diagnosticsJagsMix {polySegratioMM} | R Documentation |
Produce and/or plot various diagnostic measures from coda
package for Bayesian mixture models for assessing marker dosage in
autopolyploids
diagnosticsJagsMix(mcmc.mixture, diagnostics = TRUE, plots = FALSE, index = -c( grep("T\\[",varnames(mcmc.mixture$mcmc.list)), grep("b\\[",varnames(mcmc.mixture$mcmc.list)) ), trace.plots = FALSE, auto.corrs = FALSE, density.plots = FALSE, xy.plots = FALSE, hpd.intervals = FALSE, hdp.prob = 0.95, return.results = FALSE)
mcmc.mixture |
Object of class segratioMCMC or
runJagsWrapper after JAGS run
produced by coda |
diagnostics |
if TRUE then print several coda dignostic tests |
plots |
if TRUE then produce several coda dignostic plots |
index |
index of parameters for disgnostic tests/plots (Default: mixture model (and random effects) parameters) |
trace.plots |
if TRUE plot mcmc traces (default: FALSE) |
auto.corrs |
if TRUE produce autocorrelations of mcmc's (default: FALSE) |
density.plots |
if TRUE plot parameter densities (default: FALSE) |
xy.plots |
if TRUE plot traces using 'lattice' (default: FALSE) |
hpd.intervals |
if TRUE print and return highest posterior density
intervals for parameters specified by index |
hdp.prob |
probability for hpd.intervals |
return.results |
if TRUE return results as list |
If return.results
is TRUE then a list is returned with
components depending on various settings of arguments
Peter Baker p.baker1@uq.edu.au
mcmc
autocorr.diag
raftery.diag
geweke.diag
gelman.diag
trellisplots
## 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 x.run <- runSegratioMM(sr, x, burn.in=200, sample=500) print(x.run) diagnosticsJagsMix(x.run) diagnosticsJagsMix(x.run, plot=TRUE) ## End(Not run)