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, print_plot = TRUE, ...)
model | An object of class |
---|---|
... | Other arguments. |
pent | p value; variables with p value less than |
prem | p value; variables with p more than |
details | Logical; if |
x | An object of class |
print_plot | logical; if |
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:
final model; an object of class glm
candidate predictor variables according to the order by which they were added or removed from the model
addition/deletion
total number of steps
variables retained in the model (after addition)
akaike information criteria
bayesian information criteria
deviance
predictors
Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.
if (FALSE) { # 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 }