View source: R/performance_aicc.R
performance_aicc | R Documentation |
Compute the AIC or the second-order Akaike's information criterion (AICc).
performance_aic()
is a small wrapper that returns the AIC, however, for
models with a transformed response variable, performance_aic()
returns the
corrected AIC value (see 'Examples'). It is a generic function that also
works for some models that don't have a AIC method (like Tweedie models).
performance_aicc()
returns the second-order (or "small sample") AIC that
incorporates a correction for small sample sizes.
performance_aicc(x, ...)
performance_aic(x, ...)
## Default S3 method:
performance_aic(x, estimator = "ML", verbose = TRUE, ...)
x |
A model object. |
... |
Currently not used. |
estimator |
Only for linear models. Corresponds to the different
estimators for the standard deviation of the errors. If |
verbose |
Toggle warnings. |
performance_aic()
correctly detects transformed response and,
unlike stats::AIC()
, returns the "corrected" AIC value on the original
scale. To get back to the original scale, the likelihood of the model is
multiplied by the Jacobian/derivative of the transformation.
Numeric, the AIC or AICc value.
Akaike, H. (1973) Information theory as an extension of the maximum likelihood principle. In: Second International Symposium on Information Theory, pp. 267-281. Petrov, B.N., Csaki, F., Eds, Akademiai Kiado, Budapest.
Hurvich, C. M., Tsai, C.-L. (1991) Bias of the corrected AIC criterion for underfitted regression and time series models. Biometrika 78, 499–509.
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
AIC(m)
performance_aicc(m)
# correct AIC for models with transformed response variable
data("mtcars")
mtcars$mpg <- floor(mtcars$mpg)
model <- lm(log(mpg) ~ factor(cyl), mtcars)
# wrong AIC, not corrected for log-transformation
AIC(model)
# performance_aic() correctly detects transformed response and
# returns corrected AIC
performance_aic(model)
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