secr (version 1.3.0)

autoini: Initial Parameter Values for SECR

Description

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

Usage

autoini(capthist, mask, detectfn = 0, thin = 0.2)

Arguments

capthist

capthist object

mask

mask object compatible with the detector layout in capthist

detectfn

shape of detection function 0 = half-normal

thin

proportion of points to retain in mask

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 half-normal detection function is available in autoini (cf other options in e.g. 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. A proportion 1--thin of the points in the mask may be discarded at random to speed execution.

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)