run.particle.filter {FLightR} | R Documentation |
Run Particle Filter
Description
Main function of FLightR, it takes fully prepared object created by make.prerun.object
and produces a result object that can be used for plotting etc.
Usage
run.particle.filter(
all.out,
cpus = NULL,
threads = -1,
nParticles = 1e+06,
known.last = TRUE,
precision.sd = 25,
behav.mask.low.value = 0,
k = NA,
plot = TRUE,
cluster.type = "PSOCK",
a = 45,
b = 1500,
L = 90,
adaptive.resampling = 0.99,
check.outliers = FALSE,
sink2file = FALSE,
add.jitter = FALSE
)
Arguments
all.out |
An object created by |
cpus |
another way to specify |
threads |
An amount of threads to use while running in parallel. default is -1. if value 1 submitted package will run sequentially |
nParticles |
total amount of particles to be used with the run. 10 000 (1e4) is recommended for the preliminary run and 1 000 000 (1e6) for the final |
known.last |
Set to FALSE if your bird was not at a known place during last twilight in the data |
precision.sd |
if |
behav.mask.low.value |
Probability value that will be used instead of 0 in the behavioural mask. If set to 1 behavioural mask will not be active anymore |
k |
Kappa parameter from vonMises distribution. Default is NA, otherwise will generate particles in a direction of a previous transitions with kappa = k |
plot |
Should function plot preliminary map in the end of the run? |
cluster.type |
see help to package parallel for details |
a |
minimum distance that is used in the movement model - left boundary for truncated normal distribution of distances moved between twilights. Default is 45 for as default grid has a minimum distance of 50 km. |
b |
Maximum distance allowed to fly between two consecutive twilights |
L |
how many consecutive particles to resample |
adaptive.resampling |
Above what level of ESS resampling should be skipped |
check.outliers |
switches ON the online outlier routine |
sink2file |
will write run details in a file instead of showing on the screen |
add.jitter |
will add spatial jitter inside a grid cell for the median estimates |
Value
FLightR object, containing output and extracted results. It is a list with the following elements
Indices |
List with prior information and indices |
Spatial |
Spatial data - Grid, Mask, spatial likelihood |
Calibration |
all calibration parameters |
Data |
original data |
Results |
The main results object. Main components of it are
|
Author(s)
Eldar Rakhimberdiev
Examples
File<-system.file("extdata", "Godwit_TAGS_format.csv", package = "FLightR")
# to run example fast we will cut the real data file by 2013 Aug 20
Proc.data<-get.tags.data(File, end.date=as.POSIXct('2013-07-02', tz='GMT'))
Calibration.periods<-data.frame(
calibration.start=NA,
calibration.stop=as.POSIXct("2013-08-20", tz='GMT'),
lon=5.43, lat=52.93)
#use c() also for the geographic coordinates, if you have more than one calibration location
# (e. g., lon=c(5.43, 6.00), lat=c(52.93,52.94))
print(Calibration.periods)
# NB Below likelihood.correction is set to FALSE for fast run!
# Leave it as default TRUE for real examples
Calibration<-make.calibration(Proc.data, Calibration.periods, likelihood.correction=FALSE)
Grid<-make.grid(left=0, bottom=50, right=10, top=56,
distance.from.land.allowed.to.use=c(-Inf, Inf),
distance.from.land.allowed.to.stay=c(-Inf, Inf))
all.in<-make.prerun.object(Proc.data, Grid, start=c(5.43, 52.93),
Calibration=Calibration, threads=2)
# here we will run only 1e4 partilces for a very short track.
# One should use 1e6 particles for the full run.
Result<-run.particle.filter(all.in, threads=1,
nParticles=1e3, known.last=TRUE,
precision.sd=25, check.outliers=FALSE)