"plot"(x, whichPlot = NULL, whichParam = "cellCounts", whichCell = NULL, layout = TRUE, ...)
eiwild
"alphaDraws"
or "cellCounts"
par=mfrow()
plot.eiwild
uses the plot diagnostic functions of
the coda package. The default is NULL
which passes
arguments to plot.mcmc
: 1
passes arguments to traceplot
2
passes arguments to
autocorr.plot
3
passes
arguments to densplot
4
calculates and plots rolling mean plot.mcmc
mcmc
## Not run:
# # loading some fake election data
# data(topleveldat)
# form <- cbind(CSU_2, SPD_2, LINK_2, GRUN_2) ~ cbind(CSU_1, SPD_1, Link_1)
# set.seed(1234)
# res <- indAggEi(form=form, aggr=aggr, indi=indi, IDCols=c("ID","ID"),
# sample=1000, thinning=2, burnin=100,verbose=100)
#
# plot(res, whichPlot=1)
# plot(res, whichPlot=2)
# plot(res, whichPlot=3)
# plot(res, whichPlot=4)
#
# plot(res, whichPlot=1, whichCell=c(1,4,5))
# plot(res, whichPlot=2, whichCell=c(1,4,5))
# plot(res, whichPlot=3, whichCell=c(1,4,5))
# plot(res, whichPlot=4, whichCell=c(1,4,5))
#
# plot(res, whichPlot=1, whichCell=c(1))
# plot(res, whichPlot=2, whichCell=c(1))
# plot(res, whichPlot=3, whichCell=c(1))
# plot(res, whichPlot=4, whichCell=c(1))
#
# plot(res, whichPlot=1, whichParam="alphaDraws")
# plot(res, whichPlot=2, whichParam="alphaDraws")
# plot(res, whichPlot=3, whichParam="alphaDraws")
# plot(res, whichPlot=4, whichParam="alphaDraws")
#
# par(mfrow=c(2,2))
# plot(res, whichPlot=1, whichCell=1, layout=FALSE)
# plot(res, whichPlot=2, whichCell=1, layout=FALSE)
# plot(res, whichPlot=3, whichCell=1, layout=FALSE)
# plot(res, whichPlot=4, whichCell=1, layout=FALSE)
# ## End(Not run)
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