View source: R/graph_set_movement.R
graph_set_movement | R Documentation |
graph
Configure the movement model of a graph
by defining the value of the parameters needed to build
the transition graph_transition()
through speed2prob()
.
Three methods are currently implemented with two parametric function "gamma"
and "logis"
suitable when wind data is not available and are thus defining the probability of a groundspeed.
If wind data is available, it is recommended to use the "power"
method which rely on the power
curve equation (energy vs airspeed) to estimate the probability of a airspeed. Read more about
this approach in section 2.2.5. of Nussbaumer et al. (2023b)
graph_set_movement(
graph,
type = ifelse("ws" %in% names(graph), "as", "gs"),
method = ifelse("ws" %in% names(graph), "power", "gamma"),
shape = 7,
scale = 7,
location = 40,
bird = NULL,
power2prob = function(power) (1/power)^3,
low_speed_fix = 15,
zero_speed_ratio = 1
)
graph |
a GeoPressureR |
type |
Ground speed |
method |
method used to convert the speed to probability ("gamma", "logis" or "power") |
shape |
parameter of the gamma distribution (km/h) |
scale |
parameter of the gamma and logistic distribution (km/h) |
location |
parameter for the logistic distribution (km/h) |
bird |
A GeoPressureR |
power2prob |
function taking power as a single argument and returning a probability |
low_speed_fix |
speed below which the probability remains the same, i.e. we assign the same
probability at |
zero_speed_ratio |
multiplicative ratio of the probability for speed zero. This ratio apply only when the bird is stayin at the same location (fly and come back or stay within pixel size). This parameter (when greater than 1) is used to favour a bird to stay at the same location rather than perform short fly. |
Graph list with a new list graph$movement
storing all the parameters needed to compute
the transition probability
Nussbaumer, Raphaël, Mathieu Gravey, Martins Briedis, Felix Liechti, and Daniel Sheldon. 2023. Reconstructing bird trajectories from pressure and wind data using a highly optimized hidden Markov model. Methods in Ecology and Evolution, 14, 1118–1129 https://doi.org/10.1111/2041-210X.14082.
Other graph:
graph_create()
,
graph_marginal()
,
graph_most_likely()
,
graph_simulation()
,
print.graph()
Other movement:
bird_create()
,
graph_transition()
,
plot_graph_movement()
,
speed2prob()
,
tag_download_wind()
withr::with_dir(system.file("extdata", package = "GeoPressureR"), {
tag <- tag_create("18LX", quiet = TRUE) |>
tag_label(quiet = TRUE) |>
twilight_create() |>
twilight_label_read() |>
tag_set_map(
extent = c(-16, 23, 0, 50),
known = data.frame(stap_id = 1, known_lon = 17.05, known_lat = 48.9)
) |>
geopressure_map(quiet = TRUE) |>
geolight_map(quiet = TRUE)
})
graph <- graph_create(tag, quiet = TRUE)
graph <- graph_set_movement(graph,
method = "gamma",
shape = 4,
scale = 6,
low_speed_fix = 10
)
plot_graph_movement(graph)
graph <- graph_set_movement(graph,
method = "logis",
shape = 4,
location = 60,
low_speed_fix = 10
)
plot_graph_movement(graph)
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