bout-misc {diveMove} | R Documentation |
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.
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)
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. |
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).
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.
Sebastian P. Luque spluque@gmail.com
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.
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)