bhpm.print.convergence.summary: Print a Summary of the Convergence Diagnostics of the...

In bhpm: Bayesian Hierarchical Poisson Models for Multiple Grouped Outcomes with Clustering

Description

The function prints the maximum and minimum values of either Gelman-Rubin diagnostic or the Geweke diagnostic for each group of samples, e.g. theta, gamma, mu.gamma etc.

Usage

	bhpm.print.convergence.summary(conv)

Arguments

conv	The output from a call to bhpm.check.convergence.

Value

Nothing

Note

The Geweke statistic is a Z-score calculated from a single chain. Due to the large number of variables sampled it is possible that a certain number will be deemed significant (at the 5% level) even though the simulation may have converged.

Author(s)

R. Carragher

Examples

data(bhpm.cluster.data1)
data <- subset(bhpm.cluster.data1, Cluster == '0.0-180.0' | Cluster == '180.0-360.0')
raw = bhpm.npm(data, burnin = 100, iter = 200)
conv = bhpm.convergence.diag(raw)
bhpm.print.convergence.summary(conv)

data(bhpm.cluster.data1)
raw = bhpm.npm(bhpm.cluster.data1)
conv = bhpm.convergence.diag(raw)
bhpm.print.convergence.summary(conv)

bhpm documentation built on April 1, 2020, 5:09 p.m.