fitBmme: Fit a Brownian Motion with Measurement Error

Description Usage Arguments Details Value References See Also Examples

View source: R/bmme.R

Description

Given discretely observed animal movement locations, fit a Brownian motion model with measurement errors.

Usage

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fitBmme(data, start = NULL, method = "Nelder-Mead",
  optim.control = list())

Arguments

data

a data.frame whose first column is the observation time, and other columns are location coordinates.

start

starting value of the model, a vector of two component, one for sigma (sd of BM) and the other for delta (sd for measurement error). If unspecified (NULL), a moment estimator will be used assuming equal sigma and delta.

method

the method argument to feed optim.

optim.control

a list of control that is passed down to optim.

Details

The joint density of the increment data is multivariate normal with a sparse (tri-diagonal) covariance matrix. Sparse matrix operation from package Matrix is used for computing efficiency in handling large data.

Value

A list of the following components:

estimate

the esimated parameter vector

var.est

variance matrix of the estimator

loglik

loglikelihood evaluated at the estimate

convergence

convergence code from optim

References

Pozdnyakov V., Meyer, TH., Wang, Y., and Yan, J. (2013) On modeling animal movements using Brownian motion with measurement error. Ecology 95(2): p247–253. doi:doi:10.1890/13-0532.1.

See Also

fitMovRes

Examples

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set.seed(123)
tgrid <- seq(0, 500, by = 1)
dat <- rbmme(tgrid, sigma = 1, delta = 0.5)
fit <- fitBmme(dat)
fit

Example output

$estimate
[1] 0.9230639 0.6144865

$var.est
             [,1]         [,2]
[1,]  0.001791077 -0.001081307
[2,] -0.001081307  0.001553433

$loglik
[1] -1626.087

$convergence
[1] 0

smam documentation built on May 30, 2017, 7:23 a.m.

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