Adapt the proposal distribution of MCMC using the covariance of samples

This function takes the provided libbi object and runs MCMC, adapting the proposal distribution until the desired acceptance rate is achieved. If a scale is given, it will be used to adapt the proposal at each iteration

Usage

adapt_proposal(
  x,
  min = 0,
  max = 1,
  scale = 2,
  max_iter = 10,
  adapt = c("size", "shape", "both"),
  size = FALSE,
  correlations = TRUE,
  truncate = TRUE,
  quiet = FALSE,
  ...
)

Arguments

x

link{libbi} object

min

minimum acceptance rate

max

maximum acceptance rate

scale

scale multiplier/divider for the proposal. If >1 this will be inverted.

max_iter

maximum of iterations (default: 10)

adapt

what to adapt; if "size" (default), the width of independent proposals will be adapted; if "shape", proposals will be dependent (following a multivariate normal) taking into account empirical correlations; if "both", the size will be adapted before the shape

size

(deprecated, use {adapt} instead) if TRUE (default: FALSE), the size of the (diagonal multivariate normal) proposal distribution will be adapted

correlations

(deprecated, use {adapt} instead) if TRUE (default: FALSE), the shape of the (diagonal multivariate normal) proposal distribution will be adapted according to the empirical covariance

truncate

if TRUE, the proposal distributions will be truncated according to the support of the prior distributions

quiet

if set to TRUE, will not provide running output of particle numbers tested

...

parameters for sample

Value

a libbi with the desired proposal distribution

Examples

example_obs <- rbi::bi_read(system.file(package="rbi", "example_dataset.nc"))
example_model <- rbi::bi_model(system.file(package="rbi", "PZ.bi"))
example_bi <- rbi::libbi(model = example_model, obs = example_obs)
obs_states <- rbi::var_names(example_model, type="obs")
max_time <- max(vapply(example_obs[obs_states], function(x) {
  max(x[["time"]])
}, 0))
# adapt to acceptance rate between 0.1 and 0.5
if (FALSE) { # \dontrun{
  adapted <- adapt_proposal(example_bi,
    nsamples = 100, end_time = max_time,
    min = 0.1, max = 0.5, nparticles = 256, correlations = TRUE
  )
} # }