summary.segratioMCMC {polySegratioMM} | R Documentation |
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))
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) |
An object of class summarySegratioMCMC
is returned which
contains summary statistics for parameters and some markers. For
details see summary.mcmc
Peter Baker p.baker1@uq.edu.au
summary.mcmc
mcmc
segratioMCMC
readJags
diagnosticsJagsMix
## 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)