estgGenericSize {GenEst}

Estimate generic detection probability for multiple carcass classes

Description

Generic g estimation for a combination of SE model and CP model under a given search schedule

The g estimated by estgGenericSize is a generic aggregate detection probability and represents the probability of detecting a carcass that arrives at a (uniform) random time during the period monitored, for each of the possible cell combinations, given the SE and CP models. This is somethat different from the GenEst estimation of g when the purpose is to estimate total mortality (M), in which case the detection probability varies with carcass arrival interval and is difficult to summarize statistically. The estgGeneric estimate is a useful "big picture" summary of detection probability, but would be difficult to work with for estimating M with precision.

Usage

estgGenericSize(
  days,
  modelSetSize_SE,
  modelSetSize_CP,
  modelSizeSelections_SE,
  modelSizeSelections_CP,
  nsim = 1000
)

Arguments

days

Search schedule data as a vector of days searched

modelSetSize_SE

Searcher Efficiency model set for multiple sizes

modelSetSize_CP

Carcass Persistence model set for multiple sizes

modelSizeSelections_SE

vector of SE models to use, one for each size. Size names are required, and names must match those of modelSetSize_SE. E.g., c(lrg = "p ~ Visibility; k ~ 1", sml = "p ~ 1; k ~ 1"). Model formulas are read as text and must have exact matches among models listed in modelSetSize_SE. For example, if one of the modelSizeSelections_SE elements is lrg = "p ~ Visibility; k ~ 1", then "p ~ Visibility; k ~ 1" must be in names(modelSizeSelections_SE)[["lrg"]].

modelSizeSelections_CP

vector of CP models to use, one for each size

nsim

the number of simulation draws

Value

list of g estimates, with one element in the list corresponding to each of the cells from the cross-model combination

Examples

  data(mock)
  pkmModsSize <- pkm(formula_p = p ~ HabitatType,
                   formula_k = k ~ HabitatType, data = mock$SE,
                   obsCol = c("Search1", "Search2", "Search3", "Search4"),
                   sizeCol = "Size", allCombos = TRUE)
  cpmModsSize <- cpm(formula_l = l ~ Visibility,
                   formula_s = s ~ Visibility, data = mock$CP,
                   left = "LastPresentDecimalDays",
                   right = "FirstAbsentDecimalDays",
                   dist = c("exponential", "lognormal"),
                   sizeCol = "Size", allCombos = TRUE)

  pkMods <- c("S" = "p ~ 1; k ~ 1", "L" = "p ~ 1; k ~ 1",
             "M" = "p ~ 1; k ~ 1", "XL" = "p ~ 1; k ~ 1"
            )
  cpMods <- c("S" = "dist: exponential; l ~ 1; NULL", 
              "L" = "dist: exponential; l ~ 1; NULL",
              "M" = "dist: exponential; l ~ 1; NULL",
              "XL" = "dist: exponential; l ~ 1; NULL"
            )
  avgSS <- averageSS(mock$SS)
  gsGeneric <- estgGenericSize(nsim = 1000, days = avgSS,
                 modelSetSize_SE = pkmModsSize,
                 modelSetSize_CP = cpmModsSize,
                 modelSizeSelections_SE = pkMods,
                 modelSizeSelections_CP = cpMods
               )