
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.
ols_step_backward_p(model, ...)# S3 method for default
ols_step_backward_p(model, prem = 0.3, details = FALSE,
...)
# S3 method for ols_step_backward_p
plot(x, model = NA, ...)
An object of class lm
; the model should include all
candidate predictor variables.
Other inputs.
p value; variables with p more than prem
will be removed
from the model.
Logical; if TRUE
, will print the regression result at
each step.
An object of class ols_step_backward_p
.
ols_step_backward_p
returns an object of class "ols_step_backward_p"
.
An object of class "ols_step_backward_p"
is a list containing the
following components:
final model; an object of class lm
total number of steps
variables removed from the model
coefficient of determination
akaike information criteria
bayesian information criteria
sawa's bayesian information criteria
adjusted r-square
root mean square error
mallow's Cp
predictors
ols_step_backward()
has been deprecated. Instead use ols_step_backward_p()
.
Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.
Other variable selection procedures: ols_step_all_possible
,
ols_step_backward_aic
,
ols_step_best_subset
,
ols_step_both_aic
,
ols_step_forward_aic
,
ols_step_forward_p
# NOT RUN {
# stepwise backward regression
model <- lm(y ~ ., data = surgical)
ols_step_backward_p(model)
# stepwise backward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_p(model)
plot(k)
# final model
k$model
# }
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