dynBBMM | R Documentation |
Calculates dynamic Brownian Bridge Movement Model (dBBMM) for each track and transmitter. Tracks shorter than 30 minutes are automatically identified and not included in the analysis.
dynBBMM(
input,
base.raster,
tags = NULL,
start.time,
stop.time,
timeframe = NULL,
UTM,
debug = FALSE,
verbose = TRUE,
window.size = 7,
margin = 3
)
input |
The output of runRSP. |
base.raster |
The water raster of the study area. For example the output of |
tags |
Vector of transmitters to be analysed. By default all transmitters from runRSP will be analysed. |
start.time |
Sets the start point for analysis (format = "Y-m-d H:M:S"). |
stop.time |
Sets the stop point for analysis (format = "Y-m-d H:M:S"). |
timeframe |
Temporal window size for fine-scale dBBMM in hours. If left NULL, a single dBBMM is calculated for the whole period. |
UTM |
The UTM zone of the study area. Only relevant if a latlon-to-metric conversion is required. |
debug |
Logical: If TRUE, the function progress is saved to an RData file. |
verbose |
Logical: If TRUE, detailed check messages are displayed. Otherwise, only a summary is displayed. |
window.size |
The size of the moving window along the track. Larger windows provide more stable/accurate estimates of the brownian motion variance but are less well able to capture more frequent changes in behavior. This number has to be odd. |
margin |
The margin used for the behavioral change point analysis. This number has to be odd. |
List of calculated dBBMMs and metadata on each track used for the modelling.
# Import river shapefile
water <- actel::shapeToRaster(shape = paste0(system.file(package = "RSP"), "/River_latlon.shp"),
size = 0.0001, buffer = 0.05)
# Create a transition layer with 8 directions
tl <- actel::transitionLayer(x = water, directions = 8)
# Import example output from actel::explore()
data(input.example)
# Run RSP analysis
rsp.data <- runRSP(input = input.example, t.layer = tl, coord.x = "Longitude", coord.y = "Latitude")
# Run dynamic Brownian Bridge Movement Model (dBBMM)
dbbmm.data <- dynBBMM(input = rsp.data, base.raster = water, UTM = 56)
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