LaMa (version 3.1.0)

simulate.LaMaModel: Simulate from a fitted model

Description

Simulates states and observations from a model fitted with forward and statedens. By default, this is done on the observed design: the tracks and their lengths, the initial distribution delta, the transition probabilities Gamma and the parameters of the state-dependent densities are those reported by the model, i.e. conditional on the observed covariates. Simulating for new covariates is not possible; instead, rebuild the likelihood function for the new data and call lama on the resulting object.

Usage

# S3 method for LaMaModel
simulate(
  object,
  nsim = 1,
  seed = NULL,
  rdens = NULL,
  random = FALSE,
  fill_na = FALSE,
  ...
)

Value

data frame with the columns trackID (index of the track, if used), state and one column per data stream, for nsim = 1, else a list of nsim such data frames.

Arguments

object

model object from lama or qreml that was built with statedens

nsim

number of simulations

seed

optional seed for the random number generator

rdens

optional named list of random number generators of the data streams, for densities without one named by replacing the leading d with r (e.g. from zero_inflate). The first argument is the number of draws, the others are the parameters as in the density.

random

logical; if TRUE, obj$simulate() of RTMB is used, so that random effects are drawn from their distribution first, see ‘Details’. The data streams are those passed to statedens. Default is FALSE, which keeps the reported (estimated) random effects.

fill_na

logical; if TRUE, missing observations of the data are filled in by simulated values (their parameters need to be defined, e.g. covariates must not be missing). Default is FALSE, which keeps them missing. Streams that are missing completely are always simulated.

...

currently not used

Details

The states are drawn as in the decoding functions, so that all types of Gamma that forward accepts (homogeneous, track-specific, time-varying, periodic) work, as does a continuous-time model with one transition matrix per time interval (the simulation is then conditional on the observed time points). Each data stream is drawn from the random number generator belonging to its density, e.g. rgamma2 for dgamma2.

Missing observations of the data stay missing (unless fill_na = TRUE), except for a stream that is missing completely: such a placeholder (e.g. step = rep(NA, n)) lets you simulate from a likelihood function without data, see the examples.

Models with random effects are by default simulated conditional on the estimated random effects. With random = TRUE, RTMB draws the random effects first, which requires that the density of each random effect is evaluated in the likelihood function before the random effect is used, either by u %~% dnorm(...) or by nll <- nll - sum(dnorm(u, ..., log = TRUE)) (or another density). The states and data are then simulated given these draws, so that every simulation has new random effects. This also requires that allprobs is the product of the data streams built by statedens, with each data stream passed once.

Examples

nll <- function(par) {
  getAll(par, dat)
  Gamma <- tpm(eta)
  allprobs <- statedens(step, dgamma2, exp(log_mu), exp(log_sigma)) *
    statedens(angle, dvm, 0, exp(log_kappa))
  -forward(stationary(Gamma), Gamma, allprobs)
}
dat <- list(step = trex$step[1:500], angle = trex$angle[1:500])
par <- list(eta = c(-2, -2), log_mu = log(c(0.3, 2.5)), log_sigma = log(c(0.2, 1.5)),
            log_kappa = log(c(0.2, 1.5)))
obj <- MakeADFun(nll, par, silent = TRUE)
opt <- nlminb(obj$par, obj$fn, obj$gr)
mod <- lama(obj)

sim <- simulate(mod, seed = 1)
head(sim)

# simulate without data: all-NA streams as placeholders, parameters of your choice, then refit
dat$step <- dat$angle <- rep(NA, 500)
obj0 <- MakeADFun(nll, par, silent = TRUE)
sim <- simulate(lama(obj0), seed = 1)
dat$step <- sim$step; dat$angle <- sim$angle
obj1 <- MakeADFun(nll, par, silent = TRUE)
opt1 <- nlminb(obj1$par, obj1$fn, obj1$gr)

# random effects: RTMB draws them first, then the states and data are simulated given them
nll_re <- function(par) {
  getAll(par, dat)
  u %~% dnorm(0, exp(log_su)) # track-level random effect on the mean step length in state 1
  mu <- exp(cbind(log_mu[1] + u[id], log_mu[2]))
  Gamma <- tpm(eta)
  .nll <- .nll - forward(stationary(Gamma), Gamma, statedens(step, dgamma2, mu, exp(log_sigma)), id)
  .nll
}
dat <- list(step = trex$step[1:500], id = rep(1:5, each = 100))
par_re <- list(eta = c(-2, -2), log_mu = log(c(0.3, 2.5)), log_sigma = log(c(0.2, 1.5)),
               log_su = log(0.3), u = rep(0, 5))
obj_re <- MakeADFun(nll_re, par_re, random = "u", silent = TRUE)
sim_re <- simulate(lama(obj_re), random = TRUE, seed = 1)