fitMM {smam} | R Documentation |
Fit a Moving-Moving Model with 2 Embedded Brownian Motion
Description
Fit a Moving-Moving Model with 2 Embedded Brownian Motion with animal movement data at discretely observation times by maximizing a full likelihood constructed from the marginal density of increment. 'estVarMM' uses parametric bootstrap to obtain variance matrix of estimators from 'fitMM'.
Usage
fitMM(
data,
start,
logtr = FALSE,
method = "Nelder-Mead",
optim.control = list(),
integrControl = integr.control()
)
estVarMM(
est_theta,
data,
nBS,
detailBS = FALSE,
numThreads = 1,
integrControl = integr.control()
)
Arguments
data |
data used to process estimation |
start |
starting value of the model, a vector of four components in the order of rate for moving1, rate for moving2, and volatility1(larger), volatility2(smaller). |
logtr |
logical, if TRUE parameters are estimated on the log scale. |
method |
the method argument to feed |
optim.control |
a list of control to be passed to |
integrControl |
a list of control parameters for the |
est_theta |
estimators of MRME model |
nBS |
number of bootstrap. |
detailBS |
whether or not output estimation results during bootstrap, which can be used to generate bootstrap CI. |
numThreads |
the number of threads for parallel computation. If its value is greater than 1, then parallel computation will be processed. Otherwise, serial computation will be processed. |
Value
a list of the following components:
estimate |
the esimated parameter vector |
loglik |
maximized loglikelihood or composite loglikelihood evaluated at the estimate |
convergence |
convergence code from |
References
Yan, J., Chen, Y., Lawrence-Apfel, K., Ortega, I. M., Pozdnyakov, V., Williams, S., and Meyer, T. (2014) A moving-resting process with an embedded Brownian motion for animal movements. Population Ecology. 56(2): 401–415.
Pozdnyakov, V., Elbroch, L., Labarga, A., Meyer, T., and Yan, J. (2017) Discretely observed Brownian motion governed by telegraph process: estimation. Methodology and Computing in Applied Probability. doi:10.1007/s11009-017-9547-6.
Examples
## Not run:
## time consuming example
tgrid <- seq(0, 100, length=100)
set.seed(123)
dat <- rMM(tgrid, 1, 0.1, 1, 0.1, "m1")
## fit whole dataset to the MR model
fit <- fitMM(dat, start=c(1, 0.1, 1, 0.1))
fit
var <- estVarMM(fit$estimate, dat, nBS = 10, numThreads = 6)
var
## End(Not run)