bout-methods {diveMove}R Documentation

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

## S4 method for signature 'nls':
plotBouts(fit, ...)
## S4 method for signature 'mle':
plotBouts(fit, x, ...)
## S4 method for signature 'nls':
bec2(fit)
## S4 method for signature 'mle':
bec2(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-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.

Author(s)

Sebastian P. Luque spluque@gmail.com

References

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 for examples.


[Package diveMove version 0.9.5 Index]