LaMa (version 2.1.2)

MCreport: Sample parameters from approximate Gaussian posterior distribution

Description

Efficient Monte Carlo sampling of parameters (and REPORTed quantities) from the approximate posterior of an (R)TMB model. See sdreport for details on posterior variance-covariance in random effects models.

Usage

MCreport(
  obj,
  nSamples = 1000,
  include_random_pars = TRUE,
  report = FALSE,
  Q = NULL,
  ...
)

Value

A list structured like the original parameter list used in the MakeADFun call (potentially including additional REPORTed quantities). Each entry is a list with nSamples entries.

Arguments

obj

Optimised RTMB object generated by MakeADFun

nSamples

Number of samples to draw

include_random_pars

Logical; Should random parameters be included in the output?

report

Logical; Should REPORTed quantities be sampled as well? Defaults to FALSE because this may be slow depending on your model.

Q

Optional precalculated sparse precision matrix returned by sdreport(..., getJointPrecision = TRUE)$jointPrecision. If not provided, computed internally using sdreport. Only used for models with random effects.

...

For internal use only

Details

Caution: This has nothing to do with Bayesian posterior sampling. It is simple Monte Carlo sampling from a Gaussian distribution with mean at the MLE and covariance given by the inverse of the Hessian (for fixed effects models) or the joint precision matrix (for random effects models). This is a common approach to get an approximate idea of parameter uncertainty around the MLE, but it relies on large-sample asymptotics.

Sampling random effects from their posterior as compared to calculating marginal standard devitions like sdreport is particularly useful for multidimensional random effects (e.g. for locations \(x\) and \(y\)) where pointwise confidence intervals (e.g. along a path) based on standard deviations are not possible.

Examples

step <- trex$step[1:2000] # subsetting trex data
N <- 2                    # 2 states

# custom likelihood
nll <- function(par) {
  getAll(par)
  Gamma <- tpm(eta)
  delta <- stationary(Gamma)
  mu <- exp(log_mu); REPORT(mu)
  sigma <- exp(log_sigma); REPORT(sigma)
  allprobs <- matrix(1, length(step), N)
  for(j in 1:N) allprobs[,j] <- dgamma2(step, mu[j], sigma[j])
  -forward(delta, Gamma, allprobs)
}

# initial parameters in named list
par0 <- list(eta = rep(-1.5,2), 
             log_mu = log(c(0.3, 2.5)), 
             log_sigma = log(c(0.3, 1.5)))
    
# constructing AD object        
obj <- MakeADFun(nll, par0, silent = TRUE)

# optimising
opt <- nlminb(obj$par, obj$fn, obj$gr)

# sampling from distribution of the MLE
samples <- MCreport(obj, nSamples = 10, report = TRUE)