corrTG {gcKrig} | R Documentation |
This function implements two general methods for computing the correlation function in a transformed Gaussian random field.
corrTG(marg1, marg2, corrGauss = 0.5, method = "integral", nrep = 1000)
marg1 |
an object of class |
marg2 |
an object of class |
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 |
A scalar between 0 and 1, denoting the correlation of the transformed Gaussian random field.
Zifei Han hanzifei1@gmail.com
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.
## 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)