scenarioSummary {secrdesign}

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

k

overlap index k = \sigma \sqrt D / 100 from secr kfn

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))