model_select: Fit multiple models and select the best fit

In JonasMoss/univariateML: Maximum Likelihood Estimation for Univariate Densities

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 print(univariateML_models).
criterion	the model selection criterion. Must be one of "aic", "bic", and "loglik". Defaults to "aic".
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 match.call

See Also

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 17. Wiley, New York.

Examples

# Select among all possible models.
model_select(precip)

# View possible models to fit.
print(univariateML_models)

# Try out only gamma, Weibull, and exponential.
model_select(precip, c("gamma", "weibull", "exp"))

JonasMoss/univariateML documentation built on Feb. 6, 2024, 2:21 p.m.