sdreportMC function

Monte Carlo version of sdreport

After optimisation of an AD model, sdreportMC can be used to calculate samples of confidence intervals of all model parameters and REPORT()ed quantities including nonlinear functions of random effects and parameters.

sdreportMC(
  obj,
  what,
  Hessian = NULL,
  CI = FALSE,
  n = 1000,
  probs = c(0.025, 0.975)
)

Arguments

• obj: object returned by MakeADFun() after optimisation
• what: vector of strings with names of parameters and REPORT()ed quantities to be reported
• Hessian: optional Hessian matrix. If not provided, it will be computed from the object
• CI: logical. If TRUE, only confidence intervals instead of samples will be returned
• n: number of samples to draw from the multivariate normal distribution of the MLE
• probs: vector of probabilities for the confidence intervals (ignored if no CIs are computed)

Returns

named list corresponding to the elements of what. Each element has the structure of the corresponding quantity with an additional dimension added for the samples. For example, if a quantity is a vector, the list contains a matrix. If a quantity is a matrix, the list contains an array.

Details

Caution: Currently does not work for models with fixed parameters (i.e. that use the map argument of MakeADFun.)

Examples

# fitting an HMM to the trex data and running sdreportMC
## negative log-likelihood function
nll = function(par) {
  getAll(par, dat) # makes everything contained available without $
  Gamma = tpm(eta) # computes transition probability matrix from unconstrained eta
  delta = stationary(Gamma) # computes stationary distribution
  # exponentiating because all parameters strictly positive
  mu = exp(logmu)
  sigma = exp(logsigma)
  kappa = exp(logkappa)
  # reporting statements for sdreportMC
  REPORT(mu)
  REPORT(sigma)
  REPORT(kappa)
  # calculating all state-dependent densities
  allprobs = matrix(1, nrow = length(step), ncol = N)
  ind = which(!is.na(step) & !is.na(angle)) # only for non-NA obs.
  for(j in 1:N){
    allprobs[ind,j] = dgamma2(step[ind],mu[j],sigma[j])*dvm(angle[ind],0,kappa[j])
  }
  -forward(delta, Gamma, allprobs) # simple forward algorithm
}

## initial parameter list
par = list(
 logmu = log(c(0.3, 1)),       # initial means for step length (log-transformed)
  logsigma = log(c(0.2, 0.7)), # initial sds for step length (log-transformed)
  logkappa = log(c(0.2, 0.7)), # initial concentration for turning angle (log-transformed)
  eta = rep(-2, 2)             # initial t.p.m. parameters (on logit scale)
)   
## data and hyperparameters
dat = list(
  step = trex$step[1:500],   # hourly step lengths
  angle = trex$angle[1:500], # hourly turning angles
  N = 2
)

## creating AD function
obj = MakeADFun(nll, par, silent = TRUE) # creating the objective function

## optimising
opt = nlminb(obj$par, obj$fn, obj$gr) # optimization

## running sdreportMC
# `mu` has report statement, `delta` is automatically reported by `forward()`
sdrMC = sdreportMC(obj, 
                   what = c("mu", "delta"), 
                   n = 50)
dim(sdrMC$delta)
# now a matrix with 50 samples (rows)