Mostly for internal use, but can be useful because the output is consistent across model classes.
get_vcov(model, ...)# S3 method for default
get_vcov(model, vcov = NULL, ...)
# S3 method for MCMCglmm
get_vcov(model, vcov = NULL, ...)
# S3 method for afex_aov
get_vcov(model, vcov = NULL, ...)
# S3 method for glimML
get_vcov(model, vcov = NULL, ...)
# S3 method for biglm
get_vcov(model, vcov = NULL, ...)
# S3 method for brmsfit
get_vcov(model, vcov = NULL, ...)
# S3 method for bart
get_vcov(model, vcov = NULL, ...)
# S3 method for gamlss
get_vcov(model, ...)
# S3 method for glmmTMB
get_vcov(model, ...)
# S3 method for mhurdle
get_vcov(model, vcov = NULL, ...)
# S3 method for Learner
get_vcov(model, ...)
# S3 method for orm
get_vcov(model, vcov = NULL, ...)
# S3 method for stpm2
get_vcov(model, ...)
# S3 method for pstpm2
get_vcov(model, ...)
# S3 method for gsm
get_vcov(model, ...)
# S3 method for aft
get_vcov(model, ...)
# S3 method for scam
get_vcov(model, vcov = NULL, ...)
# S3 method for systemfit
get_vcov(model, ...)
# S3 method for model_fit
get_vcov(model, type = NULL, ...)
# S3 method for workflow
get_vcov(model, type = NULL, ...)
A named square matrix of variance and covariances. The names must match the coefficient names.
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
?slopes 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))
string indicates the type (scale) of the predictions used to
compute contrasts or slopes. This can differ based on the model
type, but will typically be a string such as: "response", "link", "probs",
or "zero". When an unsupported string is entered, the model-specific list of
acceptable values is returned in an error message. When type is NULL, the
first entry in the error message is used by default.