Build regression model from a set of candidate predictor variables by entering predictors based on p values, in a stepwise manner until there is no variable left to enter any more.
blr_step_p_forward(model, ...) # S3 method for default blr_step_p_forward(model, penter = 0.3, details = FALSE, ...) # S3 method for blr_step_p_forward plot(x, model = NA, print_plot = TRUE, ...)
model | An object of class |
---|---|
... | Other arguments. |
penter | p value; variables with p value less than |
details | Logical; if |
x | An object of class |
print_plot | logical; if |
blr_step_p_forward
returns an object of class "blr_step_p_forward"
.
An object of class "blr_step_p_forward"
is a list containing the
following components:
model with the least AIC; an object of class glm
number of steps
variables added to the model
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.
Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.
Other variable selection procedures:
blr_step_aic_backward()
,
blr_step_aic_both()
,
blr_step_aic_forward()
,
blr_step_p_backward()
if (FALSE) { # stepwise forward regression model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_step_p_forward(model) # stepwise forward regression plot model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) k <- blr_step_p_forward(model) plot(k) # final model k$model }