diveMove (version 1.1)

bout-misc: Fit a Broken Stick Model on Log Frequency Data for identification of bouts of behaviour

Description

Application of methods described by Sibly et al. (1990) and Mori et al. (2001) for the identification of bouts of behaviour, based on sequential differences of a variable.

Usage

boutfreqs(x, bw, method=c("standard", "seq.diff"), plot=TRUE)
boutinit(lnfreq, x.break, plot=TRUE)
labelBouts(x, bec, bec.method=c("standard", "seq.diff"))
logit(p)
unLogit(logit)

Arguments

x

numeric vector on which bouts will be identified based on method. For labelBouts it can also be a matrix with different variables for which bouts should be identified.

bw

bin width for the histogram.

method, bec.method

method used for calculating the frequencies: standard simply uses x, while seq.diff uses the sequential differences method.

plot

logical, whether to plot results or not.

lnfreq

data frame with components lnfreq (log frequencies) and corresponding x (mid points of histogram bins).

x.break

x value defining the break point for broken stick model, such that x < xlim is 1st process, and x >= xlim is 2nd one.

bec

numeric vector or matrix with values for the bout ending criterion which should be compared against the values in x for identifying the bouts.

p

vector of proportions (0-1) to transform to the logit scale.

logit

Logit value to transform back to original scale.

Value

• boutfreqs returns a data frame with components lnfreq containing the log frequencies and x, containing the corresponding mid points of the histogram. Empty bins are excluded. A plot is produced as a side effect if argument plot is TRUE. See the Details section.

  boutinit returns a list with components a1, lambda1, a2, and lambda2, which are starting values derived from broken stick model. A plot is produced as a side effect if argument plot is TRUE.

  labelBouts returns a numeric vector sequentially labelling each row or element of x, which associates it with a particular bout.

  unLogit and logit return a numeric vector with the (un)transformed arguments.

Details

This follows the procedure described in Mori et al. (2001), which is based on Sibly et al. 1990. Currently, only a two process model is supported.

boutfreqs creates a histogram with the log transformed frequencies of x with a chosen bin width and upper limit. Bins following empty ones have their frequencies averaged over the number of previous empty bins plus one.

boutinit fits a "broken stick" model to the log frequencies modelled as a function of x (well, the midpoints of the binned data), using a chosen value to separate the two processes.

labelBouts labels each element (or row, if a matrix) of x with a sequential number, identifying which bout the reading belongs to.

logit and unLogit are useful for reparameterizing the negative maximum likelihood function, if using Langton et al. (1995).

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, 2001 138, 1451-1466.

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

See Also

bouts2.nls, bouts.mle.

Examples

data(divesSummary)
postdives <- divesSummary$postdive.dur[divesSummary$trip.no == 2]
## Remove isolated dives
postdives <- postdives[postdives < 2000]
lnfreq <- boutfreqs(postdives, bw=0.1, method="seq.diff", plot=FALSE)
boutinit(lnfreq, 50)