summary.segratioMCMC: Summary statistics for an segratioMCMC object

In polySegratioMM: Bayesian mixture models for marker dosage in autopolyploids

Description

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

Usage

## 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)

Value

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 [e-mail]

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)

polySegratioMM documentation built on May 2, 2019, 4:41 p.m.