ggdmc (version 0.1.3.9)

summary.dmc.list: Summarise a DMC Sample with Multiple Participants

Description

Call coda package to summarise the model parameters in a DMC samples with multiple participants

Usage

# S3 method for dmc.list
summary(object, digits = 2, start = 1, end = NA, ...)

Arguments

object

a model samples

digits

how many digits to print

start

summarise from which MCMC iteration. Default uses the first iteration.

end

summarise to the end of MCMC iteration. For example, set start=101 and end=1000, instructs the function to calculate from 101 to 1000 iteration. Default uses the last iteration.

...

other aruguments

See Also

summary.dmc, summary.hyper

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=0.55, sz=0.15, sv=0.32,
               t0=0.25)
pop.scale <- c(a=0.10, v.f1=.8,   v.f2=.5,   z=0.1,  sz=0.05, sv=0.05,
               t0=0.05)
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=4, p.prior=pop.prior)
mdi1 <- data.model.dmc(dat, m1)
ps   <- attr(dat,  "parameters")

p.prior <- prior.p.dmc(
  dists= rep("tnorm", length(pop.mean)),
  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))
samples0 <- h.samples.dmc(nmc=30, p.prior=p.prior, data=mdi1, thin=1)
samples0 <- h.run.dmc(samples0)
class(samples0)
## [1] "dmc.list"
gelman.diag.dmc(samples0)

## summary calls theta.as.mcmc.list, which is very slow.
## summary(samples0)