Construct detector layouts comprising small arrays (clusters) replicated across space, possibly at a probability sample of points.
trap.builder (n = 10, cluster, region = NULL, frame = NULL, method =
c("SRS", "GRTS", "all", "rank"), edgemethod = c("clip", "allowoverlap",
"allinside"), samplefactor = 2, ranks = NULL, rotation = NULL, detector,
exclude = NULL, exclmethod = c("clip", "alloutside"), plt = FALSE, add =
FALSE)mash (object, origin = c(0,0), clustergroup = NULL, …)
cluster.counts (object)
cluster.centres (object)
detector)cluster.centres trap.builder produces an object of class `traps'.
method = "GRTS" causes messages to be displayed regarding the
stratum (always "None"), and the initial, current and final number
of levels from the GRTS algorithm.
plt = TRUE causes a plot to be displayed, including the polygon
or finite sampling frame as appropriate.
mash produces a capthist object with the same number of rows as
the input but different detector numbering and `traps'. An attribute
`n.mash' is a vector of the numbers recorded at each cluster; its
length is the number of clusters. An attribute `centres' is a
dataframe containing the x-y coordinates of the cluster centres. The
predict method for secr objects and the function derived
both recognise and adjust for mashing.
cluster.counts returns a vector with the number of individuals
detected at each cluster.
cluster.centres returns a dataframe of x- and y-coordinates.
The detector array in cluster is replicated n
times and translated to centres sampled from the area sampling frame
in region or the finite sampling frame in frame. Each
cluster may be rotated about its centre either by a fixed number of
degrees (rotation positive), or by a random angle (rotation
negative).
If the cluster argument is not provided then single detectors of
the given type are placed according to the design.
The sampling frame is finite (the points in frame) whenever
frame is not NULL. If region and frame are both
specified, sampling uses the finite frame but sites may be clipped
using the polygon.
region and exclude may be a two-column matrix or
dataframe of x-y coordinates for the boundary, or a SpatialPolygons or
SpatialPolygonsDataFrame object from sp. A SpatialPolygons
object is mostly sufficient, but a full SpatialPolygonsDataFrame
object is required for region when method = "GRTS" and
frame = NULL.
method may be "SRS", "GRTS", "all" or "rank". "SRS" takes a simple
random sample (without replacement in the case of a finite sampling
frame). "GRTS" takes a spatially representative sample using the
`generalized random tessellation stratified' (GRTS) method of Stevens
and Olsen (2004). "all" replicates cluster across all points in
the finite sampling frame. "rank" selects n sites from
frame on the basis of their ranking on the vector `ranks',
which should have length equal to the number of rows in
frame; ties are resolved by drawing a site at random.
edgemethod may be "clip" (reject individual detectors),
"allowoverlap" (no action) or "allinside" (reject whole cluster if any
component is outside region). Similarly, exclmethod may
be "clip" (reject individual detectors) or "alloutside" (reject whole cluster if any
component is outside exclude). Sufficient additional samples
((samplefactor--1) * n) must be drawn to allow for replacement
of any rejected clusters; otherwise, an error is reported (`not enough
clusters within polygon').
The package sp is required. GRTS samples require function
grts in package spsurvey of Olsen and Kincaid. Much more
sophisticated sampling designs may be specified by using grts
directly.
mash collapses a multi-cluster capthist object as if all
detections were made on a single cluster. The new detector coordinates
in the `traps' attribute are for a single cluster with (min(x),
min(y)) given by origin. clustergroup optionally selects
one or more groups of clusters to mash; if length(clustergroup)
> 1 then a multisession capthist object will be generated, one
`session' per clustergroup. By default, all clusters are mashed.
mash discards detector-level covariates and occasion-specific
`usage', with a warning.
cluster.counts returns the number of distinct
individuals detected per cluster in a single-session multi-cluster
capthist object.
