olsrr (version 0.6.0)

ols_step_best_subset: Best subsets regression

Description

Select the subset of predictors that do the best at meeting some well-defined objective criterion, such as having the largest R2 value or the smallest MSE, Mallow's Cp or AIC. The default metric used for selecting the model is R2 but the user can choose any of the other available metrics.

Usage

ols_step_best_subset(model, ...)
# S3 method for default
ols_step_best_subset(
  model,
  max_order = NULL,
  include = NULL,
  exclude = NULL,
  metric = c("rsquare", "adjr", "predrsq", "cp", "aic", "sbic", "sbc", "msep", "fpe",
    "apc", "hsp"),
  ...
)
# S3 method for ols_step_best_subset
plot(x, model = NA, print_plot = TRUE, ...)

Value

ols_step_best_subset returns an object of class "ols_step_best_subset". An object of class "ols_step_best_subset" is a list containing the following:

metrics

selection metrics

Arguments

model

An object of class lm.

...

Other inputs.

max_order

Maximum subset order.

include

Character or numeric vector; variables to be included in selection process.

exclude

Character or numeric vector; variables to be excluded from selection process.

metric

Metric to select model.

x

An object of class ols_step_best_subset.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

References

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

Examples

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_step_best_subset(model)
ols_step_best_subset(model, metric = "adjr")
ols_step_best_subset(model, metric = "cp")

# maximum subset
model <- lm(mpg ~ disp + hp + drat + wt + qsec, data = mtcars)
ols_step_best_subset(model, max_order = 3)

# plot
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k <- ols_step_best_subset(model)
plot(k)

# return only models including `qsec`
ols_step_best_subset(model, include = c("qsec"))

# exclude `hp` from selection process
ols_step_best_subset(model, exclude = c("hp"))