glance.mfx: Glance at a(n) mfx object

In broom: Convert Statistical Objects into Tidy Tibbles

Glance at a(n) mfx object

Description

Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

Usage

## S3 method for class 'mfx'
glance(x, ...)

## S3 method for class 'logitmfx'
glance(x, ...)

## S3 method for class 'negbinmfx'
glance(x, ...)

## S3 method for class 'poissonmfx'
glance(x, ...)

## S3 method for class 'probitmfx'
glance(x, ...)

Arguments

x

A logitmfx, negbinmfx, poissonmfx, or probitmfx object. (Note that betamfx objects receive their own set of tidiers.)

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are: tidy() methods will warn when supplied an exponentiate argument if it will be ignored. augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

This generic glance method wraps glance.glm() for applicable objects from the mfx package.

Value

A tibble::tibble() with exactly one row and columns:

AIC	Akaike's Information Criterion for the model.
BIC	Bayesian Information Criterion for the model.
deviance	Deviance of the model.
df.null	Degrees of freedom used by the null model.
df.residual	Residual degrees of freedom.
logLik	The log-likelihood of the model. [stats::logLik()] may be a useful reference.
nobs	Number of observations used.
null.deviance	Deviance of the null model.

See Also

glance.glm(), mfx::logitmfx(), mfx::negbinmfx(), mfx::poissonmfx(), mfx::probitmfx()

Other mfx tidiers: augment.betamfx(), augment.mfx(), glance.betamfx(), tidy.betamfx(), tidy.mfx()

Examples

# load libraries for models and data
library(mfx)

# get the marginal effects from a logit regression
mod_logmfx <- logitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars)

tidy(mod_logmfx, conf.int = TRUE)

# compare with the naive model coefficients of the same logit call
tidy(
  glm(am ~ cyl + hp + wt, family = binomial, data = mtcars),
  conf.int = TRUE
)

augment(mod_logmfx)
glance(mod_logmfx)

# another example, this time using probit regression
mod_probmfx <- probitmfx(am ~ cyl + hp + wt, atmean = TRUE, data = mtcars)

tidy(mod_probmfx, conf.int = TRUE)
augment(mod_probmfx)
glance(mod_probmfx)

broom documentation built on July 9, 2023, 5:28 p.m.