datagrid  R Documentation 
newdata
argument of the marginaleffects
or predictions
functions.Generate a data grid of "typical," "counterfactual," or userspecified values for use in the newdata
argument of the marginaleffects
or predictions
functions.
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) )
... 
named arguments with vectors of values or functions for userspecified variables.

model 
Model object 
newdata 
data.frame (one and only one of the 
grid_type 
character

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. 
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.
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.
Other grid:
datagridcf()
# 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)
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