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(model, ...) # S3 method for default ols_step_backward(model, prem = 0.3, details = FALSE, ...) # S3 method for ols_step_backward plot(x, model = NA, ...)
model | An object of class |
---|---|
... | Other inputs. |
prem | p value; variables with p more than |
details | Logical; if |
x | An object of class |
ols_step_backward
returns an object of class "ols_step_backward"
.
An object of class "ols_step_backward"
is a list containing the
following components:
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
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
# NOT RUN { # stepwise backward regression model <- lm(y ~ ., data = surgical) ols_step_backward(model) # }# NOT RUN { # stepwise backward regression plot model <- lm(y ~ ., data = surgical) k <- ols_step_backward(model) plot(k) # }