Mask Buffer Width

Description

Determines a suitable buffer width for an integration mask. The ‘buffer’ in question defines a concave polygon around a detector array constructed using make.mask with type = "trapbuffer". The method relies on an approximation to the bias of maximum likelihood density estimates (M. Efford unpubl).

Usage

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suggest.buffer(object, detectfn = NULL, detectpar = NULL, 
    noccasions = NULL, ignoreusage = FALSE, RBtarget = 0.001,
    interval = NULL, binomN = NULL, ...)

bias.D (buffer, traps, detectfn, detectpar, noccasions, binomN = NULL,
    control = NULL)

Arguments

object

single-session ‘secr’, ‘traps’ or ‘capthist’ object

detectfn

integer code or character string for shape of detection function 0 = halfnormal etc. – see detectfn

detectpar

list of values for named parameters of detection function – see detectpar

noccasions

number of sampling occasions

ignoreusage

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

RBtarget

numeric target for relative bias of density estimate

interval

a vector containing the end-points of the interval to be searched

binomN

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

...

other argument(s) passed to bias.D

buffer

vector of buffer widths

traps

‘traps’ object

control

list of mostly obscure numerical settings (see Details)

Details

The basic input style of suggest.buffer uses a ‘traps’ object and a detection model specified by ‘detectpar’, ‘detectfn’ and ‘noccasions’, plus a target relative bias (RB). A numerical search is conducted for the buffer width that is predicted to deliver the requested RB. If interval is omitted it defaults to (1, 100S) where S is the spatial scale of the detection function (usually detectpar$sigma). An error is reported if the required buffer width is not within interval. This often happens with heavy-tailed detection functions (e.g., hazard-rate): choose another function, a larger RBtarget or a wider interval.

Convenient alternative input styles are –

  • secr object containing a fitted model. Values of ‘traps’, ‘detectpar’, ‘detectfn’ and ‘noccasions’ are extracted from object and any values supplied for these arguments are ignored.

  • capthist object containing raw data. If detectpar is not supplied then autoini is used to get ‘quick and dirty’ values of g0 and sigma for a halfnormal detection function. noccasions is ignored. autoini tends to underestimate sigma, and the resulting buffer also tends to be too small.

bias.D is called internally by suggest.buffer.

Value

suggest.buffer returns a scalar value for the suggested buffer width in metres, or a vector of such values in the case of a multi-session object.

bias.D returns a dataframe with columns buffer and RB.D (approximate bias of density estimate using finite buffer width, relative to estimate with infinite buffer).

Note

The algorithm in bias.D uses one-dimensional numerical integration of a polar approximation to site-specific detection probability. This uses a further 3-part linear approximation for the length of contours of distance-to-nearest-detector (r) as a function of r.

The approximation seems to work well for a compact detector array, but it should not be taken as an estimate of the bias for any other purpose: do not report RB.D as "the relative bias of the density estimate". RB.D addresses only the effect of using a finite buffer. The effect of buffer width on final estimates should be checked with mask.check.

The default buffer type in make.mask, and hence in secr.fit, is ‘traprect’, not ‘trapbuffer’, but a buffer width that is adequate for ‘trapbuffer’ is always adequate for ‘traprect’.

control contains various settings of little interest to the user.

The potential components of control are –

method = 1

code for method of modelling p.(X) as a function of buffer (q(r))

bfactor = 20

q(r) vs p.(X) calibration mask buffer width in multiples of trap spacing

masksample = 1000

maximum number of points sampled from calibration mask

spline.df = 10

effective degrees of freedom for smooth.spline

See Also

mask, make.mask, mask.check, esa.plot

Examples

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## Not run: 
temptraps <- make.grid()
detpar <-  list(g0 = 0.2, sigma = 25)
suggest.buffer(temptraps, "halfnormal", detpar, 5)

RB <- bias.D(50:150, temptraps, "halfnormal", detpar, 5)
plot(RB)

detpar <-  list(g0 = 0.2, sigma = 25, z=5)
RB <- bias.D(50:150, temptraps, "hazard rate", detpar, 5)
lines(RB)

## compare to esa plot
esa.plot (temptraps, max.buffer = 150, spacing = 4, detectfn = 0,
  detectpar = detpar, noccasions = 5, as.density = F)

## End(Not run)

## compare detection histories and fitted model as input
suggest.buffer(captdata)
suggest.buffer(secrdemo.0)

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