DIC {extrememix}R Documentation

Deviance Information Criterion

Description

Computation of the DIC for an extreme value mixture model

Usage

DIC(x, ...)

## S3 method for class 'evmm'
DIC(x, ...)

Arguments

x

the output of a model estimated with extrememix

...

additional arguments for compatibility.

Details

Let y denote a dataset and p(y|\theta) the likelihood of a parametric model with parameter \theta. The deviance is defined as D(\theta)= -2\log p(y|\theta). The deviance information criterion (DIC) is defined as

DIC = D(\hat\theta) + 2p_D,

where \hat\theta is the posterior estimate of \theta and p_D is referred to as the effective number of parameters and defined as

E_{\theta|y}(D(\theta)) - D(\hat\theta).

Models with a smaller DIC are favored.

Value

The DIC of a model estimated with extrememix

References

Spiegelhalter, David J., et al. "Bayesian measures of model complexity and fit." Journal of the Royal Statistical Society: Series B 64.4 (2002): 583-639.

See Also

WAIC

Examples

DIC(rainfall_ggpd)


[Package extrememix version 0.0.1 Index]