secr (version 2.5.0)

trap.builder: 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 = "SRS", edgemethod = "clip", samplefactor = 2,
    ranks = NULL, rotation = NULL, detector, 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

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 detector)

plt

logical: should array be plotted?

add

logical: add to existing plot

object

single-session multi-cluster capthist object, or traps object for cluster.centres

origin

new coordinate origin for detector array

clustergroup

list of vectors subscripting the clusters to be mashed

...

other arguments passed by mash to make.capthist (e.g., sortrows)

Value

• 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.

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 may be a two-column matrix or dataframe of x-y coordinates for the boundary, or a SpatialPolygonsDataFrame object from sp. 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). 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.

References

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)

## GRTS sample of mini-grids within a rectangle
## edgemethod = "allinside" avoids truncation at edge
minigrid <- make.grid(nx = 3, ny = 3, spacing = 50,
    detector = "proximity")
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)

## 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)
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
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)