Tidy a(n) regsubsets object

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, ...)

Arguments

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.

See also

tidy(), leaps::regsubsets()

Value

A tibble::tibble() with columns:

r.squared

R squared statistic, or the percent of variation explained by the model.

adj.r.squared

Adjusted R squared statistic

BIC

Bayesian information criterion for the component.

mallows_cp

Mallow's Cp statistic.

Examples

if (requireNamespace("leaps", quietly = TRUE)) {

all_fits <- leaps::regsubsets(hp ~ ., mtcars)
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>