Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for regsubsets
tidy(x, ...)
A regsubsets
object created by leaps::regsubsets()
.
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 columns:
R squared statistic, or the percent of variation explained by the model.
Adjusted R squared statistic
Bayesian information criterion for the component.
Mallow's Cp 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("leaps", quietly = TRUE)) {
# load libraries for models and data
library(leaps)
# fit model
all_fits <- regsubsets(hp ~ ., mtcars)
# summarize model fit with tidiers
tidy(all_fits)
}
#> # A tibble: 8 × 15
#> `(Intercept)` mpg cyl disp drat wt qsec vs am gear carb
#> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 2 TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> 3 TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> 4 TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> 5 TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE
#> 6 TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE FALSE TRUE
#> 7 TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE
#> 8 TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE
#> # … with 4 more variables: r.squared <dbl>, adj.r.squared <dbl>, BIC <dbl>,
#> # mallows_cp <dbl>