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 kmeans
glance(x, ...)
A kmeans
object created by stats::kmeans()
.
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 kmeans tidiers:
augment.kmeans()
,
tidy.kmeans()
A tibble::tibble()
with exactly one row and columns:
The total between-cluster sum of squares.
Iterations of algorithm/fitting procedure completed.
The total within-cluster sum of squares.
The total sum of squares.
# feel free to ignore the following lines—they allow {broom} to supply
# examples without requiring the model/data-supplying package to be installed.
if (requireNamespace("cluster", quietly = TRUE)) {
if (requireNamespace("modeldata", quietly = TRUE)) {
library(cluster)
library(modeldata)
library(dplyr)
data(hpc_data)
x <- hpc_data[, 2:5]
fit <- pam(x, k = 4)
tidy(fit)
glance(fit)
augment(fit, x)
}
}
#> # A tibble: 4,331 × 5
#> compounds input_fields iterations num_pending .cluster
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 997 137 20 0 1
#> 2 97 103 20 0 1
#> 3 101 75 10 0 1
#> 4 93 76 20 0 1
#> 5 100 82 20 0 1
#> 6 100 82 20 0 1
#> 7 105 88 20 0 1
#> 8 98 95 20 0 1
#> 9 101 91 20 0 1
#> 10 95 92 20 0 1
#> # … with 4,321 more rows