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

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

Description

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

Usage

## S3 method for class '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)

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