corrTG {gcKrig}R Documentation

Compute the Correlation in Transformed Gaussian Random Fields

Description

This function implements two general methods for computing the correlation function in a transformed Gaussian random field.

Usage

corrTG(marg1, marg2, corrGauss = 0.5, method = "integral", nrep = 1000)

Arguments

marg1

an object of class marginal.gc specifying the first marginal distribution.

marg2

an object of class marginal.gc specifying the second marginal distribution.

corrGauss

the correlation in the Gaussian random field. Should be a scalar between 0 and 1.

method

the computation method of calculating correlation in the transformed Gaussian random field. Can be either "integral" or "mc". If use "integral" then a series expansion based on the Hermite Polynomials will be used to approximate the correlation, see De Oliveira (2013). If use "mc" then the Monte Carlo method will be used.

nrep

the Monte Carlo size in computing the correlation. Only need to be specified if method = "mc".

Value

A scalar between 0 and 1, denoting the correlation of the transformed Gaussian random field.

Author(s)

Zifei Han hanzifei1@gmail.com

References

De Oliveira, V. (2013) Hierarchical Poisson models for spatial count data. Journal of Multivariate Analysis,122:393-408.

Han, Z. and De Oliveira, V. (2018) gcKrig: An R Package for the Analysis of Geostatistical Count Data Using Gaussian Copulas. Journal of Statistical Software, 87(13), 1–32. doi: 10.18637/jss.v087.i13.

Examples

## Not run: 
corrTG(marg1 = poisson.gc(lambda = 10), marg2 = binomial.gc(size = 1, prob = 0.1),
       corrGauss = 0.5, method = "integral")
set.seed(12345)
corrTG(marg1 = poisson.gc(lambda = 10), marg2 = binomial.gc(size = 1, prob = 0.1),
       corrGauss = 0.5, nrep = 100000, method = "mc")

## End(Not run)

[Package gcKrig version 1.1.3 Index]