Computes the expected number of individuals detected across a detector layout or at each cluster of detectors.
character session vector
group – for future use
logical to output the expected number for clusters of detectors rather than whole array
logical for computation method (see Details)
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
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.
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.
ncores = NULL uses the existing value from the environment variable
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 multi-session 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 cell-specific habitat variables.
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## 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 n-p sum((y - En)^2 / En) / (length(En)-p) sum((y - En)^2 / En) / sum(y/En) ## End(Not run)
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