Initial Parameter Values for SECR
Find plausible initial parameter values for secr.fit. A
simple SECR model is fitted by a fast ad hoc method.
autoini(capthist, mask, detectfn = 0, thin = 0.2, tol = 0.001,
binomN = 1, adjustg0 = TRUE, adjustsigma = 1.2, ignoreusage = FALSE,
ncores = NULL)capthist |
|
mask |
|
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 |
adjustg0 |
logical for whether to adjust g0 for usage (effort) and binomN |
adjustsigma |
numeric scalar applied to RPSV(capthist, CC = TRUE) |
ignoreusage |
logical for whether to discard usage information from
|
ncores |
integer number of threads to be used for parallel processing |
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
Find value of sigma that predicts the 2-D dispersion of individual locations (see RPSV).
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))
Compute the effective sampling area from g0, sigma, using thinned mask (see esa)
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.
Halfnormal sigma is estimated with RPSV(capthist, CC = TRUE). The factor adjustsigma is applied as a crude correction for truncation of movements at the edge of the detector array.
If RPSV cannot be computed the algorithm tries to use observed
mean recapture distance d-bar. Computation of
d-bar 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.
Setting ncores = NULL uses the existing value from the environment variable
RCPP_PARALLEL_NUM_THREADS (see setNumThreads).
A list of parameter values :
D |
Density (animals per hectare) |
g0 |
Magnitude (intercept) of detection function |
sigma |
Spatial scale of detection function (m) |
autoini always uses the Euclidean distance between detectors and
mask points.
You may get this message from secr.fit: “'autoini' failed to find g0; setting initial g0 = 0.1”. If the fitted model looks OK (reasonable estimates, non-missing SE) there is no reason to worry about the starting values. If you get this message and model fitting fails then supply your own values in the start argument of secr.fit.
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.
demotraps <- make.grid() demomask <- make.mask(demotraps) demoCH <- sim.capthist (demotraps, popn = list(D = 5, buffer = 100), seed = 321) autoini (demoCH, demomask)
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