Evaluation of kernels
kernelEval(tZ, kernel = c("linear", "poly", "gaussian"), ...)linKernelEval(tZ)
gaussKernelEval(tZ, sigma = 1)
polyKernelEval(tZ, a = 0, d = 2)
genericKernelEval(tZ, kernel_func, ...)
gaussKernelEval_multipleRhos(tZ, rho)
polyKernelEval_multipleRhos(tZ, rho, d = 2)
kernelEval, linKernelEval, gaussKernelEval, and genericKernelEval
return an N x N matrix with entries K(Z[i,], Z[j,]) [persons i,j]
gaussKernelEval_multipleRhos and polyKernelEval_multipleRhos return
a matrix of dimension Q x N^2, where Q is the length of rho,
each row corresponds to a rho (puns!) to get the actual kernel matrix associated with a particular
value of rho, if output is G, take matrix(G[i,], N)
a P x N matrix of genomic covariates (i.e., the usual data array Z transposed)
which kernel is evaluated by kerneval. Possible values include currently implemented kernels
designated by a character string "linear", "poly" and "gaussian".
Otherwise can also be a user-defined function (see kernel_func).
other arguments to be passed to be passed to the evaluated kernel function.
standard-deviation parameter for the "gaussian" kernel.
TODO of the polynomial for the "poly". Default is 0
power of the polynomial. Default is 2 (quadratic kernel).
a function, whose first argument should be tZ
either a single rho to evaluate the kernel at, or a vector of rhos
kernelEval works only for gaussian, polynomial and linear kernels currently.
genericKernelEval
For polyKernelEval_multipleRhos, one should have rho > 0 to get
basis of monomials up to degree d