Method to raise model-specific warnings and errors
# S3 method for betareg
sanity_model_specific(model, ...)sanity_model_specific(model, calling_function = "marginaleffects", ...)
# S3 method for default
sanity_model_specific(
model,
vcov = NULL,
calling_function = "marginaleffects",
...
)
# S3 method for brmsfit
sanity_model_specific(model, ...)
# S3 method for glmmTMB
sanity_model_specific(
model,
vcov = NULL,
calling_function = "marginaleffects",
...
)
# S3 method for mblogit
sanity_model_specific(model, calling_function = "marginaleffects", ...)
# S3 method for mlogit
sanity_model_specific(model, newdata, ...)
# S3 method for clm
sanity_model_specific(model, ...)
# S3 method for plm
sanity_model_specific(model, ...)
# S3 method for plm
sanity_model_specific(model, ...)
# S3 method for rqs
sanity_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))
NULL, data frame, string, or datagrid() call. Determines the predictor values for which to compute marginal effects.
NULL (default): Unit-level marginal effects for each observed value in the original dataset.
data frame: Unit-level marginal effects for each row of the newdata data frame.
string:
"mean": Marginal Effects at the Mean. Marginal effects when each predictor is held at its mean or mode.
"median": Marginal Effects at the Median. Marginal effects 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).
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.