Description Usage Arguments Value Deprecated Function References See Also Examples
View source: R/ols-stepwise-backward-regression.R
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.
1 2 3 4 5 6 7 8 9 | ols_step_backward_p(model, ...)
## Default S3 method:
ols_step_backward_p(model, prem = 0.3,
progress = FALSE, details = FALSE, ...)
## S3 method for class 'ols_step_backward_p'
plot(x, model = NA, print_plot = TRUE,
...)
|
model |
An object of class |
... |
Other inputs. |
prem |
p value; variables with p more than |
progress |
Logical; if |
details |
Logical; if |
x |
An object of class |
print_plot |
logical; if |
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:
model |
final model; an object of class |
steps |
total number of steps |
removed |
variables removed from the model |
rsquare |
coefficient of determination |
aic |
akaike information criteria |
sbc |
bayesian information criteria |
sbic |
sawa's bayesian information criteria |
adjr |
adjusted r-square |
rmse |
root mean square error |
mallows_cp |
mallow's Cp |
indvar |
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
1 2 3 4 5 6 7 8 9 10 11 | # 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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