Automated selection of model parameters
This function performs an automated selection of the 'best' parameters, updating and returning the "best" model.
select_parameters(model, ...) ## S3 method for class 'lm' select_parameters(model, direction = "both", steps = 1000, k = 2, ...) ## S3 method for class 'merMod' select_parameters(model, direction = "backward", steps = 1000, ...) ## S3 method for class 'stanreg' select_parameters(model, method = NULL, cross_validation = FALSE, ...)
model |
A statistical model (of class |
... |
Arguments passed to or from other methods. |
direction |
the mode of stepwise search, can be one of |
steps |
the maximum number of steps to be considered. The default is 1000 (essentially as many as required). It is typically used to stop the process early. |
k |
the multiple of the number of degrees of freedom used for the penalty.
Only |
method |
The method used in the variable selection. Can be |
cross_validation |
Select with cross-validation. |
For frequentist GLMs, select_parameters() performs an AIC-based
stepwise selection.
For mixed-effects models of class merMod, stepwise selection is
based on stepcAIC(). This step function
only searches the "best" model based on the random-effects structure,
i.e. select_parameters() adds or excludes random-effects until
the cAIC can't be improved further.
For Bayesian models, it uses the projpred package.
The model refitted with optimal number of parameters.
model <- lm(mpg ~ ., data = mtcars)
select_parameters(model)
model <- lm(mpg ~ cyl * disp * hp * wt, data = mtcars)
select_parameters(model)
# lme4 -------------------------------------------
if (require("lme4")) {
model <- lmer(
Sepal.Width ~ Sepal.Length * Petal.Width * Petal.Length + (1 | Species),
data = iris
)
select_parameters(model)
}
## Not run:
# rstanarm -------------------------------------------
if (require("rstanarm") && require("projpred")) {
model <- stan_glm(
mpg ~ .,
data = mtcars,
iter = 500, refresh = 0, verbose = FALSE
)
select_parameters(model, cross_validation = TRUE)
model <- stan_glm(
mpg ~ cyl * disp * hp,
data = mtcars,
iter = 500, refresh = 0, verbose = FALSE
)
select_parameters(model, cross_validation = FALSE)
}
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