 1.470 14.3 <0.001 152.1 18.2 23.9
#> 1 4 26.3 1.039 25.3 <0.001 466.1 24.2 28.3
#> 1 5 21.4 1.315 16.3 <0.001 195.2 18.8 24.0
#>
#> Type: response
#>
subset(p, am == 1)
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> 26.3 1.04 25.3 <0.001 466.1 24.2 28.3
#> 21.4 1.31 16.3 <0.001 195.2 18.8 24.0
#>
#>
print(p, style = "data.frame")
#> am gear estimate std.error statistic p.value s.value conf.low
#> 1 0 3 16.10667 0.7589789 21.22150 6.047015e-100 329.5966 14.61910
#> 2 0 4 21.05000 1.4697592 14.32207 1.592966e-46 152.1370 18.16932
#> 3 1 4 26.27500 1.0392746 25.28206 5.032257e-141 466.0607 24.23806
#> 4 1 5 21.38000 1.3145900 16.26363 1.788354e-59 195.1551 18.80345
#> conf.high df
#> 1 17.59424 Inf
#> 2 23.93068 Inf
#> 3 28.31194 Inf
#> 4 23.95655 Inf
data.frame(p)
#> am gear estimate std.error statistic p.value s.value conf.low
#> 1 0 3 16.10667 0.7589789 21.22150 6.047015e-100 329.5966 14.61910
#> 2 0 4 21.05000 1.4697592 14.32207 1.592966e-46 152.1370 18.16932
#> 3 1 4 26.27500 1.0392746 25.28206 5.032257e-141 466.0607 24.23806
#> 4 1 5 21.38000 1.3145900 16.26363 1.788354e-59 195.1551 18.80345
#> conf.high df
#> 1 17.59424 Inf
#> 2 23.93068 Inf
#> 3 28.31194 Inf
#> 4 23.95655 Inf