diveMove (version 1.6.2)

labelBouts,numeric-method: Label each vector element or matrix row with bout membership number

Description

Identify which bout an observation belongs to.

Usage

# S4 method for numeric
labelBouts(obj, becs, bec.method = c("standard", "seq.diff"))
# S4 method for Bouts
labelBouts(obj, becs, bec.method = c("standard", "seq.diff"))

Value

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.

Arguments

obj

Object of class Bouts object, or numeric vector or matrix with independent data modelled as a Poisson process mixture.

becs

numeric vector or matrix with values for the bout ending criterion which should be compared against the values in x for identifying the bouts. It needs to have the same dimensions as x to allow for situations where bec is within x.

bec.method

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

Methods (by class)

• numeric: Label data on vector or matrix objects.

• Bouts: Label data on Bouts object

Examples

## Run example to retrieve random samples for two- and three-process
## Poisson mixtures with known parameters as 'Bouts' objects
## ('xbouts2', and 'xbouts3'), as well as starting values from
## broken-stick model ('startval2' and 'startval3')
utils::example("boutinit", package="diveMove", ask=FALSE)

## 2-process
opts0 <- list(method="L-BFGS-B", lower=c(-2, -5, -10))
opts1 <- list(method="L-BFGS-B", lower=c(1e-1, 1e-3, 1e-6))
bouts2.fit <- fitMLEbouts(xbouts2, start=startval2, optim_opts0=opts0,
                          optim_opts1=opts1)
bec2 <- bec(bouts2.fit)
## labelBouts() expects its second argument to have the same
## dimensions as the data
labelBouts(xbouts2, becs=rep(bec2, length(xbouts2@x)))