sample.geodata

0th

Percentile

Sampling from geodata objects

This functions facilitates extracting samples from geodata objects.

Keywords
manip, spatial
Usage
sample.geodata(x, size, replace = FALSE, prob = NULL, coef.logCox,
               external)
Arguments
x

an object of the class geodata.

size

non-negative integer giving the number of items to choose.

replace

Should sampling be with replacement?

prob

A vector of probability weights for obtaining the elements of the data points being sampled.

coef.logCox

optional. A scalar with the coeficient for the log-Cox process. See DETAILS below.

external

numeric values of a random field to be used in the log-Cox inhomogeneous poisson process.

Details

If prob=NULL and the argument coef.logCox, is provided, sampling follows a log-Cox proccess, i.e. the probability of each point being sampled is proportional to: $$exp(b Y(x))$$ with \(b\) given by the value passed to the argument coef.logCox and \(Y(x)\) taking values passed to the argument external or, if this is missing, the element data of the geodata object. Therefore, the latter generates a preferential sampling.

Value

a list which is an object of the class geodata.

See Also

as.geodata, sample.

Aliases
  • sample.geodata
Examples
# NOT RUN {
par(mfrow=c(1,2))
S1 <- grf(2500,  grid="reg", cov.pars=c(1, .23))
image(S1, col=gray(seq(0.9,0.1,l=100)))
y1 <- sample.geodata(S1, 80)
points(y1$coords, pch=19)
## Now a preferential sampling
y2 <- sample.geodata(S1, 80, coef=1.3)
## which is equivalent topps
## y2 <- sample.geodata(S1, 80, prob=exp(1.3*S1$data))
points(y2$coords, pch=19, col=2)
## and now a clustered (but not preferential)
S2 <- grf(2500,  grid="reg", cov.pars=c(1, .23))
y3 <- sample.geodata(S1, 80, prob=exp(1.3*S2$data))
## which is equivalent to
## points(y3$coords, pch=19, col=4)
image(S2, col=gray(seq(0.9,0.1,l=100)))
points(y3$coords, pch=19, col=4)
# }
Documentation reproduced from package geoR, version 1.8-1, License: GPL (>= 2)

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