ggdmc (version 0.1.3.9)

gelman.diag.dmc: Gelman and Rubin Convergence Diagnostic

Description

gelman.diag.dmc calls coda gelman.diag to get R hats for one or a list of subjects. It can calculate at the data or hyper level.

Usage

gelman.diag.dmc(x, hyper = FALSE, digits = 2, start = 1,
  autoburnin = FALSE, transform = TRUE, end = NA, ...)

Arguments

x

a DMC sample

hyper

a switch to extract hyper attribute and calculate it

digits

print out how many digits

start

start iteration

autoburnin

turn on auto burnin

transform

turn on transform

end

end iteraton

...

arguments passing to coda gelman.diag.

Examples

m1 <- model.dmc(
     p.map     = list(a="1",v="F",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"),F=c("f1","f2")),
     constants = c(st0=0,d=0),
     responses = c("r1","r2"),
     type      = "rd")

pop.mean  <- c(a=1.15, v.f1=1.25, v.f2=1.85, z=.55,  sz=.15, sv=.32, t0=.25)
pop.scale <- c(a=.10,  v.f1=.8,   v.f2=.5,   z=0.1,  sz=.05, sv=.05, t0=.05)
pop.prior <- prior.p.dmc(
  dists = rep("tnorm", length(pop.mean)),
  p1    = pop.mean,
  p2    = pop.scale,
  lower = c(0,-5, -5, 0, 0,   0, 0),
  upper = c(5, 7,  7, 1, 0.5, 2, 2))

dat  <- h.simulate.dmc(m1, nsim=30, ns=8, p.prior=pop.prior)
mdi1 <- data.model.dmc(dat, m1)
ps   <- attr(dat,  "parameters")
### FIT RANDOM EFFECTS
p.prior <- prior.p.dmc(
  dists = c("tnorm","tnorm","tnorm","tnorm","tnorm", "tnorm", "tnorm"),
  p1=pop.mean,
  p2=pop.scale*5,
  lower=c(0,-5, -5, 0, 0, 0, 0),
  upper=c(5, 7,  7, 2, 2, 2, 2))

mu.prior <- prior.p.dmc(
  dists = c("tnorm","tnorm","tnorm","tnorm","tnorm", "tnorm", "tnorm"),
  p1=pop.mean,
  p2=pop.scale*5,
  lower=c(0,-5, -5, 0, 0, 0, 0),
  upper=c(5, 7,  7, 2, 2, 2, 2))

sigma.prior <- prior.p.dmc(
  dists = rep("beta", length(p.prior)),
  p1=c(a=1, v.f1=1,v.f2 = 1, z=1, sz=1, sv=1, t0=1),p2=c(1,1,1,1,1,1,1),
  upper=c(2,2,2,2,2, 2, 2))

pp.prior <- list(mu.prior, sigma.prior)

hsamples0 <- h.samples.dmc(nmc=10, p.prior=p.prior, pp.prior=pp.prior,
  data=mdi1, thin=1)
hsamples0 <- h.run.dmc(hsamples0)
gelman.diag.dmc(hsamples0, hyper=TRUE)
gelman.diag.dmc(hsamples0)