Draws an equi-probable unstratified Generalized Random
Tessellation Stratified (GRTS) sample from a
grts.point(x, n, over.n = 0)
Sample size. The number of sample points to draw from
Over-sample size. The number of 'over-sample' points to draw
This is a wrapper for the
grts function in package
This simplifies calling
grts when equi-probable samples are
desired. It extends the valid input frame types to
(i.e., no attributes),
rather than just
SpatialPointsDataFrame objects. For more
complicated designs (e.g., variable probability, stratification), call
SpatialPointsDataFrame containing locations in the GRTS sample, in
order they are to be visited. Attributes of the sample points (in the embedded data frame) are
sampleID: Unique identifier for sample points. This
encodes the GRTS ordering of the sample. The output object
comes pre-sorted in GRTS order.
If the sample becomes un-GRTS-ordered, resort
samp <- samp[order(samp$sampleID),]).
pointType: A string identifying regular sample points (
and over-sample points (
geometryID: The ID of the point in
x which was sampled. The
ID of points in
Any attributes of the original points (in
Stevens, D. L. and A. R. Olsen (1999). Spatially restricted surveys over time for aquatic resources. Journal of Agricultural, Biological, and Environmental Statistics 4 (4), 415-428.
Stevens, D. L. and A. R. Olsen (2004). Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99, 262-278.
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# Draw sample WA.city.samp <- grts.point(WA.cities,100,50) # Plot plot( WA.cities, pch=16, cex=.5 ) # Plot 'sample' locations plot( WA.city.samp[ WA.city.samp$pointType == "Sample", ], pch=1, add=TRUE, col="red" ) # Plot 'over sample' locations plot( WA.city.samp[ WA.city.samp$pointType == "OverSample", ], pch=2, add=TRUE, col="blue" )
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