capthist object to create a new
data set, possibly converting between detector types## S3 method for class 'capthist':
reduce(object, columns = NULL, outputdetector =
detector(traps(object)), select='last', dropunused = TRUE,
verify = TRUE, sessions = NULL, ...)capthist objectverify function is applied to the outputcapthist with number of columns equal to
length(occasions). The detector type is inherited from object
unless a new type is specified with the argument outputdetector.columns defines the columns of
object for new occasion 1, the second for new occasion 2, etc. If
columns is NULL then all occasions are output. Subscripts in a
component of columns that do not match an occasion in the input
are ignored. When the output detector is one of the trap types
('single', 'multi'), reducing capture occasions can result in locational
ambiguity for individuals caught on more than one occasion, and for
single-catch traps there may also be conflicts between individuals at
the same trap. The method for resolving conflicts among 'multi'
detectors is determined by select which should be one of 'first',
'last' or 'random'. With 'single' detectors select is ignored and
the method is: first, randomly select* one trap per animal per day;
second, randomly select* one animal per trap per day; third, when
collapsing multiple days use the first capture, if any, in each
trap.
Usage data in the traps attribute are also
pooled if present; a trap is considered 'used' on a pooled occasion if
it was used on any contributing occasion.
* i.e., in the case of a single capture, use that capture; in the case of
multiple 'competing' captures draw one at random.capthist, subset.capthisttempcapt <- sim.capthist (make.grid(nx = 6, ny = 6), nocc = 6)
class(tempcapt)
pooled.tempcapt <- reduce(tempcapt, col = list(1,2:3,4:6))
summary (pooled.tempcapt)Run the code above in your browser using DataLab