secr (version 2.5.0)

autoini: Initial Parameter Values for SECR

Description

Find plausible initial parameter values for secr.fit. A simple SECR model is fitted by a fast ad hoc method.

Usage

autoini(capthist, mask, detectfn = 0, thin = 0.2, tol = 0.001, 
    binomN = 1, adjustg0 = TRUE, ignoreusage = FALSE)

Arguments

capthist

capthist object

mask

mask object compatible with the detector layout in capthist

detectfn

integer code or character string for shape of detection function 0 = halfnormal

thin

proportion of points to retain in mask

tol

numeric absolute tolerance for numerical root finding

binomN

integer code for distribution of counts (see secr.fit)

adjustg0

logical for whether to adjust g0 for usage (effort) and binomN

ignoreusage

logical for whether to discard usage information from traps(capthist)

Value

• A list of parameter values :
• D
  Density (animals per hectare)
• g0
  Magnitude (intercept) of detection function
• sigma
  Spatial scale of detection function (m)

Details

Plausible starting values are needed to avoid numerical problems when fitting SECR models. Actual models to be fitted will usually have more than the three basic parameters output by autoini; other initial values can usually be set to zero for secr.fit. If the algorithm encounters problems obtaining a value for g0, the default value of 0.1 is returned. Only the halfnormal detection function is currently available in autoini (cf other options in e.g. detectfn and sim.capthist). autoini implements a modified version of the algorithm proposed by Efford et al. (2004). In outline, the algorithm is

1. Find value of sigma that predicts the 2-D dispersion of individual locations (seeRPSV)
2. Find value of g0 that, with sigma, predicts the observed mean number of captures per individual (by algorithm of Efford et al. (2009, Appendix 2))
3. Compute the effective sampling area from g0, sigma, using thinned mask (seeesa)
4. Compute D =$n$/esa(g0, sigma), where$n$is the number of individuals detected

Here `find' means solve numerically for zero difference between the observed and predicted values, using uniroot. If RPSV cannot be computed the algorithm tries to use observed mean recapture distance $\bar{d}$. Computation of $\bar{d}$ fails if there no recaptures, and all returned values are NA. If the mask has more than 100 points then a proportion 1--thin of points are discarded at random to speed execution. The argument tol is passed to uniroot. It may be a vector of two values, the first for g0 and the second for sigma. If traps(capthist) has a usage attribute (defining effort on each occasion at each detector) then the value of g0 is divided by the mean of the non-zero elements of usage. This adjustment is not precise. If adjustg0 is TRUE then an adjustment is made to g0 depending on the value of binomN. For Poisson counts (binomN = 0) the adjustment is linear on effort (adjusted.g0 = g0 / usage). Otherwise, the adjustment is on the hazard scale (adjusted.g0 = 1 -- (1 -- g0) ^ (1 / (usage x binomN))). An arithmetic average is taken over all non-zero usage values (i.e. over used detectors and times). If usage is not specified it is taken to be 1.0.

References

Efford, M. G., Dawson, D. K. and Robbins C. S. (2004) DENSITY: software for analysing capture--recapture data from passive detector arrays. Animal Biodiversity and Conservation 27, 217--228. Efford, M. G., Dawson, D. K. and Borchers, D. L. (2009) Population density estimated from locations of individuals on a passive detector array. Ecology 90, 2676--2682.

See Also

capthist, mask, secr.fit, dbar

Examples

demotraps <- make.grid()
demomask <- make.mask(demotraps)
demoCH <- sim.capthist (demotraps, popn = list(D = 5, buffer = 100))
autoini (demoCH, demomask)