datagrid: Generate a data grid of "typical," "counterfactual," or user-specified values for use in the `newdata` argument of the `marginaleffects` or `predictions` functions.

Description

Generate a data grid of "typical," "counterfactual," or user-specified values for use in the newdata argument of the marginaleffects or predictions functions.

Usage

datagrid(
  ...,
  model = NULL,
  newdata = NULL,
  grid_type = "typical",
  FUN_character = Mode,
  FUN_factor = Mode,
  FUN_logical = Mode,
  FUN_numeric = function(x) mean(x, na.rm = TRUE),
  FUN_integer = function(x) round(mean(x, na.rm = TRUE)),
  FUN_other = function(x) mean(x, na.rm = TRUE)
)

Value

A data.frame in which each row corresponds to one combination of the named predictors supplied by the user via the ... dots. Variables which are not explicitly defined are held at their mean or mode.

Arguments

...

named arguments with vectors of values or functions for user-specified variables.

Functions are applied to the variable in the model dataset or newdata, and must return a vector of the appropriate type.
Character vectors are automatically transformed to factors if necessary. +The output will include all combinations of these variables (see Examples below.)

model

Model object

newdata

data.frame (one and only one of the model and newdata arguments

grid_type

character

"typical": variables whose values are not explicitly specified by the user in ... are set to their mean or mode, or to the output of the functions supplied to FUN_type arguments.
"counterfactual": the entire dataset is duplicated for each combination of the variable values specified in .... Variables not explicitly supplied to datagrid() are set to their observed values in the original dataset.

FUN_character

the function to be applied to character variables.

FUN_factor

the function to be applied to factor variables.

FUN_logical

the function to be applied to factor variables.

FUN_numeric

the function to be applied to numeric variables.

FUN_integer

the function to be applied to integer variables.

FUN_other

the function to be applied to other variable types.

Details

If datagrid is used in a marginaleffects or predictions call as the newdata argument, the model is automatically inserted in the function call, and users do not need to specify either the model or newdata arguments. Note that only the variables used to fit the models will be attached to the results. If a user wants to attach other variables as well (e.g., weights or grouping variables), they can supply a data.frame explicitly to the newdata argument inside datagrid().

If users supply a model, the data used to fit that model is retrieved using the insight::get_data function.

Examples

Run this code

# The output only has 2 rows, and all the variables except `hp` are at their
# mean or mode.
datagrid(newdata = mtcars, hp = c(100, 110))

# We get the same result by feeding a model instead of a data.frame
mod <- lm(mpg ~ hp, mtcars)
datagrid(model = mod, hp = c(100, 110))

# Use in `marginaleffects` to compute "Typical Marginal Effects". When used
# in `marginaleffects()` or `predictions()` we do not need to specify the
#`model` or `newdata` arguments.
marginaleffects(mod, newdata = datagrid(hp = c(100, 110)))

# datagrid accepts functions
datagrid(hp = range, cyl = unique, newdata = mtcars)
comparisons(mod, newdata = datagrid(hp = fivenum))

# The full dataset is duplicated with each observation given counterfactual
# values of 100 and 110 for the `hp` variable. The original `mtcars` includes
# 32 rows, so the resulting dataset includes 64 rows.
dg <- datagrid(newdata = mtcars, hp = c(100, 110), grid_type = "counterfactual")
nrow(dg)

# We get the same result by feeding a model instead of a data.frame
mod <- lm(mpg ~ hp, mtcars)
dg <- datagrid(model = mod, hp = c(100, 110), grid_type = "counterfactual")
nrow(dg)