corr_complex: Complex Gaussian processes

Description Usage Arguments Details Author(s) References Examples

Description

Various utilities for investigating complex Gaussian processes

Usage

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corr_complex(z1, z2 = NULL, distance.function = complex_CF, means =
NULL, scales = NULL, pos.def.matrix = NULL)
complex_CF(z1,z2, means, pos.def.matrix)
scales.likelihood.complex(pos.def.matrix, scales, means,  zold, z,
               give_log = TRUE, func = regressor.basis)
interpolant.quick.complex(x, d, zold, Ainv, scales = NULL, pos.def.matrix = NULL,
               means=NULL, func = regressor.basis, give.Z = FALSE,
               distance.function = corr_complex, ...)

Arguments

z, z1, z2

Points in C^n

distance.function

Function giving the (complex) covariance between two points in C^n

means, pos.def.matrix, scales

In function complex_CF(), the mean and covariance matrix of the distribution whose characteristic function is used as to give the covariance matrix; scales is used to specify the diagonal of pos.def.matrix if the off-diagonal elements are zero

zold, d, give_log, func, x, Ainv, give.Z,...

Direct analogues of the arguments in interpolant() and scales.likelihood() in the emulator package

Details

More details are given in the package vignette.

Author(s)

Robin K. S. Hankin

References

Examples

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complex_CF(c(1,1i),c(1,-1i),means=c(1i,1i),pos.def.matrix=diag(2))

V <- latin.hypercube(7,2,complex=TRUE)

cm <- c(1,1+1i)                     # "complex mean"
cs <- matrix(c(2,1i,-1i,1),2,2)   # "complex scales"
tb <- c(1,1i,1-1i)                     # "true beta"

A <- corr_complex(V,means=cm,pos.def.matrix=cs)
Ainv <- solve(A)
z <- drop(rcmvnorm(n=1,mean=regressor.multi(V) %*% tb, sigma=A))


betahat.fun(V,Ainv,z)    # should be close to 'tb'

#scales.likelihood.complex(cs,cm,V,z)   # log-likelihood evaluated true parameters



interpolant.quick.complex(x=0.1i+V[1:3,],d=z,zold=V,Ainv=Ainv,pos.def.matrix=cs,means=cm)

cmvnorm documentation built on Aug. 16, 2018, 9:05 a.m.