make.capthist: Construct capthist Object

In secr: Spatially Explicit Capture-Recapture

Construct capthist Object

Description

Form a capthist object from a data frame of capture records and a traps object.

Usage

make.capthist(captures, traps, fmt = c("trapID", "XY"), noccasions = NULL,
    covnames = NULL, bysession = TRUE, sortrows = TRUE,
    cutval = NULL, tol = 0.01, snapXY = FALSE, noncapt = "NONE", signalcovariates)

Arguments

captures	dataframe of capture records in one of two possible formats (see Details)
traps	object of class traps describing an array of passive detectors
fmt	character string for capture format.
noccasions	number of occasions on which detectors were operated
covnames	character vector of names for individual covariate fields
bysession	logical, if true then ID are made unique by session
sortrows	logical, if true then rows are sorted in ascending order of animalID
cutval	numeric, threshold of signal strength for ‘signal’ detector type
tol	numeric, snap tolerance in metres
snapXY	logical; if TRUE then fmt = 'XY' uses nearest trap within tol for non-polygon detectors
noncapt	character value; animal ID used for ‘no captures’
signalcovariates	character vector of field names from ‘captures’

Details

make.capthist is the most flexible way to prepare data for secr.fit. See read.capthist for a more streamlined way to read data from text files for common detector types. Each row of the input data frame captures represents a detection on one occasion. The capture data frame may be formed from a text file with read.table.

Input formats are based on the Density software (Efford 2012; see also secr-datainput.pdf). If fmt = "XY" the required fields are (session, ID, occasion, x, y) in that order. If fmt = "trapID" the required fields are (session, ID, occasion, trap), where trap is the numeric index of the relevant detector in traps. session and ID may be character-, vector- or factor-valued; other required fields are numeric. Fields are matched by position (column number), not by name. Columns after the required fields are interpreted as individual covariates that may be continuous (e.g., size) or categorical (e.g., age, sex).

If captures has data from multiple sessions then traps may be either a list of traps objects, one per session, or a single traps object that is assumed to apply throughout. Similarly, noccasions may be a vector specifying the number of occasions in each session.

Covariates are assumed constant for each individual; the first non-missing value is used. The length of covnames should equal the number of covariate fields in captures.

bysession takes effect when the same individual is detected in two or more sessions: TRUE results in one capture history per session, FALSE has the effect of generating a single capture history (this is not appropriate for the models currently provided in secr).

Deaths are coded as negative values in the occasion field of captures. Occasions should be numbered 1, 2, ..., noccasions. By default, the number of occasions is the maximum value of ‘occasion’ in captures.

Signal strengths may be provided in the fifth (fmt = trapID) or sixth (fmt = XY) columns. Detections with signal strength missing (NA) or below ‘cutval’ are discarded.

A session may result in no detections. In this case a null line is included in captures using the animal ID field given by noncapt, the maximum occasion number, and any trapID (e.g. "sess1 NONE 5 1" for a 5-occasion session) (or equivalently "sess1 NONE 5 10 10" for fmt = XY).

Nonspatial data (Session, AnimalID, Occasion and possibly individual covariates) may be entered by omitting the ‘traps’ argument or setting it to NULL.

Value

An object of class capthist (a matrix or array of detection data with attributes for detector positions etc.). For ‘single’ and ‘multi’ detectors this is a matrix with one row per animal and one column per occasion (dim(capthist)=c(nc,noccasions)); each element is either zero (no detection) or a detector number (the row number in traps not the row name). For ‘proximity’ detectors capthist is an array of values {-1, 0, 1} and dim(capthist)=c(nc,noccasions,ntraps). The number of animals nc is determined from the input, as is noccasions if it is not specified. traps, covariates and other data are retained as attributes of capthist.

Deaths during the experiment are represented as negative values in capthist.

For ‘signal’ and ‘signalnoise’ detectors, the columns of captures identified in signalcovariates are saved along with signal strength measurements in the attribute ‘signalframe’.

If the input has data from multiple sessions then the output is an object of class c("capthist", "list") comprising a list of single-session capthist objects.

Note

make.capthist requires that the data for captures and traps already exist as R objects. To read data from external (text) files, first use read.table and read.traps, or try read.capthist for a one-step solution.

Prior to secr 4.4.0, occasional valid records for "multi" and "single" detectors were rejected as duplicates.

From secr 4.5.0, ‘snapXY’ works for transects as well as point detectors.

References

Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://www.otago.ac.nz/density/.

See Also

capthist, traps, read.capthist, secr.fit, sim.capthist

Examples

## peek at demonstration data
head(captXY)
head(trapXY)

demotraps <- read.traps(data = trapXY)
demoCHxy  <- make.capthist (captXY, demotraps, fmt = "XY")

demoCHxy            ## print method for capthist
plot(demoCHxy)      ## plot method for capthist
summary(demoCHxy)   ## summary method for capthist


## To enter `count' data without manually repeating rows
## need a frequency vector f, length(f) == nrow(captXY)
n <- nrow(captXY)
f <- sample (1:5, size = n, prob = rep(0.2,5), replace = TRUE)
## repeat rows as required...
captXY <- captXY[rep(1:n, f),]
counttraps <- read.traps(data = trapXY, detector = "count")
countCH  <- make.capthist (captXY, counttraps, fmt = "XY")

secr documentation built on Nov. 4, 2024, 9:06 a.m.