join: Combine or Split Sessions of capthist Object

Description

Make a single-session capthist object from a list of single-session objects, or a multi-session capthist object.

Usage

join(object, remove.dupl.sites = TRUE, tol = 0.001, sites.by.name = FALSE,
    drop.sites = FALSE, intervals = NULL, sessionlabels = NULL, 
    timevaryingcov = NULL) 
    
unjoin(object, intervals, ...)

Value

For join, a single-session capthist object. The vector attribute `intervals' records the distinction between occasions that are adjacent in the input (interval = 0) and those that are in consecutive sessions (e.g., interval = 1); `intervals' has length one less than the number of occasions.

For unjoin, a multi-session capthist object. Sessions are named with integers.

Arguments

object: list of single-session objects, or a multi-session capthist object [join], or a single-session capthist object [unjoin]
remove.dupl.sites: logical; if TRUE then a single record is retained for each trap site used in multiple input sessions
tol: absolute distance in metres within which sites are considered identical
sites.by.name: logical; if TRUE and remove.dupl.sites then duplicate sites are inferred from row names rather than x-y coordinates
drop.sites: logical; if TRUE then site information is discarded
intervals: vector of times between sessions (join) or occasions (unjoin; zero indicates same session)
sessionlabels: vector of session names
timevaryingcov: character vector of covariate names
...: other arguments passed to subset.capthist

Details

join

The input sessions are assumed to be of the same detector type and to have the same attributes (e.g., covariates should be present for all or none).

The number of occasions (columns) in the output is equal to the sum of the number of occasions in each input.

Duplicates may be defined either as sites within a given distance (tol) or sites with the same name (sites.by.name = TRUE). Using site names is faster.

For non-spatial analyses it is efficient to drop the third dimension and discard the traps attribute (drop.sites = TRUE).

A new dataframe of individual covariates is formed using the covariates for the first occurrence of each animal.

If timevaryingcov is given then for each name a new covariate is generated for each session and populated with values observed in that session, or NA if the animal was not detected. A `timevaryingcov' (list) attribute is created that associates each set of new session-specific columns with the corresponding old name, so that it may be used in formulae (see timevaryingcov).

Attributes xy and signal are handled appropriately, as is trap usage.

unjoin

The input grouping of occasions (columns) into sessions is specified via intervals. This is a vector of length one less than the number of occasions (columns) in object. Elements greater than zero indicate a new session.

The intervals argument may be omitted if object has a valid `intervals' attribute, as in the output from join.

Examples

Run this code


joined.ovenCH <- join (ovenCH)
summary(joined.ovenCH)
attr(joined.ovenCH, "intervals")

summary(unjoin(joined.ovenCH))

if (FALSE) {

## suppose the 5-year ovenbird covariates include a column for weight
## (here generated as random numbers)
for (i in 1:5) covariates(ovenCH[[i]])$wt <- runif(nrow(ovenCH[[i]]))
## construct single-session version of data for openCR
## identify 'wt' as varying across years
ovenCHj <- join(ovenCH, timevaryingcov = 'wt')
head(covariates(ovenCHj))
timevaryingcov(ovenCHj)
## Use example: openCR.fit(ovenCHj, model = p~wt)

}