join {secr} | R Documentation

## 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, ...)

### 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.

### See Also

`MS.capthist`, `rbind.capthist`

### Examples

    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)

[Package secr version 3.2.1 Index]