secr (version 3.0.1)

fxi: Probability Density of Home Range Centre

Description

Display contours of the probability density function for the estimated location of one or more range centres, compute values for particular points X, or compute mode of pdf. The pdf is given by \(f(X_j|\omega_i) = \mbox{Pr}(\omega_i|X_j)\pi(X_j)\), where \(\pi(X)\) is the probability density of range centres across the mask (Borchers and Efford 2008).

Usage

fxi.contour (object, i = 1, sessnum = 1, border = 100, nx = 64,
    levels = NULL, p = seq(0.1,0.9,0.1), plt = TRUE, add = FALSE,
    fitmode = FALSE, plotmode = FALSE, normal = TRUE, fill = NULL,
    SPDF = FALSE, ncores = 1, ...)
fxi.secr(object, i = 1, sessnum = 1, X, normal = TRUE, ncores = 1)
fxi.mode(object, i = 1, sessnum = 1, start = NULL, ...)

Arguments

object

a fitted secr model

i

integer or character vector of individuals for which to plot contours, or a single individual as input to other functions

sessnum

session number if object$capthist spans multiple sessions

border

width of blank margin around the outermost detectors

nx

dimension of interpolation grid in x-direction

levels

numeric vector of confidence levels for Pr(X|wi)

p

numeric vector of contour levels as probabilities

plt

logical to plot contours

add

logical to add contour(s) to an existing plot

fitmode

logical to refine estimate of mode of each pdf

plotmode

logical to plot mode of each pdf

X

2-column matrix of x- and y- coordinates

normal

logical; should values of pdf be normalised?

fill

vector of colours to fill contours (optional)

SPDF

logical; if TRUE the output is a SpatialPolygonsDataFrame

ncores

integer number of cores to be used for parallel processing

start

vector of x-y coordinates for maximization

…

additional arguments passed to contour or nlm

Value

fxi.contour (SPDF = FALSE) --

Coordinates of the plotted contours are returned as a list with one component per polygon. The list is returned invisibly if plt = TRUE.

An additional component `mode' reports the x-y coordinates of the highest point of each pdf (see Details).

fxi.contour (SPDF = TRUE) --

Contours are returned as a SpatialPolygonsDataFrame (see package sp) with one component per animal. The attributes dataframe has two columns, the x- and y-coordinates of the mode. The SpatialPolygonsDataFrame is returned invisibly if plt = TRUE.

fxi.secr --

Vector of probability densities

fxi.mode --

List with components `x' and `y'

Warnings

Use ncores > 1 only if there is adequate memory available for each worker process. Otherwise processing will slow to a crawl. This is a particular problem in fxi.contour with fitmode = TRUE. In Windows, check memory usage under the Performace tab of the Task Manager (Ctrl-Alt-Delete). If you abort a parallel job you may need to manually delete the stranded Rscript processes in Task Manager.

fxi.mode may fail to find the true mode unless a good starting point is provided. Note that the distribution may have multiple modes and only one is reported. The default value of start before secr 2.9.4 was the first detected location of the animal.

Details

fxi.contour computes contours of probability density for one or more detection histories. Increase nx for smoother contours. If levels is not set, contour levels are set to approximate the confidence levels in p.

fxi.secr computes the probability density for one or more detection histories; X may contain coordinates for one or several points; a dataframe or vector (x then y) will be coerced to a matrix.

fxi.mode attempts to find the x- and y-coordinates corresponding to the maximum of the pdf for a single detection history (i.e. i is of length 1). fxi.mode calls nlm.

fxi.contour with fitmode = TRUE calls fxi.mode for each individual. Otherwise, the reported mode is an approximation (mean of coordinates of highest contour).

If i is character it will be matched to row names of object$capthist (restricted to the relevant session in the case of a multi-session fit); otherwise it will be interpreted as a row number.

Values of the pdf are optionally normalised by dividing by the integral of \(\mbox{Pr}(\omega_i|X)\pi(X)\) over the habitat mask in object.

If ncores and length(i) are both greater than 1 then multiple worker processes will be run in separate cores to speed up the calculations.

If start is not provided to fit.mode then (from 2.9.4) the weighted mean of all detector sites is used (see Warning below).

The … argument gives additional control over a contour plot; for example, set drawlabels = FALSE to suppress contour labels.

References

Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture--recapture studies. Biometrics 64, 377--385.

See Also

pdot.contour, contour, fx.total

Examples

fxi.secr(secrdemo.0, i = 1, X = c(365,605))

## contour first 5 detection histories
plot(secrdemo.0$capthist)
fxi.contour (secrdemo.0, i = 1:5, add = TRUE,
    plotmode = TRUE, drawlabels = FALSE)

## Not run: ------------------------------------
# 
#   ## extract modes only
#   ## these are more reliable than those from fit.mode called directly as
#   ## they use a contour-based approximation for the starting point
#   fxiout <- fxi.contour (secrdemo.0, i = 1:5, plt = FALSE, fitmode = TRUE)
#   t(sapply(fxiout, "[[", "mode"))
# 
#   ## using fill colours
#   ## lty = 0 suppresses contour lines
#   ## nx = 256 ensures smooth outline
#   plot(traps(captdata))
#   fxi.contour(secrdemo.0, i = 1:5, add = TRUE, p = c(0.5,0.95), drawlabels
#       = FALSE, nx = 256, fill = topo.colors(4), lty = 0)
# 
#   ## output as SpatialPolygonsDataFrame
#   spdf <- fxi.contour(secrdemo.0, i = 1:3, plt = FALSE, p = c(0.5,0.95),
#       nx = 256, SPDF = TRUE, fitmode = TRUE)
# 
#   ## save as ESRI shapefile
#   library(maptools)
#   writeSpatialShape(spdf, fn = "test")
# 
#   ## plot contours and modes
#   plot(spdf)
#   points(data.frame(spdf))
# 
## ---------------------------------------------