Get a named variance-covariance matrix from a model object

Description

Mostly for internal use, but can be useful because the output is consistent across model classes.

Usage

get_vcov(model, ...)

## Default S3 method:
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'MCMCglmm'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'afex_aov'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'glimML'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'biglm'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'brmsfit'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'bart'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'gamlss'
get_vcov(model, ...)

## S3 method for class 'glmmTMB'
get_vcov(model, ...)

## S3 method for class 'mhurdle'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'Learner'
get_vcov(model, ...)

## S3 method for class 'orm'
get_vcov(model, vcov = NULL, ...)

## S3 method for class 'stpm2'
get_vcov(model, ...)

## S3 method for class 'pstpm2'
get_vcov(model, ...)

## S3 method for class 'gsm'
get_vcov(model, ...)

## S3 method for class 'aft'
get_vcov(model, ...)

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

## S3 method for class 'systemfit'
get_vcov(model, ...)

## S3 method for class 'model_fit'
get_vcov(model, type = NULL, ...)

## S3 method for class 'workflow'
get_vcov(model, type = 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 ?slopes 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))
type 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.

Value

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