Description Usage Arguments Examples
Plot trace and probability desntiy. This method provides a switch to choose plotting at the data or hyper level. The samples must be an object fit by a hierarchical model.
1 2 3 4 5 6 7 | ## S3 method for class 'hyper'
plot(x, y = NULL, hyper = FALSE, start = 1, end = NA,
save.ll = FALSE, main.pll = NULL, pll.chain = FALSE,
pll.together = TRUE, pll.barplot = FALSE, only.prior = FALSE,
only.like = FALSE, subject = 1, smooth = FALSE, density = FALSE,
save.dat = FALSE, p.prior = NULL, natural = TRUE, trans = NA,
xlim = NA, chain1 = TRUE, ...)
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x |
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y |
default NULL. No function. Just to make it compatible to
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hyper |
a boolean switch to draw hyper-parameter. By default it is off. |
start |
instruct the function to plot starting from which iteration. This indicates how many burn-in interations one requests. For example, start=101, indicates 100 burn-in interations. |
end |
instruct the function to plot ending at a certain iteration |
save.ll |
a boolean switch to tell the function to save the mean log-likelihood. This option does not work in DMC's plot.dmc, too. |
main.pll |
a string as the title for the boxplot. Default is NULL |
pll.chain |
a boolean switch to plot posterior log likelihoood |
pll.together |
a boolean switch to plot the posterior log-likelihood chains all together in one canvar |
pll.barplot |
a boolean switch to plot the means of posterior log-likelihood of all chains as a barplot. By default, it is off. |
only.prior |
only.prior and only.like pick out one or other component of pll. |
only.like |
a boolean switch. |
subject |
indicate which subject to plot in a multi-subject list samples |
smooth |
default FALSE |
density |
plot probability density too? |
save.dat |
whether save the internal data table out for polish plots |
p.prior |
prior distribution setting. necessary for plot.prior to work |
natural |
additional argument for plot.prior |
trans |
additional argument for plot.prior |
xlim |
additional argument for plot.prior |
chain1 |
plot all chains or just chain1 as coda does |
... |
other arguments |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | m1 <- model.dmc(
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")),
constants = c(st0=0, d=0),
responses = c("r1","r2"),
type = "rd")
## Population distribution
pop.prior <- prior.p.dmc(
dists = rep("tnorm", 6),
p1 = c(a=2, v=2.5, z=0.5, sz=0.3, sv=1, t0=0.3),
p2 = c(a=0.5, v=.5, z=0.1, sz=0.1, sv=.3, t0=0.05),
lower = c(0,-5, 0, 0, 0, 0),
upper = c(5, 7, 2, 2, 2, 2))
dat <- h.simulate.dmc(m1, p.prior=pop.prior, n=50, ns=4)
mdi <- data.model.dmc(dat, m1)
p.prior <- prior.p.dmc(
dists = rep("tnorm", 6),
p1 = c(a=2, v=2.5, z=0.5, sz=0.3, sv=1, t0=0.3),
p2 = c(a=0.5, v=.5, z=0.1, sz=0.1, sv=.3, t0=0.05) * 5,
lower = c(0,-5, 0, 0, 0, 0),
upper = c(5, 7, 2, 2, 2, 2))
## Fixed-effect model
samplesInit <- h.samples.dmc(nmc=100, p.prior=p.prior, data=mdi, thin=1)
samples0 <- h.run.dmc(samples=samplesInit, report=20)
## Make a hyper-prior list
mu.prior <- prior.p.dmc(
dists = rep("tnorm", 6),
p1 = c(a=2, v=2.5, z=0.5, sz=0.3, sv=1, t0=0.3),
p2 = c(a=0.5, v=.5, z=0.1, sz=0.1, sv=.3, t0=0.05) * 5,
lower = c(0,-5, 0, 0, 0, 0),
upper = c(5, 7, 2, 2, 2, 2))
sigma.prior <- prior.p.dmc(
dists = rep("beta", 6),
p1 = c(a=1, v=1, z=1, sz=1, sv=1, t0=1),
p2 = c(1,1,1,1,1,1),
upper = c(2,2,2,2,2,2))
pp.prior <- list(mu.prior, sigma.prior)
## Random-effect model
hsamplesInit <- h.samples.dmc(nmc=50, p.prior=p.prior, pp.prior=pp.prior,
data=mdi, thin=1)
hsamples0 <- h.run.dmc(samples=hsamplesInit)
## Windows Testing errors
## plot(hsamples0) ## Only first participant
## plot(hsamples0, hyper=TRUE) ## Group-level parameters
## plot(hsamples0, hyper=TRUE, density=TRUE) ## Trace and density plots
## plot(hsamples0, p.prior=p.prior) ## plot prior and posterior
## plot(hsamples0, p.prior=pp.prior, hyper=TRUE) ## hyper-level
## plot(hsamples0, hyper=TRUE, start=101) ## starting from 101th iteration
## plot(hsamples0, hyper=TRUE, density=TRUE, start=101)
## Plot posterior likelihood
## plot(hsamples0, hyper=TRUE, pll.chain=TRUE, start=21)
## Save plot data for modifying
## D <- plot(hsamples0, hyper=TRUE, save.dat=TRUE)
## head(D)
## Source: local data frame [6 x 4]
## Iteration Chain Parameter value
## (int) (fctr) (fctr) (dbl)
## 1 1 1 a.h1 2.184504
## 2 2 1 a.h1 2.184504
## 3 3 1 a.h1 2.184504
## 4 4 1 a.h1 2.501221
## 5 5 1 a.h1 2.501221
## 6 6 1 a.h1 2.501221
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