diveMove (version 1.4.1)

bout-methods: Methods for Plotting and Extracting the Bout Ending Criterion

Description

Plot results from fitted mixture of 2-process Poisson models, and calculate the bout ending criterion.

Usage

"plotBouts"(fit, ...)
"plotBouts"(fit, x, ...)
"bec2"(fit)
"bec2"(fit)
"bec3"(fit)

Arguments

fit

nls or mle object.

x

numeric object with variable modelled.

...

Arguments passed to the underlying plotBouts2.nls and plotBouts2.mle.

General Methods

plotBouts

signature(fit="nls"): Plot fitted 2- or 3-process model of log frequency vs the interval mid points, including observed data.

plotBouts

signature(x="mle"): As the nls method, but models fitted through maximum likelihood method. This plots the fitted model and a density plot of observed data.

bec2

signature(fit="nls"): Extract the estimated bout ending criterion from a fitted 2-process model.

bec2

signature(fit="mle"): As the nls method, but extracts the value from a maximum likelihood model.

bec3

signature(fit="nls"): Extract the estimated bout ending criterion from a fitted 3-process model.

References

Berdoy, M. (1993) Defining bouts of behaviour: a three-process model. Animal Behaviour 46, 387-396.

Langton, S.; Collett, D. and Sibly, R. (1995) Splitting behaviour into bouts; a maximum likelihood approach. Behaviour 132, 9-10.

Luque, S. P. and Guinet, C. (2007) A maximum likelihood approach for identifying dive bouts improves accuracy, precision, and objectivity. Behaviour 144, 1315-1332.

Mori, Y.; Yoda, K. and Sato, K. (2001) Defining dive bouts using a sequential differences analysis. Behaviour 138, 1451-1466.

Sibly, R.; Nott, H. and Fletcher, D. (1990) Splitting behaviour into bouts. Animal Behaviour 39, 63-69.

See Also

bouts.mle, bouts2.nls, bouts3.nls for examples.