fxi {secr}

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, fill = NULL,
    output = c('list','sf','SPDF'), ncores = NULL, ...)
fxi.secr(object, i = NULL, sessnum = 1, X = NULL, ncores = NULL)
fxi.mode(object, i = 1, sessnum = 1, start = NULL, ncores = NULL, ...)

Arguments

object

a fitted secr model

i

integer or character vector of individuals (defaults to all in fxi.secr), or a single individual as input to fxi.mode

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 (defaults to mask)

fill

vector of colours to fill contours (optional)

output

character; format of output (list, sf or SpatialPolygonsDataFrame)

ncores

integer number of threadss to be used for parallel processing

start

vector of x-y coordinates for maximization

...

additional arguments passed to contour or nlm

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 normalised by dividing by the integral of \mbox{Pr}(\omega_i|X)\pi(X) over the habitat mask in object. (May differ in secr 4.0).

Setting ncores = NULL uses the existing value from the environment variable RCPP_PARALLEL_NUM_THREADS (see setNumThreads).

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.

Value

fxi.contour (output = 'list') –

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 (output = 'SPDF') –

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.contour (output = 'sf') – simple features 'sf' object, as for SPDF.

fxi.secr –

Vector of probability densities

fxi.mode –

List with components ‘x’ and ‘y’

Warnings

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.

Note

From secr 2.8.3, these functions work with both homogeneous and inhomogeneous Poisson density models, and fxi.secr accepts vector-valued i.

See fx.total for a surface summed across individuals.

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

## Not run: 

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)

## 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 simple features
sf <- fxi.contour(secrdemo.0, i = 1:3, plt = FALSE, p = c(0.5,0.95),
    nx = 256, output = 'sf', fitmode = TRUE)

## save as ESRI shapefile testsf.shp etc.
library(sf)
st_write(sf, 'testsf.shp')
## plot contours and modes
plot(st_as_sfc(sf))    # outline only
points(sf$modex, sf$modey)

## output as SpatialPolygonsDataFrame
spdf <- fxi.contour(secrdemo.0, i = 1:3, plt = FALSE, p = c(0.5,0.95),
    nx = 256, output = 'SPDF', fitmode = TRUE)
sp::plot(spdf)
points(data.frame(spdf))


## End(Not run)