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, ...)



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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.


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

See also


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, = TRUE) ## Compare with the naive model coefficients of the same logit call (not run) # tidy(glm(am ~ cyl + hp + wt, family = binomial, data = mtcars), = 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, = TRUE) augment(mod_probmfx) glance(mod_probmfx) }