get_group_names: Get levels of the outcome variable in grouped or multivariate...

get_group_namesR Documentation

Get levels of the outcome variable in grouped or multivariate models

Description

Get levels of the outcome variable in grouped or multivariate models

Usage

get_group_names(model, ...)

## Default S3 method:
get_group_names(model, ...)

## S3 method for class 'polr'
get_group_names(model, ...)

## S3 method for class 'multinom'
get_group_names(model, ...)

## S3 method for class 'bracl'
get_group_names(model, ...)

## S3 method for class 'brmsfit'
get_group_names(model, ...)

## S3 method for class 'mblogit'
get_group_names(model, type, ...)

## S3 method for class 'mlm'
get_group_names(model, ...)

## S3 method for class 'clm'
get_group_names(model, ...)

## S3 method for class 'hurdle'
get_group_names(model, type = "count", ...)

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.

type

string indicates the type (scale) of the predictions used to compute marginal effects or contrasts. 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 default value is used. This default is the first model-related row in the marginaleffects:::type_dictionary dataframe.

Value

A character vector


marginaleffects documentation built on Nov. 24, 2022, 1:06 a.m.