Build regression model from a set of candidate predictor variables by removing predictors based on p values, in a stepwise manner until there is no variable left to remove any more.
blr_step_p_backward(model, ...) # S3 method for default blr_step_p_backward(model, prem = 0.3, details = FALSE, ...) # S3 method for blr_step_p_backward plot(x, model = NA, print_plot = TRUE, ...)
model | An object of class |
---|---|
... | Other inputs. |
prem | p value; variables with p more than |
details | Logical; if |
x | An object of class |
print_plot | logical; if |
blr_step_p_backward
returns an object of class "blr_step_p_backward"
.
An object of class "blr_step_p_backward"
is a list containing the
following components:
model with the least AIC; an object of class glm
total number of steps
variables removed from 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.
Other variable selection procedures:
blr_step_aic_backward()
,
blr_step_aic_both()
,
blr_step_aic_forward()
,
blr_step_p_forward()
if (FALSE) { # stepwise backward regression model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) blr_step_p_backward(model) # stepwise backward regression plot model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) k <- blr_step_p_backward(model) plot(k) # final model k$model }