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 margins
glance(x, ...)
A margins
object returned from margins::margins()
.
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.
A tibble::tibble()
with exactly one row and columns:
Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.
Degrees of freedom used by the model.
Residual degrees of freedom.
Number of observations used.
P-value corresponding to the test statistic.
R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.
Estimated standard error of the residuals.
Test statistic.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("margins", quietly = TRUE)) {
# load libraries for models and data
library(margins)
# example 1: logit model
mod_log <- glm(am ~ cyl + hp + wt, data = mtcars, family = binomial)
# get tidied "naive" model coefficients
tidy(mod_log)
# convert to marginal effects with margins()
marg_log <- margins(mod_log)
# get tidied marginal effects
tidy(marg_log)
tidy(marg_log, conf.int = TRUE)
# requires running the underlying model again. quick for this example
glance(marg_log)
# augmenting `margins` outputs isn't supported, but
# you can get the same info by running on the underlying model
augment(mod_log)
# example 2: threeway interaction terms
mod_ie <- lm(mpg ~ wt * cyl * disp, data = mtcars)
# get tidied "naive" model coefficients
tidy(mod_ie)
# convert to marginal effects with margins()
marg_ie0 <- margins(mod_ie)
# get tidied marginal effects
tidy(marg_ie0)
glance(marg_ie0)
# marginal effects evaluated at specific values of a variable (here: cyl)
marg_ie1 <- margins(mod_ie, at = list(cyl = c(4,6,8)))
# summarize model fit with tidiers
tidy(marg_ie1)
# marginal effects of one interaction variable (here: wt), modulated at
# specific values of the two other interaction variables (here: cyl and drat)
marg_ie2 <- margins(mod_ie,
variables = "wt",
at = list(cyl = c(4,6,8), drat = c(3, 3.5, 4)))
# summarize model fit with tidiers
tidy(marg_ie2)
}
#> # A tibble: 18 × 7
#> term at.variable at.value estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 wt cyl 4 7.91 5.06 1.56 0.118
#> 2 wt drat 3 7.91 5.06 1.56 0.118
#> 3 wt cyl 4 7.91 5.06 1.56 0.118
#> 4 wt drat 3.5 7.91 5.06 1.56 0.118
#> 5 wt cyl 4 7.91 5.06 1.56 0.118
#> 6 wt drat 4 7.91 5.06 1.56 0.118
#> 7 wt cyl 6 2.96 2.52 1.18 0.239
#> 8 wt drat 3 2.96 2.52 1.18 0.239
#> 9 wt cyl 6 2.96 2.52 1.18 0.239
#> 10 wt drat 3.5 2.96 2.52 1.18 0.239
#> 11 wt cyl 6 2.96 2.52 1.18 0.239
#> 12 wt drat 4 2.96 2.52 1.18 0.239
#> 13 wt cyl 8 -1.98 2.40 -0.825 0.409
#> 14 wt drat 3 -1.98 2.40 -0.825 0.409
#> 15 wt cyl 8 -1.98 2.40 -0.825 0.409
#> 16 wt drat 3.5 -1.98 2.40 -0.825 0.409
#> 17 wt cyl 8 -1.98 2.40 -0.825 0.409
#> 18 wt drat 4 -1.98 2.40 -0.825 0.409