Stevens, D. L., Jr., and Olsen, A. R. (2004) Spatially-balanced sampling of natural resources. Journal of the American Statistical Association 99, 262--278.
make.grid, traps,
make.systematic,
clusterID,
clustertrap
## solitary detectors placed randomly within a rectangle
tempgrid <- trap.builder (n = 10, method = "SRS",
region = cbind(x = c(0,1000,1000,0),
y = c(0,0,1000,1000)), plt = TRUE)
## GRTS sample of mini-grids within a rectangle
## GRTS requires package 'spsurvey' that may be unavailable
## on Mavericks
## edgemethod = "allinside" avoids truncation at edge
minigrid <- make.grid(nx = 3, ny = 3, spacing = 50,
detector = "proximity")
if (require(spsurvey)) {
tempgrid <- trap.builder (n = 20, cluster = minigrid,
method = "GRTS", edgemethod = "allinside", region =
cbind(x = c(0,6000,6000,0), y = c(0,0,6000,6000)),
plt = TRUE)
}
## as before, but excluding detectors from a polygon
if (require(spsurvey)) {
tempgrid <- trap.builder (n = 40, cluster = minigrid,
method = "GRTS", edgemethod = "allinside", region =
cbind(x = c(0,6000,6000,0), y = c(0,0,6000,6000)),
exclude = cbind(x = c(3000,7000,7000,3000), y =
c(2000,2000,4000,4000)), exclmethod = "alloutside",
plt = TRUE)
}
## one detector in each 100-m grid cell -
## a form of stratified simple random sample
origins <- expand.grid(x = seq(0, 900, 100),
y = seq(0, 1100, 100))
XY <- origins + runif(10 * 12 * 2) * 100
temp <- trap.builder (frame = XY, method = "all",
detector = "multi")
## same as temp <- read.traps(data = XY)
plot(temp, border = 0) ## default grid is 100 m
## simulate some data
## regular lattice of mini-arrays
minigrid <- make.grid(nx = 3, ny = 3, spacing = 50,
detector = "proximity")
tempgrid <- trap.builder (cluster = minigrid , method =
"all", frame = expand.grid(x = seq(1000, 5000, 2000),
y = seq(1000, 5000, 2000)), plt = TRUE)
tempcapt <- sim.capthist(tempgrid, popn = list(D = 10))
cluster.counts(tempcapt)
cluster.centres(tempgrid)
## "mash" the CH
summary(mash(tempcapt))
## compare timings (estimates are near identical)
## Not run: ------------------------------------
# tempmask1 <- make.mask(tempgrid, type = "clusterrect",
# buffer = 200, spacing = 10)
# fit1 <- secr.fit(tempcapt, mask = tempmask1, trace = FALSE) ## 680 s
#
# tempmask2 <- make.mask(minigrid, spacing = 10)
# fit2 <- secr.fit(mash(tempcapt), mask = tempmask2, trace = FALSE) ## 6.2 s
# ## density estimate is adjusted automatically
# ## for the number of mashed clusters (9)
#
# predict(fit1)
# predict(fit2)
# fit1$proctime
# fit2$proctime
## ---------------------------------------------
## two-phase design: preliminary sample across region,
## followed by selection of sites for intensive grids
## Not run: ------------------------------------
# arena <- data.frame(x = c(0,2000,2000,0), y = c(0,0,2500,2500))
# t1 <- make.grid(nx = 1, ny = 1)
# t4 <- make.grid(nx = 4, ny = 4, spacing = 50)
# singletraps <- make.systematic (n = c(8,10), cluster = t1,
# region = arena)
# CH <- sim.capthist(singletraps, popn = list(D = 2))
# plot(CH, type = "n.per.cluster", title = "Number per cluster")
# temp <- trap.builder(10, frame = traps(CH), cluster = t4,
# ranks = cluster.counts(CH), method = "rank",
# edgemethod = "allowoverlap", plt = TRUE, add = TRUE)
## ---------------------------------------------
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