Model data

Source: R/track.R

Model flight path from point estimates using a Hidden Markov Model.

track(data, states = 1, i_lambda = TRUE, ...)

Arguments

data

A data.frame containing the point estimate data.

states

The number of states to use in the model; defaults to 1.

i_lambda

Logical indicating whether to estimate individual correlation parameters; defaults to TRUE.

...

Additional arguments passed to cmdstanr::sample().

Value

Returns a movetrack object including the posterior distributions for longitude, latitude, distance, and speed per time interval.

Details

This function calls Stan via cmdstanr and uses a Hidden Markov Model (HMM) to estimate individual flight paths.

References

Auger‐Méthé, M., Newman, K., Cole, D., Empacher, F., Gryba, R., King, A. A., ... & Thomas, L. (2021). A guide to state–space modeling of ecological time series. Ecological Monographs, 91(4), e01470. doi:10.1002/ecm.1470

Baldwin, J. W., Leap, K., Finn, J. T., & Smetzer, J. R. (2018). Bayesian state-space models reveal unobserved off-shore nocturnal migration from Motus data. Ecological Modelling, 386, 38-46. doi:10.1016/j.ecolmodel.2018.08.006

Jonsen, I. D., Flemming, J. M., & Myers, R. A. (2005). Robust state–space modeling of animal movement data. Ecology, 86(11), 2874-2880. doi:10.1890/04-1852

Examples

if (FALSE) {
# Load data
data(motusData)

# Estimate locations
loc <- locate(motusData, dTime = 2)

# Model flight paths
track(loc, states = 2, parallel_chains = 4)
track(loc, i_lambda = FALSE, parallel_chains = 4)
}