join: Combine or Split Sessions of capthist Object

Description Usage Arguments Details Value Note See Also Examples

Description

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

Usage

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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, ...)

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.

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.

Note

Do not confuse unjoin with split.capthist which splits by row (animal) rather than by column (occasion).

Occasions survive intact; to pool occasions use reduce.capthist.

join was modified in version 2.9.5 to check whether the components of ‘object’ all used the same detectors (‘traps’) (putting aside differences in usage). If the traps are identical and remove.dupl.sites = TRUE then the resulting ‘capthist’ uses the common list of detectors, with a usage attribute formed by concatenating the usage columns of the input. This is faster than the previous filtering algorithm using ‘tol’; the older algorithm is still used if the traps differ.

Problems may be encountered with large datasets. These may be alleviated by setting sites.by.name = TRUE (if matching sites have matching names, avoiding the need for coordinate matching) or drop.sites = TRUE (if only non-spatial data are required for openCR).

See Also

MS.capthist, rbind.capthist

Examples

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

summary(unjoin(joined.ovenCH))

## Not run: 

## 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)


## End(Not run)

secr documentation built on July 12, 2021, 9:07 a.m.