ols_step_backward_p {olsrr}

Stepwise backward regression

Description

Build regression model from a set of candidate predictor variables by removing predictors based on p values, in a stepwise manner until there is no variable left to remove any more.

Usage

ols_step_backward_p(model, ...)

## Default S3 method:
ols_step_backward_p(
  model,
  p_val = 0.3,
  include = NULL,
  exclude = NULL,
  hierarchical = FALSE,
  progress = FALSE,
  details = FALSE,
  ...
)

## S3 method for class 'ols_step_backward_p'
plot(x, model = NA, print_plot = TRUE, details = TRUE, ...)

Arguments

model

An object of class lm; the model should include all candidate predictor variables.

...

Other inputs.

p_val

p value; variables with p more than p_val will be removed from the model.

include

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

exclude

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

hierarchical

Logical; if TRUE, performs hierarchical selection.

progress

Logical; if TRUE, will display variable selection progress.

details

Logical; if TRUE, will print the regression result at each step.

x

An object of class ols_step_backward_p.

print_plot

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

Value

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

model

final model; an object of class lm

metrics

selection metrics

References

Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

See Also

Other backward selection procedures: ols_step_backward_adj_r2(), ols_step_backward_aic(), ols_step_backward_r2(), ols_step_backward_sbc(), ols_step_backward_sbic()

Examples

# stepwise backward regression
model <- lm(y ~ ., data = surgical)
ols_step_backward_p(model)

# stepwise backward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_p(model)
plot(k)

# selection metrics
k$metrics

# final model
k$model

# include or exclude variables
# force variable to be included in selection process
ols_step_backward_p(model, include = c("age", "alc_mod"))

# use index of variable instead of name
ols_step_backward_p(model, include = c(5, 7))

# force variable to be excluded from selection process
ols_step_backward_p(model, exclude = c("pindex"))

# use index of variable instead of name
ols_step_backward_p(model, exclude = c(2))

# hierarchical selection
model <- lm(y ~ bcs + alc_heavy + pindex + age + alc_mod, data = surgical)
ols_step_backward_p(model, 0.1, hierarchical = TRUE)

# plot
k <- ols_step_backward_p(model, 0.1, hierarchical = TRUE)
plot(k)