Data grids — datagrid • marginaleffects

Generate a data grid of user-specified values for use in the newdata argument of the predictions(), comparisons(), and slopes() functions. This is useful to define where in the predictor space we want to evaluate the quantities of interest. Ex: the predicted outcome or slope for a 37 year old college graduate.

datagrid() generates data frames with combinations of "typical" or user-supplied predictor values.
datagridcf() generates "counter-factual" data frames, by replicating the entire dataset once for every combination of predictor values supplied by the user.

Usage

datagrid(
  ...,
  model = NULL,
  newdata = NULL,
  by = NULL,
  FUN_character = get_mode,
  FUN_factor = get_mode,
  FUN_logical = get_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),
  grid_type = "typical"
)

datagridcf(..., model = NULL, newdata = NULL)

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 can be used.)

by

character vector with grouping variables within which FUN_* functions are applied to create "sub-grids" with unspecified variables.

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.

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.

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.

Details

If datagrid is used in a predictions(), comparisons(), or slopes() call as the newdata argument, the model is automatically inserted in the model argument of datagrid() call, and users do not need to specify either the model or newdata arguments.

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

Functions

datagridcf(): Counterfactual data grid

Examples

# 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))
#>        mpg    cyl     disp     drat      wt     qsec     vs      am   gear
#> 1 20.09062 6.1875 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875
#> 2 20.09062 6.1875 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875
#>     carb  hp
#> 1 2.8125 100
#> 2 2.8125 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))
#>        mpg  hp
#> 1 20.09062 100
#> 2 20.09062 110

# Use in `marginaleffects` to compute "Typical Marginal Effects". When used
# in `slopes()` or `predictions()` we do not need to specify the
#`model` or `newdata` arguments.
slopes(mod, newdata = datagrid(hp = c(100, 110)))
#> 
#>  Term Estimate Std. Error     z Pr(>|z|)   2.5 %  97.5 %  hp
#>    hp  -0.0682     0.0101 -6.74   <0.001 -0.0881 -0.0484 100
#>    hp  -0.0682     0.0101 -6.74   <0.001 -0.0881 -0.0484 110
#> 
#> Columns: rowid, term, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, mpg, hp 
#> 

# datagrid accepts functions
datagrid(hp = range, cyl = unique, newdata = mtcars)
#>        mpg     disp     drat      wt     qsec     vs      am   gear   carb  hp
#> 1 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125  52
#> 2 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125  52
#> 3 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125  52
#> 4 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125 335
#> 5 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125 335
#> 6 20.09062 230.7219 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875 2.8125 335
#>   cyl
#> 1   6
#> 2   4
#> 3   8
#> 4   6
#> 5   4
#> 6   8
comparisons(mod, newdata = datagrid(hp = fivenum))
#> 
#>  Term Contrast Estimate Std. Error     z Pr(>|z|)   2.5 %  97.5 %
#>    hp       +1  -0.0682     0.0101 -6.74   <0.001 -0.0881 -0.0484
#>    hp       +1  -0.0682     0.0101 -6.74   <0.001 -0.0881 -0.0484
#>    hp       +1  -0.0682     0.0101 -6.74   <0.001 -0.0881 -0.0484
#>    hp       +1  -0.0682     0.0101 -6.74   <0.001 -0.0881 -0.0484
#>    hp       +1  -0.0682     0.0101 -6.74   <0.001 -0.0881 -0.0484
#> 
#> Columns: rowid, term, contrast, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, mpg, hp 
#> 

# 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)
#> [1] 64

# 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)
#> [1] 64