Description Usage Arguments Details Note Author(s) See Also Examples

View source: R/variogram.fit.R

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..

1 2 3 | ```
ctmm.guess(data,CTMM=ctmm(),variogram=NULL,name="GUESS",interactive=TRUE)
variogram.fit(variogram,CTMM=ctmm(),name="GUESS",fraction=0.5,interactive=TRUE,...)
``` |

`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 |

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.

If the `manipulate`

package is unavailable, then `interactive`

is set to `FALSE`

.

C. H. Fleming.

`ctmm.fit`

, `plot.variogram`

, `variogram`

.

1 2 3 4 5 6 7 8 9 10 11 12 |

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