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.

blr_step_p_both(model, ...)

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

# S3 method for blr_step_p_both
plot(x, model = NA, ...)

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.

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:

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)
# }