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

This function returns a list of biologically interesting parameters in human readable format, as derived from a continuous-time movement model.

1 2 |

`object` |
A |

`level` |
Confidence level for parameter estimates. |

`level.UD` |
Confidence level for the Gaussian home-range area. |

`units` |
Convert result to natural units. |

`IC` |
Information criteria for sorting lists of |

`...` |
Unused options. |

If summary is called with a single `ctmm`

object output from `ctmm.fit`

, then a list is returned with effective sample size array `DOF`

and parameter estimate table `CI`

, with low, maximum likelihood, and high estimates for the following possible parameters:

`tau`

The autocorrelation timescales.

`area`

The Gaussian home-range area, where the point estimate has a significance level of

`level.UD`

. I.e., the core home range is where the animal is located 50% of the time with`level.UD=0.50`

. This point estimate itself is subject to uncertainty, and is given confidence intervals derived from`level`

.`speed`

The Gaussian root-mean-square (RMS) velocity, which is a convenient measure of average speed but not the conventional measure of average speed (see

`speed`

).

If summary is called on a list of `ctmm`

objects output from `ctmm.select`

, then a table is returned with the model names and AIC differences, where "IID" denotes the uncorrelated bi-variate Gaussian model, "OU" denotes the continuous-position Ornstein-Uhlenbeck model, and "OUF" denotes the continuous-velocity Ornstein-Uhlenbeck-F model.

Confidence intervals on the autocorrelation timescales assume they are sufficiently greater than zero and less than infinity.

In `ctmm`

v0.3.4 the speed estimate was fixed to be the RMS velocity and not *1/√{2}* times the RMS velocity.

C. H. Fleming.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Load package and data
library(ctmm)
data(buffalo)
# Extract movement data for a single animal
Cilla <- buffalo$Cilla
# fit model
GUESS <- ctmm.guess(Cilla,interactive=FALSE)
FIT <- ctmm.fit(Cilla,GUESS)
# Tell us something interpretable
summary(FIT)
``` |

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