scenarioSummary: Summary of Scenarios

In secrdesign: Sampling Design for Spatially Explicit Capture-Recapture

Summary of Scenarios

Description

Compute various deterministic summaries for scenarios generated by make.scenarios

Usage

scenarioSummary(scenarios, trapset, maskset, xsigma = 4, nx = 64, CF = 1.0, 
    costing = FALSE, ..., ncores = 1)

Arguments

scenarios	dataframe of simulation scenarios
trapset	secr traps object or a list of traps objects
maskset	secr mask object or a list of mask objects (optional)
xsigma	numeric buffer width as multiple of sigma (alternative to maskset)
nx	integer number of cells in mask in x direction (alternative to maskset)
CF	numeric correction factor for rule-of-thumb RSE (see minnrRSE)
costing	logical; if TRUE then costings will be appended
...	arguments passed to costing
ncores	integer number of cores for parallel processing

Details

Not all scenarios from make.scenarios() are suitable. Grouped (multi-line) scenarios are excluded. Hazard detection functions are preferred (‘HHN’, ‘HHR’, ‘HEX’, ‘HAN’, ‘HCG’). ‘HN’, ‘HR’ and ‘EX’ are converted approximately to ‘HHN’, ‘HHR’ and ‘HEX’ respectively, with a warning; other functions are rejected.

CF may be a vector of values that is recycled across the components of trapset. The correction factor is a multiplier applied after all other calculations.

The approximate RSE(D-hat) is rotRSE = CF/ sqrt(min(E(n), E(r))). This assumes n is Poisson-distributed. For binomial n an ad hoc adjustment is rotRSEB = sqrt(rotRSE^2 - 1 / (D x A)) where A is the mask area.

The default ncores = 1 (new in 2.7.0) is usually faster than setting ncores>1 because of the overheads in setting up a parallel cluster.

The ... argument is for inputs to costing, including unitcost (required) and routelength (optional).

Value

A dataframe including the first 8 columns from scenarios and the computed columns –

En	expected number of individuals
Er	expected number of recaptures
Em	expected number of movement recaptures
En2	expected number of individuals detected at two or more sites
esa	effective sampling area (ha)
CF	rule-of-thumb correction factor
rotRSE	rule-of-thumb relative standard error of density estimate
rotRSEB	rotRSE with adjustment for fixed N in region defined by mask (i.e. Binomial n rather than Poisson n)
arrayN	number of detectors in each array
arrayspace	array spacing in sigma units
arrayspan	largest dimension of array in sigma units
saturation	expected proportion of detectors at which detection occurs (trap success)
travel	travel cost
arrays	cost of each repeated array
detectors	fixed cost per detector
visits	cost per detector per visit
detections	cost per detection
totalcost	summed costs
detperHR	median number of detectors per 95% home range

Costings (the last 6 columns) are omitted if costing = FALSE.

See Also

make.scenarios, Enrm, costing, minnrRSE

Examples

scen <- make.scenarios(D = c(5,10), sigma = 25, lambda0 = 0.2, detectfn = 'HHN')
grid <- make.grid(6,6, detector = 'multi')
scenarioSummary(scen, list(grid), costing = TRUE, unitcost = list(perkm = 10))

secrdesign documentation built on March 31, 2023, 10:25 p.m.