ggdmc (version 0.1.3.9)

plot.dmc: Plot DMC Samples

Description

Plot trace and probability desntiy, using a model samples.

Usage

# S3 method for dmc
plot(x, y = NULL, start = 1, end = NA, save.ll = FALSE,
  main.pll = NULL, pll.chain = FALSE, pll.together = TRUE,
  pll.barplot = FALSE, only.prior = FALSE, only.like = FALSE,
  smooth = FALSE, density = FALSE, save.dat = FALSE, p.prior = NULL,
  natural = TRUE, trans = NA, xlim = NA, chain1 = TRUE, ...)

Arguments

x

a run.dmc or samples.dmc generated model samples

y

default NULL. No function. Just to make it compatible to plot

start

instruct the function to plot starting from which iteration. This indicates how many burn-in interations one requests. For example, start=101, indicates 100 burn-in interations.

end

instruct the function to plot ending at a certain iteration

save.ll

a boolean switch to tell the function to save the mean log-likelihood. This option does not work in DMC's plot.dmc, too.

main.pll

a string as the title for the boxplot. Default is NULL

pll.chain

a boolean switch to plot posterior log likelihoood

pll.together

a boolean switch to plot the posterior log-likelihood chains all together in one canvar

pll.barplot

a boolean switch to plot the means of posterior log-likelihood of all chains as a barplot. By default, it is off.

only.prior

Default is FALSE

only.like

Default is FALSE. only.prior and only.like two switches to plot only prior density or only log-likelihood probability.

smooth

default FALSE

density

plot probability density together with trace? Default FALSE

save.dat

whether save the internal data table out for polish plots

p.prior

prior distribution setting. necessary for plot.prior to work

natural

additional argument for plot.prior

trans

additional argument for plot.prior

xlim

additional argument for plot.prior

chain1

plot all chains or just chain1

...

other arguments

Details

If a samples with hyper attribute is set, plot.hyper will be called. If pll.chain=TRUE changes samples$pll to an mcmc object and plots posterior log-likelihood of chains.

Examples

m1 <- model.dmc(
  p.map=list(a="1",v="1",z="1",d="1",sz="1",sv="1", t0="1",st0="1"),
  constants=c(st0=0,d=0),
  match.map=list(M=list(s1="r1",s2="r2")),
  factors=list(S=c("s1","s2")),
  responses=c("r1","r2"),
  type="rd")

p.vector <- c(a=1,v=1, z=0.5, sz=0.25, sv=0.2,t0=.15)

dat1 <- simulate(m1, nsim=1e2, p.vector=p.vector)
mdi1 <- data.model.dmc(dat1, m1)

p.prior <- prior.p.dmc(
  dists = rep("tnorm", 6),
  p1=c(a=2,   v=2.5, z=0.5, sz=0.3, sv=1,  t0=0.3),
  p2=c(a=0.5, v=.5,  z=0.1, sz=0.1, sv=.3, t0=0.05),
  lower=c(0,-5, 0, 0, 0, 0),
  upper=c(5, 7, 2, 2, 2, 2))

samples0 <- samples.dmc(nmc=100, p.prior=p.prior, data=mdi1)
samples0 <- run.dmc(samples0, p.migrate=.05)

## Windows tests produce grid.Call problems
## Use should use with caution.
## plot(samples0)
## plot(samples0, density=TRUE)
## plot(samples0, start=101)
## plot(samples0, start=101, density=TRUE)
## plot(samples0, pll.chain=TRUE)