TMBoneStepPredict: Calculate one-step-ahead (OSA) residuals for a latent...

In ianjonsen/mpmm: (Animal) Movement Persistence Mixed-Effects Models

Calculate one-step-ahead (OSA) residuals for a latent variable model.

Description

Calculate one-step-ahead (OSA) residuals for a latent variable model. (Modified from TMB version to allow easier parallel computations)

Usage

TMBoneStepPredict(
  obj,
  observation.name = NULL,
  data.term.indicator = NULL,
  method = c("oneStepGaussianOffMode", "fullGaussian", "oneStepGeneric",
    "oneStepGaussian", "cdf"),
  subset = NULL,
  conditional = NULL,
  discrete = NULL,
  discreteSupport = NULL,
  range = c(-Inf, Inf),
  seed = 123,
  parallel = FALSE,
  ncores = 2,
  trace = TRUE,
  reverse = (method == "oneStepGaussianOffMode"),
  ...
)

Arguments

obj	Output from MakeADFun.
observation.name	Character naming the observation in the template.
data.term.indicator	Character naming an indicator data variable in the template (not required by all methods - see details).
method	Method to calculate OSA (see details).
subset	Index vector of observations that will be added one by one during OSA. By default 1:length(observations) (with conditional subtracted).
conditional	Index vector of observations that are fixed during OSA. By default the empty set.
discrete	Are observations discrete? (assumed FALSE by default)
discreteSupport	Possible outcomes of discrete distribution (method="oneStepGeneric" only).
range	Possible range of the observations.
seed	Randomization seed (discrete case only). If NULL the RNG seed is untouched by this routine.
parallel	Run in parallel using the parallel package?
ncores	number of threads to run in parallel. Ignored if parallel = FALSE
trace	Trace progress?
reverse	Do calculations in opposite order to improve stability ? (currently enabled by default for oneStepGaussianOffMode method only)
...	Control parameters for OSA method

Value

data.frame with OSA standardized residuals in column residual. Depending on the method the output may also include OSA expected observation in column mean.

ianjonsen/mpmm documentation built on Dec. 7, 2022, 4:27 a.m.