Stepwise backward regression
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, ...) ## Default S3 method: blr_step_p_backward(model, prem = 0.3, details = FALSE, ...) ## S3 method for class '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 |
model with the least AIC; an object of class |
steps |
total number of steps |
removed |
variables removed from 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.
Other variable selection procedures:
blr_step_aic_backward()
,
blr_step_aic_both()
,
blr_step_aic_forward()
,
blr_step_p_forward()
## Not run: # 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 ## End(Not run)
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