rwalc: Correlated Random Walk Filter

In SWotherspoon/RWalc: Continuous-Time Correlated Random Walk Models for Animal Movement

Description

Fit a continuous time correlated random walk to filter a track and predict locations for given time steps.

Usage

rwalc(
  data,
  predict = NULL,
  track = NULL,
  par = c(1, 1, 1, 1, 1, 1),
  betaPar = c("free", "equal", "fixed"),
  sigmaPar = c("free", "equal", "fixed"),
  tauPar = c("free", "equal", "fixed"),
  tdf = -1,
  bshrink = 1e-06,
  control = rwalcControl()
)

Arguments

data	A dataframe representing the track (see details).
predict	A vector of times (as POSIXct) or a dataframe of segments and times for which to predict locations. Ignored if track is provided.
track	Dataframe representing an initial estimate of the track (see details).
par	Vector of initial parameter estimates.
betaPar	Controls the autocorrelaion parameter for x and y processes.
sigmaPar	Controls the standard deviation parameters for the stochastic innovations of the velocity for the x and y processes.
tauPar	Controls the scaling parameter for the observational errors for the x and y processes
tdf	Degrees of freedom for the multivariate t error distribution.
bshrink	Shrinkage penalty for the correlation parameter.
control	List of control parameters (see rwalcControl)

Details

The filter fits a continuous time correlated random walk movement model similar to that described in Johnson et al. (2008) and implemented in the package crawl. Unlike the crawl model, the model implemented here has no drift or haul out components, and assumes t distributed errors as described by Albertsen et al. (2015) if tdf is positive.

The input track may consist of several independent segments. These may represent either non-overlapping segments of a single track, or distinct tracks that may overlap in time. The fitted random walk is correlated within a segment, but segments are assumed independent. It is assumed the input dataframe is ordered by segment and by date within segment.

The input track to be filtered is supplied as a dataframe (data) where each row is an observed location, with columns

segment	track segment (integer, optional)
date	observation time (as GMT POSIXct)
x	observed x coordinate
y	observed y coordinate
x.se	standard error of the x coordinate (optional)
y.se	standard error of the y coordinate (optional)

The filtering model assumes the errors in the spatial coordinates are have standard deviations 'x.se' and 'y.se' scaled by the tau model parameters. If these columns are missing, they are assumed to be 1.

An estimate of the track is required to initialize the fitting process. This can be supplied by the user through the track argument as a dataframe with the same format returned by interpolateTrack. When this argument is NULL, the initial track is generated with interpolateTrack from the data and predict arguments.

The predict argument specifies prediction times for which locations along the track will be predicted when no initial track is given. When the track consists of a single segment, these argument may be a vector of POSIXct times, otherwise it must be a dataframe with columns

segment	track segment (integer, optional)
date	prediction time (as GMT POSIXct)

If an initial track is given, the prediction times are determined from that.

The arguments betaPar, sigmaPar and tauPar control how the correlation parameters beta, the standard deviations of the innovations for the velocity process sigma and the error scaling parameters tau apply to the x and y processes:

"free"

independent parameters are estimated for x and y

"equal"

a common parameter is estimated for both x and y

"fixed"

the parameters are determined by par

Value

Returns a list with components

summary	parameter summary table
par	vector of parameter estimates
track	dataframe of the fitted track
opt	the object returned by the optimizer
tmb	the TMB object

The track dataframe has columns

segment	track segment
date	time (as GMT POSIXct)
x	x coordinate
y	y coordinate
x.v	x component of velocity
y.v	y component of velocity
x.se	standard error of x coordinate
y.se	standard error of y coordinate
x.v.se	standard error of x component of velocity
y.v.se	standard error of y component of velocity
observed	whether this was an observed time
predicted	whether this was a prediction time

References

Johnson, D. S., London, J. M., Lea, M. A. and Durban, J. W. (2008). Continuous-time correlated random walk model for animal telemetry data. Ecology, 89(5), 1208-1215.

Albertsen, C. M., Whoriskey, K., Yurkowski, D., Nielsen, A. and Flemming, J. M. (2015). Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder. Ecology, 96(10), 2598-2604.

Lange, K. L., Little, R. J., & Taylor, J. M. (1989). Robust statistical modeling using the t distribution. Journal of the American Statistical Association, 84(408), 881-896.

SWotherspoon/RWalc documentation built on Feb. 25, 2021, 8:26 p.m.