Compute likelihood map from twilight

This functions estimate a likelihood map for each stationary period based on twilight data. The functions performs the following steps:

1. Perform a calibration on the known stationary period. See below for details

2. Compute a likelihood map for each twilight using the calibration.

3. Combine the likelihood maps of all twilights belonging to the same stationary periods with a log-linear pooling. See GeoPressureManual | Probability aggregation for more information on probability aggregation using log-linear pooling.

Usage

geolight_map(
  tag,
  twl_calib_adjust = 1.4,
  twl_llp = function(n) log(n)/n,
  compute_known = FALSE,
  quiet = FALSE
)

Arguments

tag

a GeoPressureR tag object.

twl_calib_adjust

smoothing parameter for the kernel density (see stats::kernel()).

twl_llp

log-linear pooling aggregation weight.

compute_known

logical defining if the map(s) for known stationary period should be estimated based on twilight or hard defined by the known location stap$known_l**

quiet

logical to hide messages about the progress

Value

a tag with the likelihood of light as tag$map_light

Calibration

Calibration requires to have a known position for a least one stationary periods. Use tag_set_map() to define the known position.

Instead of calibrating the twilight errors in terms of duration, we directly model the zenith angle error. We use a kernel distribution to fit the zenith angle during the known stationary period(s). The twl_calib_adjust parameter allows to manually adjust how smooth you want the fit of the zenith angle to be. Because the zenith angle error model is fitted with data from the calibration site only, and we are using it for all locations of the bird’s journey, it is safer to assume a broader/smoother distribution.

See also

Other geolight: twilight_create(), twilight_label_read(), twilight_label_write()

Examples

withr::with_dir(system.file("extdata", package = "GeoPressureR"), {
  # Read geolocator data and build twilight
  tag <- tag_create("18LX", quiet = TRUE) |>
    tag_label(quiet = TRUE) |>
    tag_set_map(
      extent = c(-16, 23, 0, 50),
      scale = 10,
      known = data.frame(
        stap_id = 1,
        known_lon = 17.05,
        known_lat = 48.9
      )
    )

  # Compute the twilight
  tag <- twilight_create(tag) |> twilight_label_read()

  # Compute likelihood map
  tag <- geolight_map(tag, quiet = TRUE)
})

plot(tag, type = "map_light")