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.

# S3 method for mfx glance(x, ...) # S3 method for logitmfx glance(x, ...) # S3 method for negbinmfx glance(x, ...) # S3 method for poissonmfx glance(x, ...) # S3 method for probitmfx glance(x, ...)

x | A |
---|---|

... | Additional arguments. Not used. Needed to match generic
signature only. |

This generic glance method wraps `glance.glm()`

for applicable
objects from the `mfx`

package.

`glance.glm()`

, `mfx::logitmfx()`

, `mfx::negbinmfx()`

,
`mfx::poissonmfx()`

, `mfx::probitmfx()`

Other mfx tidiers:
`augment.betamfx()`

,
`augment.mfx()`

,
`glance.betamfx()`

,
`tidy.betamfx()`

,
`tidy.mfx()`

A `tibble::tibble()`

with exactly one row and columns:

Akaike's Information Criterion for the model.

Bayesian Information Criterion for the model.

Deviance of the model.

Degrees of freedom used by the null model.

Residual degrees of freedom.

The log-likelihood of the model. [stats::logLik()] may be a useful reference.

Number of observations used.

Deviance of the null model.

if (FALSE) { 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 (not run) # 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) }