variogram.fit {ctmm} | R Documentation |
Visually fit a movement model to a variogram
Description
This function plots a variogram
object overlayed with a continuous-time movement model guesstimated from the variogram's shape. Sliders are given to adjust the parameter guesstimates and the result can be saved to a global variable. The intention of this function is to facilitate good starting guesses for ctmm.fit
, starting with a prototype hypothesis argument CTMM
, which can contain features such as isotropic
, range
, circle
, etc..
Usage
ctmm.guess(data,CTMM=ctmm(),variogram=NULL,name="GUESS",interactive=TRUE)
variogram.fit(variogram,CTMM=ctmm(),name="GUESS",fraction=0.5,interactive=TRUE,...)
Arguments
data |
A |
CTMM |
Optional model prototype or initial guesstimate of the model parameters, in |
name |
Name of the global variable to store the guesstimate in. |
interactive |
Boolean denoting whether to render the initial guess with interactive sliders or store the result silently. |
variogram |
A |
fraction |
Initial fraction of the variogram to render. |
... |
Optional parameters passed to |
Details
By default, sigma
is the asymptote of the variogram and tau
is an array of autocorrelation timescales. The position timescale is roughly the time lag it takes of the variogram to reach 63% of its asymptote. The velocity autocorrelation timescale visually corresponds to width of the concave bowl shape at the beginning of the variogram. If CTMM=ctmm(range=FALSE)
, sigma
is the asymptotic slope of the variogram and only the velocity timescale is finite.
By default, parameter values are estimated from the shape of the variogram. If this fails, the CTMM
option can provide alternative initial guesstimates.
variogram.fit
is called by ctmm.guess
, and there is usually no reason to call variogram.fit
directly.
Note
If the manipulate
package is unavailable, then interactive
is set to FALSE
.
Author(s)
C. H. Fleming.
See Also
ctmm.fit
, plot.variogram
, variogram
.
Examples
#Load package and data
library(ctmm)
data(buffalo)
#Extract movement data for a single animal
DATA <- buffalo$Cilla
# generate a visual fit of the variogram (requires RStudio or a guess object is returned)
ctmm.guess(DATA)