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 cross 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

Value

A tibble::tibble() with columns:

mallows_cp

Mallow's Cp statistic.

Examples

all_fits <- leaps::regsubsets(hp ~ ., mtcars) tidy(all_fits)
#> # A tibble: 8 x 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>