sample.geodata {geoR} | R Documentation

## Sampling from geodata objects

### Description

This functions facilitates extracting samples from geodata objects.

### 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`.

### Examples

    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$coords, pch=19)
    ## Now a preferential sampling
    y2 <- sample.geodata(S1, 80, coef=1.3)
    ## which is equivalent to
    ## y2 <- sample.geodata(S1, 80, prob=exp(1.3*S1$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)

[Package geoR version 1.6-19 Index]