Package 'movetrack'

Coerce to a Data Frame

Description

Coerce a movetrack object to a data.frame.

Usage

## S3 method for class 'movetrack'
as.data.frame(x, ...)

Arguments

x	An object of class movetrack.
...	Unused; for compatibility with the generic method.

See Also

summary.movetrack()

---

Extract draws

Description

Extract draws from a movetrack object.

Usage

getDraws(fit, nsim = 50)

Arguments

fit	An object of class movetrack.
nsim	The number of simulations to extract; defaults to 50.

Value

A data.frame with the draws.

---

Estimate locations

Description

Calculate point estimates based on antenna bearing and signal strength.

Usage

locate(
  data,
  ID = "tagDeployID",
  ts = "ts",
  sig = "sig",
  aLon = "recvDeployLon",
  aLat = "recvDeployLat",
  aType = NULL,
  aBearing = "antBearing",
  aRange = 12,
  dTime = 2
)

Arguments

data	A data.frame containing the telemetry data.
ID	Unique identifier for individuals or tag deployments.
ts	Timestamp column.
sig	Signal strength column.
aLon	Antenna longitude column.
aLat	Antenna latitude column.
aType	Antenna type column, only required for antenna-specific detection ranges.
aBearing	Antenna bearing column.
aRange	Assumed maximum detection range of antennas in kilometres. Can be a single value or a named list of values for different antenna types.
dTime	Time interval in minutes for which point estimates are to be calculated.

Details

This function performs the following steps as described in Baldwin et al. 2018:

• Estimate locations for each detection: half of the maximum detection range aRange along the directional beam.

• Derive oscillating measurement error arising from antenna geometry and orientation.

• Calculate weighted means (by signal strength) for each time interval dTime.

Value

Returns a data.frame containing estimated coordinates and measurement errors for each time interval together with the proportions of time intervals w.

Examples

## Not run: 
data(motusData)
locate(motusData)
locate(motusData, dTime = 1, aRange = 10)
locate(motusData,
  aType = "antType", aRange = list("yagi-5" = 10, "yagi-6" = 12)
)

## End(Not run)

---

Map model result

Description

Map individual flight trajectories and model uncertainty.

Usage

mapTrack(fit, id = NULL, nsim = 50, lwd = 2, alpha = 0.1)

Arguments

fit	An object of class movetrack.
id	The individuals to plot. Defaults to NULL which plots all individuals.
nsim	The number of posterior draws to plot. Defaults to 50.
lwd	The line width for the mean trajectory. Defaults to 2.
alpha	The alpha value for the posterior draws. Defaults to 0.1.

Value

Returns an overview map with the mean trajectories and nsim posterior draws per individual.

Examples

## Not run: 
mapTrack(fit)
mapTrack(fit, nsim = 100, alpha = 0.05)

## End(Not run)

---

Motus test data

Description

Motus test data

Usage

motusData

Format

An object of class tbl_df (inherits from tbl, data.frame) with 4464 rows and 7 columns.

References

https://motus.org/

---

Plot model results

Description

Plot model results per individual and variable.

Usage

## S3 method for class 'movetrack'
plot(x, vars = c("lon", "lat"), id = NULL, ...)

Arguments

x	An object of class movetrack.
vars	The variables to plot. Defaults to c("lon", "lat").
id	The individuals to plot. Defaults to NULL which plots all individuals.
...	Additional arguments passed to movetrack::summary().

Value

Returns one or multiple ggplot plots.

See Also

movetrack::summary()

Examples

## Not run: 
# Set ggplot theme
theme_set(theme_bw(base_size = 20))

# Plot
plot(fit)
plot(fit, vars = "speed", prob = 0.89, ci = "ETI")

## End(Not run)

---

Print

Description

Print a summary for a movetrack object.

Usage

## S3 method for class 'movetrack'
print(x, digits = 3, ...)

Arguments

x	An object of class movetrack.
digits	The minimal number of significant digits; defaults to 3.
...	Additional arguments passed to print().

See Also

summary.movetrack()

---

Summary

Description

Create a summary of a movetrack object.

Usage

## S3 method for class 'movetrack'
summary(object, var = c("lon", "lat"), ci = "HDI", prob = 0.9, ...)

Arguments

object	An object of class movetrack.
var	The variable to summarise; defaults to 'lon'.
ci	The method used to calculate the credible intervals. Available options are 'HDI' for the highest posterior density interval and 'ETI' for the equal-tailed interval; defaults to 'HDI'.
prob	The probability mass of the credible interval; defaults to 0.9.
...	Unused; for compatibility with the generic method.

Value

A data.frame with the summary statistics.

Examples

## Not run: 
summary(fit)
summary(fit, var = "distance")
summary(fit, ci = "ETI", prob = 0.89)

## End(Not run)

---

Model data

Description

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

Usage

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().

Details

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

Value

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

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

See Also

cmdstanr::sample()

Examples

## Not run: 
# 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)

## End(Not run)

---