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 rlm
glance(x, ...)A tibble::tibble() with exactly one row and columns:
Akaike's Information Criterion for the model.
Bayesian Information Criterion for the model.
Logical indicating if the model fitting procedure was succesful and converged.
Deviance of the model.
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
Number of observations used.
Estimated standard error of the residuals.
An rlm object returned by MASS::rlm().
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.
glance(), MASS::rlm()
Other rlm tidiers:
augment.rlm(),
tidy.rlm()
# load libraries for models and data
library(MASS)
# fit model
r <- rlm(stack.loss ~ ., stackloss)
# summarize model fit with tidiers
tidy(r)
augment(r)
glance(r)
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