The Cauchy family of correlation functions is useful to describe spatial processes with power-law decrease of correlation at long distance. It is valid for Euclidean distances in spaces of any dimension, and for great-circle distances on spheres of any dimension. It has a scale parameter (rho, as in the Mat<U+00E9>rn correlation function), a shape (or “smoothness”, Gneiting 2013) parameter, and a long-memory dependence (or, more abstractly, “shape”; Gneiting 2013) parameter (Gneiting and Schlater 2004). The present implementation also accepts a Nugget parameter. The family can be invoked in two ways. First, the CauchyCorr function evaluates correlations, using distances as input. Second, a term of the form Cauchy(1|<...>) in a formula specifies a random effect with Cauchy correlation function, using coordinates found in a data frame as input. In the latter case, the correlations between realizations of the random effect for any two observations in the data will be the value of the Cauchy function at the scaled distance between coordinates specified in <...>, using “+” as separator (e.g., Cauchy(1|latitude + longitude)).
CauchyCorr(d, rho=1, shape, longdep, Nugget=NULL)
# Cauchy(1|...)Euclidean or great-circle distance
The scaling factor for distance, a real >0.
The shape parameter, a real 0<.<=2 for Euclidean distances and 0<.<=1 for great-circle distances.
The long-memory dependence parameter, a real >0. It gives the exponent of the asymptotic decrease of correlation with distance: the smaller longdep is, the longer the dependence.
(Following the jargon of Kriging) a parameter describing a discontinuous decrease in
correlation at zero distance. Correlation will always be 1 at
Names of coordinates, using “+” as separator (e.g., Matern(1|latitude + longitude)
Scalar/vector/matrix depending on input.
The correlation at distance
Gneiting, T. and Schlater M. (2004) Stochastic models that separate fractal dimension and the Hurst effect. SIAM Rev. 46: 269<U+2013>282.
Gneiting T. (2013) Strictly and non-strictly positive definite functions on spheres. Bernoulli 19: 1327-1349.
# NOT RUN {
data("blackcap")
HLCor(migStatus ~ means+ Cauchy(1|latitude+longitude),data=blackcap,
HLmethod="ML",ranPars=list(longdep=0.5,shape=0.5,rho=0.05))
## The Cauchy family can be used in Euclidean spaces of any dimension:
set.seed(123)
randpts <- matrix(rnorm(20),nrow=5)
distMatrix <- as.matrix(proxy::dist(randpts))
CauchyCorr(distMatrix,rho=0.1,shape=1,longdep=10)
# }
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