Description Usage Arguments Details Value
Correlated Random Walk Filter
1 2 3 4 |
data |
A dataframe representing the track (see details) |
subset |
Logical vector indicating which rows of the data frame be kept in the filtering |
tstep |
The time step to predict to (in days) |
nu |
The degrees of freedom parameter for the location errors |
gamma |
The autocorrelation parameter used to estimate initial parameters. |
span |
The span parameter for the loess fits used to estimate initial locations. |
verbose |
Enable tracing information |
optim |
The function used to minimize the negative log likelihood. |
extrap |
If TRUE, the final predicted state occurs immediately before the last observation, otherwise the final predicted state occurs immediately after the last observation. |
parameters |
The TMB parameter list. |
esf |
The error scale factors for the location classes. |
theta.zero |
Should theta be fixed at zero. |
common.tau |
Should a common tau parameter be fitted for longitude and latitude. |
Fit a correlated random walk to filter a track and predict locations on a regular time step.
The input track is given as a dataframe where each row is an observed location, with columns
observation time (as GMT POSIXct),
observed longitude,
observed latitude,
location class.
The TMB parameter list can be specified directly with the
parameters
argument. Otherwise suitable parameter values
are estimated from predicted locations estimated by fitting loess
smooths to the raw observations.
If extrap
is TRUE
, the last observations occur after
the last predicted location and the last fitted locations are
extrapolations, otherwise the final observations occur before the
final predicted locations and all fitted locations are
interpolated.
The filtering model assumes the errors in longitude and latitude
are proportional to scale factors determined by the location
class. The scale factors are speficied through the aes
argument. By default the function uses the same scaling factors
for location accuracy as used in crawl for ARGOS data.
a list with components
|
a dataframe of predicted locations |
|
a dataframe of fitted locations |
|
model parameter summary |
|
the input dataframe |
|
the input subset vector |
|
the prediction time step |
|
has theta been fixed at zero |
|
has a common tau been fitted for lon and lat |
|
the object returned by the optimizer |
|
the TMB object |
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