Regressive bridge sampler

Generate new tracks from a Crawl model

surrogateCrawl(
  model,
  xs,
  ts = 1:nrow(xs),
  fixed = rep(c(TRUE, FALSE, TRUE), c(1, nrow(xs) - 2, 1)),
  point.check = function(tm, pt) TRUE,
  Verr = diag(c(1e-04, 1e-04, 0.1, 0.1)),
  partial = FALSE
)

Arguments

model	a list with the transition and covariance matrices corresponding to the fitted movement model.
xs	the template sequence of states
ts	the times at which the track is sampled
fixed	a logical vector indicating which locations in the template path are to be held fixed.
point.check	function that accepts a state and returns boolean indicating whether the state is acceptable.
Verr	error covariance for fixed points.
partial	if TRUE, a partial track is returned if the sampling fails.

Value

An array of states the define the simulated path.

Details

Given a fitted crawl model and a template track, this function generates a new track of the same length that coincides with the template at the start point and optionally other specified points along the track.

The template track must be supplied as a matrix representing a sequence of states where each row is a state and each column a state variable. The states must be equispaced in time, and can be generated as the "p" location types from crwPredict.

The crawl model object is generated by crawlModel from a fitted crawl model.

Locations from the template track can be marked as fixed with the fixed argument. In the current implementation the first location must always be fixed.

Additional constraints can be placed on the path by rejection sampling through the function point.check. This function must accept a state and return a boolean indicating whether the point is acceptable. For example, the track can be constrained to the ocean by supplying a point.check function that compares the state to a land mask and returns FALSE for states corresponding to locations that fall on land.