model_select {univariateML} | R Documentation |
Fit multiple models and select the best fit
Description
Selects the best model by log-likelihood, AIC, or BIC.
Usage
model_select(
x,
models = univariateML_models,
criterion = c("aic", "bic", "loglik"),
na.rm = FALSE,
...
)
Arguments
x |
a (non-empty) numeric vector of data values. |
models |
a character vector containing the distribution models to
select from; see |
criterion |
the model selection criterion. Must be one of |
na.rm |
logical. Should missing values be removed? |
... |
unused. |
Value
model_select
returns an object of class
univariateML
. This is a named numeric vector with maximum likelihood
estimates for the parameters of the best fitting model and the following
attributes:
model |
The name of the model. |
density |
The density associated with the estimates. |
logLik |
The loglikelihood at the maximum. |
support |
The support of the density. |
n |
The number of observations. |
call |
The call as captured my |
See Also
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 17. Wiley, New York.
Examples
model_select(precip)