print.marginaleffects | R Documentation |
marginaleffects
objectsThis 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.
## 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 = TRUE),
...
)
x |
An object produced by one of the |
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 |
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. |
library(marginaleffects)
mod <- lm(mpg ~ hp + am + factor(gear), data = mtcars)
p <- predictions(mod, by = c("am", "gear"))
p
subset(p, am == 1)
print(p, style = "data.frame")
data.frame(p)
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