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.

Usage

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

Arguments

obj

object returned by MakeADFun() after optimisation or model of class qremlModel as returned by qreml.

what

vector of strings with names of parameters and/or REPORT()ed quantities to be reported

nSamples

number of samples to draw from the multivariate normal distribution of the MLE

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

probs

vector of probabilities for the confidence intervals (ignored if no CIs are computed)

Value

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. If quantity is an array, the list contains an array with one extra dimension.

Details

This function simply samples from the approximate multivariate normal distribution of the maximum likelihood estimate (MLE) of the parameters $$ \hat{\theta} \sim N(\theta, H^{-1}), $$ where \(H\) is the Hessian matrix of the negative log-likelihood function at the MLE. It then returns either the sampled parameters or REPORT()ed transformations of them. If CI = TRUE, it does not return the samples but directly computes confidence intervals.

If you are interested in several quantities, calling sdreportMC once with a vector what will generally be faster than calling it several times with single elements of what.

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
#> Performance tip: Consider running `TapeConfig(matmul = 'plain')` before `MakeADFun()` to speed up the forward algorithm.

## 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"), 
                   nSamples = 50)
#> Performance tip: Consider running `TapeConfig(matmul = 'plain')` before `MakeADFun()` to speed up the forward algorithm.
#> Sampling reported quantities...
dim(sdrMC$delta)
#> [1] 50  2
# now a matrix with 50 samples (rows)