Description Usage Arguments Details Value See Also Examples
Manipulate PMMH objects.
The method biips_pmmh_update
performs adaptation and burn-in
iterations for the PMMH algorithm.
The method biips_pmmh_samples
performs iterations for the PMMH
algorithm and returns samples.
1 2 3 4 5 6 7 8 9 | is.pmmh(object)
## S3 method for class 'pmmh'
biips_pmmh_update(object, n_iter, n_part, thin = 1,
max_fail = 0, rw_adapt = TRUE, output = "p", ...)
## S3 method for class 'pmmh'
biips_pmmh_samples(object, n_iter, n_part, thin = 1,
max_fail = 0, output = "p", ...)
|
object |
a |
n_iter |
integer. Number of burn-in iterations. |
n_part |
integer. Number of particles used in SMC algorithms. |
thin |
integer. Thinning interval. Returns samples every |
max_fail |
integer. maximum number of failed SMC algorithms allowed. (default=0). |
rw_adapt |
logical. Activate adaptation of the proposal
(default= |
output |
string. Select additional members to be returned in the
|
... |
Additional arguments to be passed to the SMC
algorithm such as |
The output
string arguments can be used to query additional
members in the mcmcarray.list
output. If output
contains:
returns member log_marg_like_pen
. mcmcarray
with penalized
log marginal likelihood estimates over iterations.
returns member log_marg_like
. mcmcarray
with log marginal likelihood estimates over iterations.
returns member info$accept_rate
. mcmcarray
with acceptance
rate over iterations.
returns member info$rw_step
. mcmcarray
with standard deviations of the random walk over iterations.
returns member info$n_fail
. number of failed SMC algorithms.
The function is.pmmh
returns TRUE
if the object is of
class pmmh
.
The methods biips_pmmh_update
and biips_pmmh_update
return an object of class mcmcarray.list
.
biips_pmmh_samples
output contains one mcmcarray
member for each monitored variable returned by the param_names()
and
latent_names()
member functions of the pmmh
object.
The members of the mcmcarray.list
object are
mcmcarray
objects for different variables. Assuming
dim
is the dimension of the monitored variable, the
mcmcarray
object is an array of dimension c(dim,
n_iter)
with the following attributes (accessible with
attr
):
name |
string with the name of the variable. |
lower |
vector with the lower bounds of the variable. |
upper |
vector with the upper bounds of the variable. |
If the output
argument is not empty, the output contains
additional members. See details.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | modelfile <- system.file('extdata', 'hmm.bug', package = 'rbiips')
stopifnot(nchar(modelfile) > 0)
cat(readLines(modelfile), sep = '\n')
data <- list(tmax = 10, p = c(.5, .5), logtau_true = log(1))
model <- biips_model(modelfile, data)
n_part <- 50
obj_pmmh <- biips_pmmh_init(model, 'logtau', latent_names = c('x', 'c[2:10]'),
inits = list(logtau = -2)) # Initialize
is.pmmh(obj_pmmh)
out_pmmh_burn <- biips_pmmh_update(obj_pmmh, 100, n_part) # Burn-in
out_pmmh <- biips_pmmh_samples(obj_pmmh, 100, n_part, thin = 1) # Samples
dens_pmmh_lt <- biips_density(out_pmmh$logtau)
summ_pmmh_x <- biips_summary(out_pmmh$x, order = 2, probs = c(0.025, 0.975))
dens_pmmh_x <- biips_density(out_pmmh$x)
summ_pmmh_c <- biips_summary(out_pmmh[['c[2:10]']])
table_pmmh_c <- biips_table(out_pmmh[['c[2:10]']])
par(mfrow = c(2, 2))
plot(c(out_pmmh_burn$log_marg_like_pen, out_pmmh$log_marg_like_pen), type = 'l',
col = 'blue', xlab = 'PMMH iteration', ylab = 'log p(y|logtau) + log p(logtau)')
plot(out_pmmh$logtau[1, ], type = 'l', col = 'blue',
xlab = 'PMMH iteration', ylab = 'logtau')
points(0, model$data()$logtau_true, pch = 17, col = 'green')
plot(dens_pmmh_lt, col = 'blue', ylab = 'posterior density')
points(model$data()$logtau_true, 0, pch = 17, col = 'green')
biips_hist(out_pmmh$logtau, col = 'blue', ylab = 'posterior density')
points(model$data()$logtau_true, 0, pch = 17, col = 'green')
par(mfrow = c(2, 2))
plot(model$data()$x_true, type = 'l', col = 'green', xlab = 't', ylab = 'x[t]')
lines(summ_pmmh_x$mean, col = 'blue')
matlines(matrix(unlist(summ_pmmh_x$quant), data$tmax), lty = 2, col = 'blue')
legend('topright', leg = c('true', 'PMMH estimate'), lty = c(2, 1),
col = c('green', 'blue'), bty = 'n')
barplot(.5*(model$data()$c_true==1), col = 'green', border = NA, space = 0, offset = 1,
ylim=c(0,2), xlab='t', ylab='c[t]==1', axes = FALSE)
axis(1, at=1:data$tmax-.5, labels=1:data$tmax)
axis(2, line = 1, at=c(0,2), labels=NA)
text(data$tmax/2, 1.75, 'true')
barplot(.5*c(NA, summ_pmmh_c$mode==1), col = 'blue', border = NA, space = 0,
axes = FALSE, add = TRUE)
text(data$tmax/2, .75, 'PMMH mode')
t <- 5
plot(dens_pmmh_x[[t]], col='blue', ylab = 'posterior density')
points(model$data()$x_true[t], 0, pch = 17, col = 'green')
plot(table_pmmh_c[[t-1]], col='blue', ylab = 'posterior probability mass')
points(model$data()$c_true[t], 0, pch = 17, col = 'green')
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