summary.qde {qrjoint} | R Documentation |
Summary Method for Quantile based Density Estimation
Description
Summarize model fit for qde
Usage
## S3 method for class 'qde'
summary(object, ntrace = 1000, burn.perc = 0.5,
plot.dev = TRUE, more.details = FALSE, ...)
Arguments
object |
a fitted model of the class 'qde'. |
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 |
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. Also prints two versions of Watanabe AIC.
The following quantities are returned invisibly.
deviance |
vector deviance statistic of the samples parameter draws |
pg |
a matrix with |
prox |
posterior draws of proximity in the form of a |
ll |
a matrix of |
ql |
a matrix of |
waic |
Two versions of Watanabe AIC from Gelman, Hwang and Vehtari (2014). |
References
Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criterion for Bayesian models. Stat Comput, 24, 997-1016.
See Also
qrjoint
and coef.qrjoint
.
Examples
# Plasma data analysis
data(plasma)
Y <- plasma$BetaPlasma
Y <- Y + 0.1 * rnorm(length(Y)) ## remove atomicity
# model fitting with 50 posterior samples from 100 iterations (thin = 2)
fit.qde <- qde(Y, 50, 2)
summary(fit.qde, more = TRUE)