plot.dmc: Plot DMC Samples

In TasCL/ggdmc: Dynamic Model of Choice with Parallel Computation, and C++ Capabilities

Description

Plot trace and probability desntiy, using a model samples.

Usage

## S3 method for class '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)

TasCL/ggdmc documentation built on May 9, 2019, 4:19 p.m.