blr_step_p_both: Stepwise regression

In blorr: Tools for Developing Binary Logistic Regression Models

Description

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

## Not run: 
# 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


## End(Not run)

blorr documentation built on July 2, 2020, 2:15 a.m.