Stepwise regression — blr_step_p

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

Usage

blr_step_p_both(model, ...)

# Default S3 method
blr_step_p_both(model, pent = 0.1, prem = 0.3, details = FALSE, ...)

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

Arguments

model: An object of class lm; the model should include all candidate predictor variables.
...: Other arguments.
pent: p value; variables with p value less than pent will enter into the model.
prem: p value; variables with p more than prem will be removed from the model.
details: Logical; if TRUE, will print the regression result at each step.
x: An object of class blr_step_p_both.
print_plot: logical; if TRUE, prints the plot else returns a plot object.

Value

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

model: final model; an object of class glm
orders: candidate predictor variables according to the order by which they were added or removed from the model
method: addition/deletion
steps: total number of steps
predictors: variables retained in the model (after addition)
aic: akaike information criteria
bic: bayesian information criteria
dev: deviance
indvar: predictors

References

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

Examples

if (FALSE) { # \dontrun{
# stepwise regression
model <- glm(y ~ ., data = stepwise)
blr_step_p_both(model)

# stepwise regression plot
model <- glm(y ~ ., data = stepwise)
k <- blr_step_p_both(model)
plot(k)

# final model
k$model

} # }