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Get a named variance-covariance matrix from a model object (internal function)

Usage

get_vcov(model, ...)

# S3 method for default
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 gamlss
get_vcov(model, ...)

# S3 method for mhurdle
get_vcov(model, ...)

# S3 method for scam
get_vcov(model, vcov = NULL, ...)

Arguments

model

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.

vcov

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))

Value

A named square matrix of variance and covariances. The names must match the coefficient names.