Description Usage Arguments Details Value Author(s) References See Also Examples
Functions to model a mixture of 3 random Poisson processes to histogram-like data of log frequency vs interval mid points. This follows Sibly et al. (1990) method, adapted for a three-process model by Berdoy (1993).
1 2 3 4 | bouts3.nlsFUN(x, a1, lambda1, a2, lambda2, a3, lambda3)
bouts3.nls(lnfreq, start, maxiter)
bouts3.nlsBEC(fit)
plotBouts3.nls(fit, lnfreq, bec.lty, ...)
|
x |
numeric vector with values to model. |
a1, lambda1, a2, lambda2, a3, lambda3 |
numeric: parameters from the mixture of Poisson processes. |
lnfreq |
|
start, maxiter |
Arguments passed to |
fit |
nls object. |
bec.lty |
Line type specification for drawing the BEC reference line. |
... |
Arguments passed to |
bouts3.nlsFUN
is the function object defining the nonlinear
least-squares relationship in the model. It is not meant to be used
directly, but is used internally by bouts3.nls
.
bouts3.nls
fits the nonlinear least-squares model itself.
bouts3.nlsBEC
calculates the BEC from a list object, as the one
that is returned by nls
, representing a fit of the
model. plotBouts3.nls
plots such an object.
bouts3.nlsFUN
returns a numeric vector evaluating the mixture of 3
Poisson process.
bouts3.nls
returns an nls object resulting from fitting this
model to data.
bouts3.nlsBEC
returns a number corresponding to the bout ending
criterion derived from the model.
plotBouts3.nls
plots the fitted model with the corresponding
data.
Sebastian P. Luque spluque@gmail.com
Sibly, R.; Nott, H. and Fletcher, D. (1990) Splitting behaviour into bouts. Animal Behaviour 39, 63-69.
Berdoy, M. (1993) Defining bouts of behaviour: a three-process model. Animal Behaviour 46, 387-396.
bouts.mle
for a better approach;
boutfreqs
; boutinit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ## Using the Example from '?diveStats':
utils::example("diveStats", package="diveMove",
ask=FALSE, echo=FALSE)
## Postdive durations
postdives <- tdrX.tab$postdive.dur
postdives.diff <- abs(diff(postdives))
## Remove isolated dives
postdives.diff <- postdives.diff[postdives.diff < 4000]
## Construct histogram
lnfreq <- boutfreqs(postdives.diff, bw=0.1, plot=FALSE)
startval <- boutinit(lnfreq, c(50, 400))
## Drop names by wrapping around as.vector()
startval.l <- list(a1=as.vector(startval[[1]]["a"]),
lambda1=as.vector(startval[[1]]["lambda"]),
a2=as.vector(startval[[2]]["a"]),
lambda2=as.vector(startval[[2]]["lambda"]),
a3=as.vector(startval[[3]]["a"]),
lambda3=as.vector(startval[[3]]["lambda"]))
## Fit the 3 process model
bout.fit <- bouts3.nls(lnfreq, start=startval.l, maxiter=500)
summary(bout.fit)
plotBouts(bout.fit)
## Estimated BEC
bec3(bout.fit)
|
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