Draws a Halton Iterative Partition (HIP) sample from a
SpatialPoints* object.
hip.point(x, n, J = NULL, plot.lattice = FALSE)A SpatialPoints or SpatialPointsDataFrame object
representing the 2-dimensional point resource
from which samples are taken.
This object must contain at least 1 point.
Sample size. The number locations to draw from the set of points
contained in x. If the sample size returned is less than the desired sample size,
increase n until the desired sample size is reached.
A 2X1 vector of base powers. J[1] is for horizontal,
J[2] for vertical dimension. J determines the size and shape
of the smallest Halton boxes. There are bases[1]^J[1] vertical columns
of Halton boxes over x's bounding box, and bases[2]^J[2]
horizontal rows of Halton boxes over the bounding box, for a total
of prod(bases^J) boxes. The dimension of each box is
c(dx,dy)/ (bases^J), where c(dx,dy) are the horizontal and
vertical extents of x's bounding box. If J=NULL (the default),
J is chosen so that Halton boxes are as square as possible.
Boolean. If TRUE, plots the sample drawn with corresponding halton lattice.
A SpatialPoints objects containing locations in the
HIP sample, in HIP order.
Additional attributes of the output object, beyond those which
make it a SpatialPoints, are:
frame: Name of the input sampling frame.
frame.type: Type of resource in sampling frame. (i.e., "point").
sample.type: Type of sample drawn. (i.e., "HIP").
J: Exponents of the bases used to form the lattice of
Halton boxes. This is either the input J, or the J vector
computed by halton.indices.
bases: Bases of the Halton sequence used to draw the sample.
hl.bbox: The bounding box around points in x used to
draw the sample. See halton.indices.
A brief description of Halton Iterative Partition (HIP) sampling for points:
Given a set of Halton Iterative Partition parameters
x (SpatialPoints* object) and n (sample size),
a lattice of Halton boxes is constructed iteratively over the bounding box
of x.
This results in enough Halton boxes on the bounding box to uniquely
cover the point resource. That is, one and only one point per
box.
The Halton index (the inverse of the Halton sequence) of all boxes
is computed and assigned to points that lie
in each box. Finally, a random number between 0 and the largest Halton index is
drawn, and the next n points associated with the
next n Halton boxes are taken as
the sample, wrapping to the beginning if necessary.
# NOT RUN {
# Draw sample of cities in the state of Washington
data(WA.cities)
samp <- hip.point( WA.cities, 100 )
# }
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