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

These functions cluster and classify individual movement models and related estimates, including AKDE home-range areas, while taking into account estimation uncertainty.

1 2 |

`x` |
A list of |

`level` |
Confidence level for parameter estimates. |

`level.UD` |
Coverage level for home-range estimates. E.g., 50% core home range. |

`debias` |
Apply Bessel's inverse-Gaussian correction and various other bias corrections. |

`IC` |
Information criterion to determine whether or not population variation can be estimated. Can be |

`units` |
Convert result to natural units. |

`plot` |
Generate a meta-analysis forest plot with two means. |

`sort` |
Sort individuals by their point estimates in forest plot. |

`...` |
Further arguments passed to |

So-far only the clustering of home-range areas is implemented. More details will be provided in an upcomming manuscript.

A `list`

with elements `P`

and `CI`

,
where `P`

is an array of individual membership probabilities for sub-population 1,
and `CI`

is a table with rows corresponding to the sub-population means, coefficients of variation, and membership probabilities, and the ratio of sub-population means.

The AICc formula is approximated via the Gaussian relation.

C. H. Fleming.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
# load package and data
library(ctmm)
data(buffalo)
# fit movement models
FITS <- AKDES <- list()
for(i in 1:length(buffalo))
{
GUESS <- ctmm.guess(buffalo[[i]],interactive=FALSE)
# use ctmm.select unless you are certain that the selected model is OUF
FITS[[i]] <- ctmm.fit(buffalo[[i]],GUESS,trace=2)
}
# calculate AKDES on a consistent grid
AKDES <- akde(buffalo,FITS,trace=1)
# color to be spatially distinct
COL <- color(AKDES,by='individual')
# plot AKDEs
plot(AKDES,col.DF=COL,col.level=COL,col.grid=NA,level=NA)
# cluster-analysis of buffalo
cluster(AKDES,sort=TRUE)
``` |

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