summary.segratioMCMC function

Summary statistics for an segratioMCMC object

Wrapper for summary.mcmc processing only mixture model parameters although markers may also easily be summarised. The mean, standard deviation, naive standard error of the mean (ignoring autocorrelation of the chain) and time-series standard error based on an estimate of the spectral density at 0. For details see summary.mcmc

## S3 method for class 'segratioMCMC'
summary(object, ..., row.index = c(1:10),
 var.index = NULL,
 marker.index = c(1:8))

Arguments

• object: object of class segratioMCMC
• ...: extra options for summary.mcmc
• row.index: which rows to print (Default: first 10)
• var.index: which mixture model variable to summarise (Default: all)
• marker.index: which markers to summarise (Default: 1:8)

Returns

An object of class summarySegratioMCMC is returned which contains summary statistics for parameters and some markers. For details see summary.mcmc

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

summary.mcmc mcmc

segratioMCMC readJags

diagnosticsJagsMix

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## Not run:

## fit simple model in one hit and summarise

x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(summary(x.run$mcmc.mixture))
print(summary(x.run$mcmc.mixture, var.index=c(1:3), marker.index=c(1:4)))
## End(Not run)