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 multinom
glance(x, ...)
A multinom
object returned from nnet::multinom()
.
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.
Other multinom tidiers:
tidy.multinom()
A tibble::tibble()
with exactly one row and columns:
Akaike's Information Criterion for the model.
Deviance of the model.
The effective degrees of freedom.
Number of observations used.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("nnet", quietly = TRUE)) {
if (requireNamespace("MASS", quietly = TRUE)) {
# load libraries for models and data
library(nnet)
library(MASS)
example(birthwt)
bwt.mu <- multinom(low ~ ., bwt)
tidy(bwt.mu)
glance(bwt.mu)
# or, for output from a multinomial logistic regression
fit.gear <- multinom(gear ~ mpg + factor(am), data = mtcars)
tidy(fit.gear)
glance(fit.gear)
}
}
#>
#> Attaching package: ‘nnet’
#> The following object is masked from ‘package:mgcv’:
#>
#> multinom
#>
#> brthwt> bwt <- with(birthwt, {
#> brthwt+ race <- factor(race, labels = c("white", "black", "other"))
#> brthwt+ ptd <- factor(ptl > 0)
#> brthwt+ ftv <- factor(ftv)
#> brthwt+ levels(ftv)[-(1:2)] <- "2+"
#> brthwt+ data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
#> brthwt+ ptd, ht = (ht > 0), ui = (ui > 0), ftv)
#> brthwt+ })
#>
#> brthwt> options(contrasts = c("contr.treatment", "contr.poly"))
#>
#> brthwt> glm(low ~ ., binomial, bwt)
#>
#> Call: glm(formula = low ~ ., family = binomial, data = bwt)
#>
#> Coefficients:
#> (Intercept) age lwt raceblack raceother smokeTRUE
#> 0.82302 -0.03723 -0.01565 1.19241 0.74068 0.75553
#> ptdTRUE htTRUE uiTRUE ftv1 ftv2+
#> 1.34376 1.91317 0.68020 -0.43638 0.17901
#>
#> Degrees of Freedom: 188 Total (i.e. Null); 178 Residual
#> Null Deviance: 234.7
#> Residual Deviance: 195.5 AIC: 217.5
#> # weights: 12 (11 variable)
#> initial value 131.004817
#> iter 10 value 98.029803
#> final value 97.737759
#> converged
#> # weights: 12 (6 variable)
#> initial value 35.155593
#> iter 10 value 14.156582
#> iter 20 value 14.031881
#> iter 30 value 14.025659
#> iter 40 value 14.021414
#> iter 50 value 14.019824
#> iter 60 value 14.019278
#> iter 70 value 14.018601
#> iter 80 value 14.018282
#> iter 80 value 14.018282
#> iter 90 value 14.017126
#> final value 14.015374
#> converged
#> # A tibble: 1 × 4
#> edf deviance AIC nobs
#> <dbl> <dbl> <dbl> <int>
#> 1 6 28.0 40.0 32