Evaluation of kernels
Usage
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)Arguments
- tZ
a
P x Nmatrix of genomic covariates (i.e., the usual data array Z transposed)- kernel
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 (seekernel_func).- ...
other arguments to be passed to be passed to the evaluated kernel function.
- sigma
standard-deviation parameter for the
"gaussian"kernel.- a
TODO of the polynomial for the
"poly". Default is0- d
power of the polynomial. Default is
2(quadratic kernel).- kernel_func
a function, whose first argument should be
tZ- rho
either a single rho to evaluate the kernel at, or a vector of rhos
Value
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)