scenarioSummary: 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)

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.

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).

Examples

Run this code


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))