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Tidy a(n) regsubsets object

Source: R/leaps-tidiers.R
tidy.regsubsets.Rd

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.

Usage

# S3 method for class '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. Two exceptions here are:

  • tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

  • augment() methods will warn when supplied a newdata argument if it will be ignored.

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


# 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 
#>   <lgl>         <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
#> 1 TRUE          FALSE TRUE  FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> 2 TRUE          FALSE FALSE TRUE  FALSE FALSE FALSE FALSE FALSE FALSE
#> 3 TRUE          FALSE FALSE TRUE  FALSE TRUE  FALSE FALSE FALSE FALSE
#> 4 TRUE          TRUE  FALSE TRUE  FALSE TRUE  FALSE FALSE FALSE FALSE
#> 5 TRUE          TRUE  FALSE TRUE  FALSE TRUE  FALSE TRUE  FALSE FALSE
#> 6 TRUE          TRUE  TRUE  TRUE  FALSE TRUE  FALSE TRUE  FALSE FALSE
#> 7 TRUE          TRUE  TRUE  TRUE  FALSE TRUE  FALSE TRUE  TRUE  FALSE
#> 8 TRUE          TRUE  TRUE  TRUE  FALSE TRUE  FALSE TRUE  TRUE  TRUE 
#> # ℹ 5 more variables: carb <lgl>, r.squared <dbl>, adj.r.squared <dbl>,
#> #   BIC <dbl>, mallows_cp <dbl>