crwSamplePar: Create a weighted importance sample for posterior predictive...

In crawl: Fit Continuous-Time Correlated Random Walk Models to Animal Movement Data

Create a weighted importance sample for posterior predictive track simulation.

Description

The crwSamplePar function uses a fitted model object from crwMLE and a set of prediction times to construct a list from which crwPostIS will draw a sample from either the posterior distribution of the state vectors conditional on fitted parameters or a full posterior draw from an importance sample of the parameters.

Usage

crwSamplePar(
  object.sim,
  method = "IS",
  size = 1000,
  df = Inf,
  grid.eps = 1,
  crit = 2.5,
  scale = 1,
  quad.ask = T,
  force.quad
)

Arguments

object.sim	A simulation object from crwSimulator.
method	Method for obtaining weights for movement parameter samples
size	Size of the parameter importance sample
df	Degrees of freedom for the t approximation to the parameter posterior
grid.eps	Grid size for method="quadrature"
crit	Criterion for deciding "significance" of quadrature points (difference in log-likelihood)
scale	Scale multiplier for the covariance matrix of the t approximation
quad.ask	Logical, for method='quadrature'. Whether or not the sampler should ask if quadrature sampling should take place. It is used to stop the sampling if the number of likelihood evaluations would be extreme.
force.quad	A logical indicating whether or not to force the execution of the quadrature method for large parameter vectors.

Details

The crwSamplePar function uses the information in a crwSimulator object to create a set of weights for importance sample-resampling of parameters in a full posterior sample of parameters and locations using crwPostIS. This function is usually called from crwPostIS. The average user should have no need to call this function directly.

Value

List with the following elements:

x	Longitude coordinate with NA at prediction times
y	Similar to above for latitude
locType	Indicates prediction types with a "p" or observation times with an "o"
P1.y	Initial state covariance for latitude
P1.x	Initial state covariance for longitude
a1.y	Initial latitude state
a1.x	Initial longitude state
n.errX	number of longitude error model parameters
n.errY	number of latitude error model parameters
delta	vector of time differences
driftMod	Logical. indicates random drift model
stopMod	Logical. Indicated stop model fitted
stop.mf	stop model design matrix
err.mfX	Longitude error model design matrix
err.mfY	Latitude error model design matrix
mov.mf	Movement model design matrix
fixPar	Fixed values for parameters in model fitting
Cmat	Covariance matrix for parameter sampling distribution
Lmat	Cholesky decomposition of Cmat
par	fitted parameter values
N	Total number of locations
loglik	log likelihood of the fitted model
Time	vector of observation times
coord	names of coordinate vectors in original data
Time.name	Name of the observation times vector in the original data
thetaSampList	A list containing a data frame of parameter vectors and their associated probabilities for a resample

Author(s)

Devin S. Johnson

See Also

See demo(northernFurSealDemo) for example.

crawl documentation built on Oct. 10, 2022, 1:07 a.m.