capthist object from a data frame of capture records and a traps object.make.capthist(captures, traps, fmt = "trapID", noccasions = NULL,
covnames = NULL, bysession = TRUE, sortrows = TRUE,
cutval = NULL, tol = 0.01)traps describing an array of passive detectorscapthist (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.
If the input has data from multiple sessions then the output is an
object of class c('list','capthist') comprising a list of single-session
capthist objects.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 2009; see also
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 captures. Occasions should be numbered 1, 2, ..., noccasions. By
default, the number of occasions is the maximum value of 'occasion' in
captures.capthist, traps,
read.capthist, secr.fit,
sim.capthist## load demonstration data and peek at XY data
data(secrdemo)
captXY[1:5,]
trapXY[1:5,]
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 capthistRun the code above in your browser using DataLab