| trap.builder {secr} | R Documentation |
Complex Detector Layouts
Description
Construct detector layouts comprising small arrays (clusters) replicated across space, possibly at a probability sample of points.
Usage
trap.builder (n = 10, cluster, region = NULL, frame = NULL,
method = c("SRS", "GRTS", "all", "rank"),
edgemethod = c("clip", "allowoverlap", "allinside", "anyinside", "centreinside"),
samplefactor = 2, ranks = NULL, rotation = NULL, detector,
exclude = NULL, exclmethod = c("clip", "alloutside", "anyoutside", "centreoutside"),
plt = FALSE, add = FALSE, ...)
mash (object, origin = c(0,0), clustergroup = NULL, ...)
cluster.counts (object)
cluster.centres (object)
Arguments
n |
integer number of clusters (ignored if method = "all") |
cluster |
traps object |
region |
bounding polygon(s) |
frame |
data frame of points used as a finite sampling frame |
method |
character string (see Details) |
edgemethod |
character string (see Details) |
samplefactor |
oversampling to allow for rejection of edge clusters (multiple of n) |
ranks |
vector of relative importance (see Details) |
rotation |
angular rotation of each cluster about centre (degrees) |
detector |
character detector type (see |
exclude |
polygon(s) from which detectors are to be excluded |
exclmethod |
character string (see Details) |
plt |
logical: should array be plotted? |
add |
logical: add to existing plot |
object |
single-session multi-cluster capthist object, or traps
object for |
origin |
new coordinate origin for detector array |
clustergroup |
list of vectors subscripting the clusters to be mashed |
... |
other arguments passed by trap.builder to spsurvey::grts (e.g., mindis) and by mash to make.capthist (e.g., sortrows) |
Details
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 one of the other polygon
sources listed in boundarytoSF (these allow multiple polygons).
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.
Options for edgemethod are –
edgemethod | Description |
"clip" | reject any individual detectors outside region |
"allowoverlap" | no action |
"allinside" | reject whole cluster if any component is outside region |
"anyinside" | reject whole cluster if no component is inside region |
"centreinside" | reject whole cluster if centre outside region, and clip to region
|
Similarly, exclmethod may be "clip" (reject individual detectors),
"alloutside" (reject whole cluster if any component is outside exclude) etc.
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’).
GRTS samples require function grts in version >= 5.3.0 of package spsurvey
(Dumelle et al. 2022). More sophisticated stratified 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.
cluster.centres returns the centroid of the detector locations in each cluster. When clusters have been truncated these differ from the attribute centres set by make.systematic.
Value
trap.builder produces an object of class ‘traps’.
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.
Note
The function make.systematic should be used to generate
systematic random layouts. It calls trap.builder.
The sequence number of the cluster to which each detector belongs, and
its within-cluster sequence number, may be retrieved with the
functions clusterID and clustertrap.
References
Dumelle, M., Kincaid, T. M., Olsen, A. R., and Weber, M. H. (2021). spsurvey: Spatial Sampling Design and Analysis. R package version 5.2.0.
Stevens, D. L., Jr., and Olsen, A. R. (2004) Spatially-balanced sampling of natural resources. Journal of the American Statistical Association 99, 262–278.
See Also
make.grid, traps,
make.systematic,
clusterID,
clustertrap
Examples
## 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)
## one detector in each 100-m grid cell -
## a form of stratified simple random sample
## see also Examples in ?make.grid
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
## Not run:
## 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)
tempmask1 <- make.mask(tempgrid, type = "clusterrect",
buffer = 200, spacing = 10)
fit1 <- secr.fit(tempcapt, mask = tempmask1, trace = FALSE)
tempmask2 <- make.mask(minigrid, spacing = 10)
fit2 <- secr.fit(mash(tempcapt), mask = tempmask2, trace = FALSE)
## density estimate is adjusted automatically
## for the number of mashed clusters (9)
predict(fit1)
predict(fit2)
fit1$proctime
fit2$proctime
## SRS excluding detectors from a polygon
region <- cbind(x = c(0,6000,6000,0,0), y = c(0,0,6000,6000,0))
exclude <- cbind(x = c(3000,7000,7000,3000,3000), y = c(2000,2000,4000,4000,2000))
newgrid <- trap.builder (n = 40, cluster = minigrid,
method = "SRS", edgemethod = "allinside", region = region,
exclude = exclude, exclmethod = "alloutside",
plt = TRUE)
## two-phase design: preliminary sample across region,
## followed by selection of sites for intensive grids
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)
## GRTS sample of mini-grids within a rectangle
## GRTS uses package 'spsurvey' >= 5.3.0
minigrid <- make.grid(nx = 3, ny = 3, spacing = 50,
detector = "proximity")
region <- cbind(x = c(0,6000,6000,0,0), y = c(0,0,6000,6000,0))
if (requireNamespace("spsurvey", versionCheck = list(version = ">=5.3.0"))) {
tempgrid <- trap.builder (n = 20, cluster = minigrid, region = region,
plt = TRUE, method = "GRTS")
# specifying minimum distance between cluster origins
tempgrid2 <- trap.builder (n = 20, cluster = minigrid, region = region,
plt = TRUE, method = "GRTS", mindis = 500, maxtry = 10)
# use spsurvey::warnprnt() to view warnings (e.g., maxtry inadequate)
}
## End(Not run)