ggdmc (version 0.1.3.9)

effectiveSize.dmc: Effective Sample Size for Estimating the Mean

Description

effectiveSize.dmc calls coda effectiveSize to effective size for either single or multiple subjects. It can calculate at the data or hyper level, too.

Usage

effectiveSize.dmc(x, hyper = FALSE, digits = 0, start = 1, 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

end

end iteraton

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=4, 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)
es <- effectiveSize.dmc(hsamples0)
hes <- effectiveSize.dmc(hsamples0, hyper=TRUE)