| fxi {secr} | R Documentation |
Display contours of the probability density function for the estimated
location of one or more activity centres (AC), 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).
These functions were previously named fxi.secr, fxi.contour and fxi.mode.
## S3 method for class 'secr'
fxi(object, i = NULL, sessnum = 1, X = NULL, ncores = NULL, ...)
fxiContour (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, ...)
fxiMode(object, i = 1, sessnum = 1, start = NULL, ncores = NULL, ...)
object |
a fitted secr model |
i |
integer or character vector of individuals (defaults to all in |
sessnum |
session number if |
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 |
fxiContour computes contours of AC 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 computes the AC 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.
fxiMode attempts to find the x- and y-coordinates
corresponding to the maximum of the AC pdf for a single detection history
(i.e. i is of length 1). fxiMode calls
nlm.
fxiContour with fitmode = TRUE calls fxiMode
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. (In secr >= 4.0 may differ from previous versions).
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.
fxi –
Vector of probability densities
fxiContour (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).
fxiContour (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.
fxiContour (output = 'sf') – simple features 'sf' object, as for SPDF.
fxiMode –
List with components ‘x’ and ‘y’
fxiMode 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.
From secr 2.8.3, these functions work with both homogeneous
and inhomogeneous Poisson density models, and fxi accepts
vector-valued i.
See fxTotal for a surface summed across individuals.
Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.
## Not run:
fxi(secrdemo.0, i = 1, X = c(365,605))
## contour first 5 detection histories
plot(secrdemo.0$capthist)
fxiContour (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 <- fxiContour (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))
fxiContour(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 <- fxiContour(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 <- fxiContour(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)