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Treat location data with a continuous-time state-space model

Source: R/location_treatment.R
location_treatment.Rd

Using fit_ssm function from aniMotum package, this function "clean" the location data to be used for further analysis at the dive scale.

Usage

location_treatment(
  data,
  model = "crw",
  time.step = 1,
  vmax = 3,
  with_plot = FALSE,
  export = NULL
)

Arguments

data

Dataset of observation, usually the file \*Argos.csv or \*Location.csv files

model

Choose to fit either a simple random walk ("rw") or correlated random walk ("crw") as a continuous-time process model

time.step

options: 1) the regular time interval, in hours, to predict to; 2) a vector of prediction times, possibly not regular, must be specified as a data.frame with id and POSIXt dates; 3) NA - turns off prediction and locations are only estimated at observation times.

vmax

The max travel rate (m/s) passed to sda to identify outlier locations

with_plot

A diagnostic plot

export

To export the new generated dataset

Value

A dataset with the new location data

References

run_aniMotum_generic.R (tkeates@ucsc.edu)

https://ianjonsen.github.io/aniMotum/

See also

fit_ssm

Examples

# load library
library(aniMotum)
library(data.table)

# run this function on sese dataset included in aniMotum package
output <- location_treatment(copy(sese), with_plot = TRUE)
#> 
#> 
#> 
#>