Description Usage Arguments Details Value Warning See Also Examples
Computes the expected number of individuals detected across a detector layout or at each cluster of detectors.
1 2 
object 

session 
character session vector 
group 
group – for future use 
bycluster 
logical to output the expected number for clusters of detectors rather than whole array 
splitmask 
logical for computation method (see Details) 
ncores 
integer number of threads to be used for parallel processing 
The expected number of individuals detected is E(n) = integral p.(X) D(X) dX where the integration is a
summation over object$mask
. p.(X) is the probability an
individual at X will be detected at least once either on the
whole detector layout (bycluster = FALSE
) or on the detectors
in a single cluster (see pdot for more on p.). D(X)
is the expected density at X, given the model. D(X) is
constant (i.e. density surface flat) if object$CL == TRUE
or
object$model$D == ~1
, and for some other possible models.
If the bycluster
option is selected and detectors are not, in
fact, assigned to clusters then each detector will be treated as a
cluster, with a warning.
Setting ncores = NULL
uses the existing value from the environment variable
RCPP_PARALLEL_NUM_THREADS (see setNumThreads
).
By default, a full habitat mask is used for each cluster. This is the more robust option. Alternatively, the mask may be split into subregions defined by the cells closest to each cluster.
The calculation takes account of any fitted continuous model for spatial variation in density (note Warning).
The expected count (bycluster = FALSE) or a vector of expected counts, one per cluster. For multisession data, a list of such vectors.
This function changed slightly between 2.1.0 and 2.1.1, and now performs as indicated here when bycluster = TRUE and clusters are not specified.
Clusters of detectors are assumed to be independent (always true with detector types ‘proximity’, ‘count’ etc.). The computed E(n) does not apply when there is competition among clusters of detectors.
The prediction of density at present considers only the base level of density covariates, such as cellspecific habitat variables.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Not run:
expected.n(secrdemo.0)
expected.n(secrdemo.0, bycluster = TRUE)
expected.n(ovenbird.model.D)
## Clustered design
mini < make.grid(nx = 3, ny = 3, spacing = 50, detector =
"proximity")
tempgrids < trap.builder (cluster = mini , method = "all",
frame = expand.grid(x = seq(1000, 9000, 2000),
y = seq(1000, 9000, 2000)), plt = TRUE)
capt < sim.capthist(tempgrids, popn = list(D = 2))
tempmask < make.mask(tempgrids, buffer = 100,
type = "clusterbuffer")
fit < secr.fit(capt, mask = tempmask, trace = FALSE)
En < expected.n(fit, bycluster = TRUE)
## GoF or overdispersion statistic
p < length(fit$fit$par)
y < cluster.counts(capt)
## scaled by np
sum((y  En)^2 / En) / (length(En)p)
sum((y  En)^2 / En) / sum(y/En)
## End(Not run)

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