
## S3 method for class '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"),
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, ...)
capthist
varycol
=
TRUE), or colour scale (non-petal plots)varycol
= TRUE)splitocc
)par
)par
)par
)plot.traps
type = "petal"
, the number of detections 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.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 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
.
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.
To overplot the point telemetry locations use xy2CH
to
first convert the xylist attribute to a full telemetry capthist object
(see Examples).capthist
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))
## 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)
## try varying individual colours - requires RColorBrewer
library(RColorBrewer)
plot(infraCH[[2]], icolours = brewer.pal(12, "Set3"), tracks = T,
bg = "black", cappar = list(cex = 2), border = 10, rad = 2,
gridlines=F)
## generate telemetry data
## as in ?addTelemetry
te <- make.telemetry()
tr <- make.grid(detector = "proximity")
totalpop <- sim.popn(tr, D = 20, buffer = 100)
tepop <- subset(totalpop, runif(nrow(totalpop)) < 0.05)
trCH <- sim.capthist(tr, popn = totalpop, renumber = FALSE, detectfn = "HHN")
teCH <- sim.capthist(te, popn = tepop, renumber=FALSE, detectfn = "HHN",
detectpar = list(lambda0 = 3, sigma = 25))
combinedCH <- addTelemetry(trCH, teCH)
plotMCP(combinedCH)
plot(xy2CH(combinedCH), add=T)
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