blr_step_p_forward {blorr} | R Documentation |
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, ...)
## Default S3 method:
blr_step_p_forward(model, penter = 0.3, details = FALSE, ...)
## S3 method for class '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 |
model with the least AIC; an object of class |
steps |
number of steps |
predictors |
variables added to the model |
aic |
akaike information criteria |
bic |
bayesian information criteria |
dev |
deviance |
indvar |
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()
## Not run:
# 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
## End(Not run)