Method to raise model-specific warnings and errors
# S3 method for glimML
sanitize_model_specific(model, ...)# S3 method for betareg
sanitize_model_specific(model, ...)
sanitize_model_specific(model, ...)
# S3 method for default
sanitize_model_specific(
model,
vcov = NULL,
calling_function = "marginaleffects",
...
)
# S3 method for brmsfit
sanitize_model_specific(model, ...)
# S3 method for bart
sanitize_model_specific(model, ...)
# S3 method for glmmTMB
sanitize_model_specific(
model,
vcov = NULL,
calling_function = "marginaleffects",
...
)
# S3 method for inferences_simulation
sanitize_model_specific(model, vcov = FALSE, ...)
# S3 method for mblogit
sanitize_model_specific(model, calling_function = "marginaleffects", ...)
# S3 method for mlogit
sanitize_model_specific(model, newdata, ...)
# S3 method for clm
sanitize_model_specific(model, ...)
# S3 method for plm
sanitize_model_specific(model, ...)
# S3 method for plm
sanitize_model_specific(model, ...)
# S3 method for rqs
sanitize_model_specific(model, ...)
A warning, an error, or nothing
Model object
Additional arguments are passed to the predict()
method
supplied by the modeling package.These arguments are particularly useful
for mixed-effects or bayesian models (see the online vignettes on the
marginaleffects
website). Available arguments can vary from model to
model, depending on the range of supported arguments by each modeling
package. See the "Model-Specific Arguments" section of the
?marginaleffects
documentation for a non-exhaustive list of available
arguments.
Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values:
FALSE: Do not compute standard errors. This can speed up computation considerably.
TRUE: Unit-level standard errors using the default vcov(model)
variance-covariance matrix.
String which indicates the kind of uncertainty estimates to return.
Heteroskedasticity-consistent: "HC"
, "HC0"
, "HC1"
, "HC2"
, "HC3"
, "HC4"
, "HC4m"
, "HC5"
. See ?sandwich::vcovHC
Heteroskedasticity and autocorrelation consistent: "HAC"
Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger"
Other: "NeweyWest"
, "KernHAC"
, "OPG"
. See the sandwich
package documentation.
One-sided formula which indicates the name of cluster variables (e.g., ~unit_id
). This formula is passed to the cluster
argument of the sandwich::vcovCL
function.
Square covariance matrix
Function which returns a covariance matrix (e.g., stats::vcov(model)
)
Grid of predictor values at which we evaluate the slopes.
Warning: Please avoid modifying your dataset between fitting the model and calling a marginaleffects
function. This can sometimes lead to unexpected results.
NULL
(default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using insight::get_data()
, which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model.
datagrid()
call to specify a custom grid of regressors. For example:
newdata = datagrid(cyl = c(4, 6))
: cyl
variable equal to 4 and 6 and other regressors fixed at their means or modes.
See the Examples section and the datagrid()
documentation.
string:
"mean": Marginal Effects at the Mean. Slopes when each predictor is held at its mean or mode.
"median": Marginal Effects at the Median. Slopes when each predictor is held at its median or mode.
"marginalmeans": Marginal Effects at Marginal Means. See Details section below.
"tukey": Marginal Effects at Tukey's 5 numbers.
"grid": Marginal Effects on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors).