polySegratioMM (version 0.6-2)

summary.segratioMCMC: Summary statistics for an segratioMCMC object

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

concept

• segregation ratio
• dominant marker
• autopolyploid

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)

## 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)))