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.
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.
grid_type
character. Determines the functions to apply to each variable. The defaults can be overridden by defining individual variables explicitly in …, or by supplying a function to one of the FUN_* arguments.
"mean_or_mode": Character, factor, logical, and binary variables are set to their modes. Numeric, integer, and other variables are set to their means.
"balanced": Each unique level of character, factor, logical, and binary variables are preserved. Numeric, integer, and other variables are set to their means. Warning: When there are many variables and many levels per variable, a balanced grid can be very large. In those cases, it is better to use grid_type=“mean_or_mode” and to specify the unique levels of a subset of named variables explicitly.
"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.
response
Logical should the response variable be included in the grid, even if it is not specified explicitly.
FUN_character
the function to be applied to character variables.
FUN_factor
the function to be applied to factor variables. This only applies if the variable in the original data is a factor. For variables converted to factor in a model-fitting formula, for example, FUN_character is used.
FUN_logical
the function to be applied to logical variables.
FUN_numeric
the function to be applied to numeric variables.
FUN_integer
the function to be applied to integer variables.
FUN_binary
the function to be applied to binary variables.
FUN_other
the function to be applied to other variable types.
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. The same behavior will occur when the value supplied to newdata= is a function call which starts with "datagrid". This is intended to allow users to create convenience shortcuts like:
library(marginaleffects)
mod <- lm(mpg ~ am + vs + factor(cyl) + hp, mtcars)
datagrid_bal <- function(...) datagrid(..., grid_type = "balanced")
predictions(model, newdata = datagrid_bal(cyl = 4))
If users supply a model, the data used to fit that model is retrieved using the insight::get_data function.
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.
Examples
library(marginaleffects)# 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.framemod<-lm(mpg~hp, mtcars)datagrid(model =mod, hp =c(100, 110))
hp rowid
1 100 1
2 110 2
# 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 hp Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
hp 100 -0.0682 0.0101 -6.74 <0.001 35.9 -0.0881 -0.0484
hp 110 -0.0682 0.0101 -6.74 <0.001 35.9 -0.0881 -0.0484
Columns: rowid, term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, hp, predicted_lo, predicted_hi, predicted, mpg
Type: response
Term Contrast hp Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
hp +1 52 -0.0682 0.0101 -6.74 <0.001 35.9 -0.0881 -0.0484
hp +1 96 -0.0682 0.0101 -6.74 <0.001 35.9 -0.0881 -0.0484
hp +1 123 -0.0682 0.0101 -6.74 <0.001 35.9 -0.0881 -0.0484
hp +1 180 -0.0682 0.0101 -6.74 <0.001 35.9 -0.0881 -0.0484
hp +1 335 -0.0682 0.0101 -6.74 <0.001 35.9 -0.0881 -0.0484
Columns: rowid, term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, hp, predicted_lo, predicted_hi, predicted, mpg
Type: response
# 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.framemod<-lm(mpg~hp, mtcars)dg<-datagrid(model =mod, hp =c(100, 110), grid_type ="counterfactual")nrow(dg)