test_plotsubchain.R
In ggdmc: Cognitive Models

  rm(list = ls())

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

  npar <- length(GetPNames(model))
  pop.mean  <- c(a=2,   v=4, z=0.5, t0=0.3)
  pop.scale <- c(a=0.5, v=.5, z=0.1, t0=0.05)
  pop.prior <- BuildPrior(
    dists = rep("tnorm", npar),
    p1    = pop.mean,
    p2    = pop.scale,
    lower = c(0,-5,  0, 0),
    upper = c(5, 7,  1, 1))

  ## Simulate some data
  dat <- simulate(model, nsub = 5, nsim = 30, prior = pop.prior)
  dmi <- BuildDMI(dat, model)
  ps <- attr(dat, "parameters")
  p.prior <- BuildPrior(
    dists = rep("tnorm", npar),
    p1    = pop.mean,
    p2    = pop.scale*5,
    lower = c(0,-5, 0, 0),
    upper = c(5, 7, 1, 1))
  mu.prior <- BuildPrior(
    dists = rep("tnorm", npar),
    p1    = pop.mean,
    p2    = pop.scale*5,
    lower = c(0,-5,  0, 0),
    upper = c(5, 7,  1, 1)
  )

  sigma.prior <- BuildPrior(
    dists = rep("beta", npar),
    p1    = c(a=1, v=1, z=1, t0=1),
    p2    = rep(1, npar),
    upper = rep(1, npar))

  priors <- list(pprior=p.prior, location=mu.prior, scale=sigma.prior)
  fit <- run(StartNewsamples(dmi, priors))

  pdf(file = "subchains.pdf")
  p0 <- plot(fit, hyper = TRUE)
  plot(fit, subchain = TRUE, nsubchain = 1)
  plot(fit, subchain = TRUE, nsubchain = 2)
  plot(fit, subchain = TRUE, nsubchain = 3)
  plot(fit, subchain = TRUE, nsubchain = 3, chains=1:3)
  plot(fit, subchain = TRUE, nsubchain = 2, chains=1:3)
  plot(fit, subchain = TRUE, nsubchain = 1, chains=1:3)
  plot(fit, subchain = TRUE, nsubchain = 4, chains=1:3)
  plot(fit, subchain = TRUE, nsubchain = 4, chains=1:4)
  plot(fit, subchain = TRUE, nsubchain = 5)
  plot(fit, hyper = TRUE, subchain = TRUE, nsubchain = 3)
  plot(fit, hyper = TRUE, pll=FALSE, subchain = TRUE, nsubchain = 3)