summary.sbde: Summary Method for Semiparametric Density Estimation

Description

Summarize model fit for sbde

Usage

# S3 method for sbde
summary(object, ntrace = 1000, burn.perc = 0.5, plot.dev = TRUE,
        more.details = FALSE, ...)

Value

Displays the trace of the deviance statistic. More details include trace plots of of the proximity parameter of each GP, a plot of Geweke p-values for (from geweke.diag) convergence of each model parameter and an image plot of parameter correlation.

The following quantities are returned invisibly.

deviance: vector deviance statistic of the samples parameter draws
pg: a matrix with nsamp number of columns. Each column gives the conditional posterior weights on the lambda grid values for the corresponding GP function.
prox: posterior draws of proximity parameter.
ll: a matrix of n*nsamp containing observation level log-likelihood contributions. Used to calculate waic, and could be used for other AIC calculations.
waic: Two versions of Watanabe AIC from Gelman, Hwang and Vehtari (2014).

Arguments

object: a fitted model of the class 'sbde'.
ntrace: number of draws to be included in trace plots
burn.perc: fraction of MCMC draws to be discarded as burn-in.
plot.dev: logical indicator of whether to show trace plot of deviance
more.details: logical indicating whether other details from MCMC are to be plotted
...: a limited number of plotting controls that are passed onto the deviance plot

References

Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criterion for Bayesian models. Stat Comput, 24, 997-1016.

Examples

Run this code

y <- abs(rt(n=1000, df=4))
fit <- sbde(y, blocking="all", fbase="gpd", verbose=FALSE)
sm <- summary(fit, more=TRUE)
print(sm$waic)