Simple Monte-Carlo goodness-of-fit tests for full-likelihood SECR models. The approach is to calculate a statistic from either the raw data or a fitted model, and to relate this to the distribution of the statistic under the original fitted model. The distribution is estimated by simulating data from the model, and possibly re-fitting the model to each simulated dataset.
The suitability of different test statistics has yet to be assessed.
An alternative and possibly more informative set of tests is provided
in function MCgof that implements the approach of
Choo et al. (2024) for a range of models.
secr.test(object, nsim = 99, statfn, fit = FALSE, seed =
NULL, ncores = NULL, tracelevel = 1, ...)An object of class c('secrtest','list') with components:
as input
as input
as input
as input
as input
list comprising the simulated values, the observed value(s), and estimated probabilities
For multi-session input when fit = FALSE, `output' is a list
in which each session provides one component.
Print and plot methods are provided for `secrtest' objects.
a fitted secr model
integer number of replicates
function to compute a numeric vector of one or more statistics from a single-session `capthist' object or from a fitted model (see Details)
logical; if TRUE the model is re-fitted to each simulated dataset
either NULL or an integer that will be used in a call to set.seed
integer number of threads for parallel processing
see sim.secr
other arguments passed to statfn, if needed
The test statistic(s) may be computed either on a dataset or on a
fitted model, as determined by the argument fit. The single
argument expected by statfn should be either a `capthist' object
(fit = FALSE) or an `secr' object (fit = TRUE).
The default statistic when fit = FALSE is the proportion of
individuals observed on only one occasion, which is equivalent to
statfn = function(CH) c(f1 = sum(apply(abs(CH) > 0,1,sum) == 1) /
nrow(CH)). Repeat detections on one occasion at the same or different
detectors are not counted. The default statistic is therefore not
appropriate for some data, specifically from `count' or `polygon'
detectors with few occasions or only one.
The default statistic when fit = TRUE is the deviance divided by
the residual degrees of freedom (i.e., statfn = function(object)
c(devdf = deviance(object) / df.residual(object))).
The reported probability (p) is the rank of the observed value in the
vector combining the observed value and simulated values, divided by
(nsim + 1). Ranks are computed with rank using the default
ties.method = "average".
Simulations take account of the usage attribute of detectors in the original capthist object, given that usage was defined and ignoreusage was not set.
Setting ncores = NULL uses the existing value from the environment variable
RCPP_PARALLEL_NUM_THREADS (see setNumThreads).
statfn may return a vector of statistics for each observed or
simulated dataset or model: then the value of each statistic will be
calculated for every simulated dataset, and summarised. If fit =
TRUE the vector of statistics may include both those computed on the
raw data (object$capthist) and the fitted model.
Choo, Y. R., Sutherland, C. and Johnston, A. (2024) A Monte Carlo resampling framework for implementing goodness-of-fit tests in spatial capture-recapture model Methods in Ecology and Evolution DOI: 10.1111/2041-210X.14386.
MCgof,
print.secrtest,
plot.secrtest,
simulate.secr,
sim.secr,
deviance.secr
if (FALSE) {
secr.test(secrdemo.0, nsim = 99)
secr.test(ovenbird.model.1, nsim = 20)
## example combining raw data summary and model fit
## assumes single-session
bothfn <- function(object) {
CH <- object$capthist
f1 <- sum(apply(abs(CH) > 0, 1, sum) == 1) / nrow(CH)
devdf <- deviance(object) / df.residual(object)
c(f1 = f1, devdf = devdf)
}
test <- secr.test (secrdemo.0, nsim = 19, statfn = bothfn, fit = TRUE)
test
plot(test, main = '')
}
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