secr.test: Goodness-of-Fit Test

Description

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.

Usage

secr.test(object, nsim = 99, statfn, fit = FALSE, seed =
NULL, ncores = NULL, tracelevel = 1, ...)

Value

An object of class c('secrtest','list') with components:

object: as input
nsim: as input
statfn: as input
fit: as input
seed: as input
output: 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.

Arguments

object: a fitted secr model
nsim: integer number of replicates
statfn: function to compute a numeric vector of one or more statistics from a single-session `capthist' object or from a fitted model (see Details)
fit: logical; if TRUE the model is re-fitted to each simulated dataset
seed: either NULL or an integer that will be used in a call to set.seed
ncores: integer number of threads for parallel processing
tracelevel: see sim.secr
...: other arguments passed to statfn, if needed

Details

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.

Examples

Run this code


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 = '')

}