secr (version 4.6.10)

plot.capthist: Plot Detection Histories

Description

Display a plot of detection (capture) histories or telemetry data over a map of the detectors.

Usage

# S3 method for capthist
plot(x, rad = 5, hidetraps = FALSE, tracks = FALSE,
    title = TRUE, subtitle = TRUE, add = FALSE, varycol = TRUE, 
    icolours = NULL, randcol = FALSE, lab1cap = FALSE, laboffset = 4, ncap = FALSE, 
    splitocc = NULL, col2 = "green", type = c("petal", "n.per.detector", "n.per.cluster",
        "sightings", "centres", "telemetry", "nontarget"),
    cappar = list(cex = 1.3, pch = 16, col = "blue"), 
    trkpar = list(col = "blue", lwd = 1), 
    labpar = list(cex = 0.7, col = "black"), ...)
plotMCP(x, add = FALSE, col = "black", fill = NA, lab1cap = FALSE, laboffset = 4,
    ncap = FALSE, ...)

Value

For type = "petal", the number of detections in x. For type = "sightings", the number of sightings of unmarked animals in x. For type = "n.per.detector" or type = "n.per.cluster", a dataframe with data for a legend (see Examples).

plotMCP invisibly returns a list in which each component is a 2-column (x,y) dataframe of boundary coordinates for one individual.

Arguments

x

an object of class capthist

rad

radial displacement of dot indicating each capture event from the detector location (used to separate overlapping points)

hidetraps

logical indicating whether trap locations should be displayed

tracks

logical indicating whether consecutive locations of individual animals should be joined by a line

title

logical or character string for title

subtitle

logical or character string for subtitle

add

logical for whether to add to existing plot

varycol

logical for whether to distinguish individuals by colour

icolours

vector of individual colours (when varycol = TRUE), or colour scale (non-petal plots)

randcol

logical to use random colours (varycol = TRUE)

lab1cap

logical for whether to label the first capture of each animal

laboffset

distance by which to offset labels from points

ncap

logical to display the number of detections per trap per occasion

splitocc

optional occasion from which second colour is to be used

col2

second colour (used with splitocc)

type

character string ("petal", "n.per.detector" or "n.per.cluster")

cappar

list of named graphical parameters for detections (passed to par)

trkpar

list of named graphical parameters for tracks (passed to par)

labpar

list of named graphical parameters for labels (passed to par)

...

arguments to be passed to plot.traps

col

vector of line colour numbers or names (plotMCP only)

fill

vector of fill colour numbers or names (plotMCP only)

Details

By default, a `petal' plot is generated in the style of Density (Efford 2012) using eqscplot from the MASS library.

If type = "n.per.detector" or type = "n.per.cluster" the result is a colour-coded plot of the number of individuals at each unit, pooled over occasions.

If type = "sightings" the sightings of unmarked animals are displayed on a petal-like plot (requires mark-resight data) (see also sightingPlot).

If type = "centres" then a single point is plotted for each animal, jittered on each axis by a random amount (limits +/- rad/2).

If type = "telemetry" and the `telemetryxy' attribute is not NULL then the telemetry locations are plotted.

If type = "nontarget" and the `nontarget' attribute is not NULL then the nontarget captures or interference events are plotted.

If title = FALSE no title is displayed; if title = TRUE, the session identifer is used for the title.

If subtitle = FALSE no subtitle is displayed; if subtitle = TRUE, the subtitle gives the numbers of occasions, detections and individuals.

If x is a multi-session capthist object then a separate plot is produced for each session. Use par(mfrow = c(nr, nc)) to allow a grid of plots to be displayed simultaneously (nr rows x nc columns).

These arguments are used only for petal plots: rad, tracks, varycol, randcol, lab1cap, laboffset, ncap, splitocc, col2, trkpar, and labpar. Call occasionKey to add a key to the petals.

If icolours = NULL and varycol = TRUE then a vector of colours is generated automatically as topo.colors((nrow(x)+1) * 1.5). If there are too few values in icolours for the number of individuals then colours will be re-used.

plotMCP plots minimum convex polygons of individual location data over a base plot of detector locations. Usually the data are telemetry locations in the xylist attribute of the capthist object; if this is not present and x is a polygon search capthist then the individual xy data are plotted.

References

Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture--recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://www.otago.ac.nz/density/.

See Also

capthist, occasionKey, sightingPlot

Examples

demotrap <- make.grid()
tempcapt <- sim.capthist(demotrap, 
    popn = list(D = 5, buffer = 50), 
    detectpar = list(g0 = 0.15, sigma = 30))
plot(tempcapt, border = 10, rad = 3, tracks = TRUE, 
    lab1cap = TRUE, laboffset = 2.5)

## type = n.per.cluster

## generate some captures
testregion <- data.frame(x = c(0,2000,2000,0),
    y = c(0,0,2000,2000))
popn <- sim.popn (D = 10, core = testregion, buffer = 0,
    model2D = "hills", details = list(hills = c(-2,3)))
t1 <- make.grid(nx = 1, ny = 1)
t1.100 <- make.systematic (cluster = t1, spacing = 100,
    region = testregion)
capt <- sim.capthist(t1.100, popn = popn, noccasions = 1)

## now plot captures ...
temp <- plot(capt, title = "Individuals per cluster",
    type = "n.per.cluster", hidetraps = FALSE,
    gridlines = FALSE, cappar = list(cex = 1.5))

if (interactive()) {
    ## add legend; click on map to place top left corner
    legend (locator(1), pch = 21, pt.bg = temp$colour,
        pt.cex = 1.3, legend = temp$legend, cex = 0.8)
}

if (FALSE) {

## try varying individual colours - requires RColorBrewer
library(RColorBrewer)
plot(infraCH[[2]], icolours = brewer.pal(12, "Set3"), tracks = TRUE,
    bg = "black", cappar = list(cex = 2), border = 10, rad = 2,
    gridlines = FALSE)

## generate telemetry data
te <- make.telemetry()
tr <- make.grid(detector = "proximity")
totalpop <- sim.popn(tr, D = 20, buffer = 100)
tepop <- subset(totalpop, runif(nrow(totalpop)) < 0.05)
teCH <- sim.capthist(te, popn = tepop, renumber=FALSE, detectfn = "HHN",
    detectpar = list(lambda0 = 3, sigma = 25))
plot(teCH, type = 'telemetry', tracks = TRUE)

## simple "centres" example
## polygon data require 'hazard' detection function 14:19
CH <- sim.capthist(make.poly(), nocc = 20, detectfn = 'HHN', 
    detectpar = list(lambda0 = 1, sigma = 10))
plot(CH, cappar = list(col = 'orange'), varycol = FALSE, border = 10)
plot(CH, type = 'centres', add = TRUE, rad = 0)
}