Description Usage Arguments Value Deprecated Function References See Also Examples
Select the subset of predictors that do the best at meeting some well-defined objective criterion, such as having the largest R2 value or the smallest MSE, Mallow's Cp or AIC.
1 2 3 4 | ols_step_best_subset(model, ...)
## S3 method for class 'ols_step_best_subset'
plot(x, model = NA, ...)
|
model |
An object of class |
... |
Other inputs. |
x |
An object of class |
ols_step_best_subset
returns an object of class "ols_step_best_subset"
.
An object of class "ols_step_best_subset"
is a data frame containing the
following components:
n |
model number |
predictors |
predictors in the model |
rsquare |
rsquare of the model |
adjr |
adjusted rsquare of the model |
predrsq |
predicted rsquare of the model |
cp |
mallow's Cp |
aic |
akaike information criteria |
sbic |
sawa bayesian information criteria |
sbc |
schwarz bayes information criteria |
gmsep |
estimated MSE of prediction, assuming multivariate normality |
jp |
final prediction error |
pc |
amemiya prediction criteria |
sp |
hocking's Sp |
ols_best_subset()
has been deprecated. Instead use ols_step_best_subset()
.
Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.
Other variable selection procedures: ols_step_all_possible
,
ols_step_backward_aic
,
ols_step_backward_p
,
ols_step_both_aic
,
ols_step_forward_aic
,
ols_step_forward_p
1 2 3 4 5 6 7 | model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_step_best_subset(model)
# plot
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
k <- ols_step_best_subset(model)
plot(k)
|
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