estimate_base: Estimating Projection Matrices

In IrisTeng/CVEK: Cross-Validated Kernel Ensemble

Description

Calculate the estiamted projection matrices for every kernels in the kernel library.

Usage

estimate_base(n, kern_size, Y, X1, X2, kern_list, mode, lambda)

Arguments

n	(integer) A numeric number specifying the number of observations.
kern_size	(integer, =K) A numeric number specifying the number of kernels in the kernel library.
Y	(vector of length n) Reponses of the dataframe.
X1	(dataframe, n*p1) The first type of factor in the dataframe (could contains several subfactors).
X2	(dataframe, n*p2) The second type of factor in the dataframe (could contains several subfactors).
kern_list	(list of length K) A list of kernel functions given by user.
mode	(character) A character string indicating which tuning parameter criteria is to be used.
lambda	(numeric) A numeric string specifying the range of noise to be chosen. The lower limit of lambda must be above 0.

Details

For a given mode, this function return a list of projection matrices for every kernels in the kernel library and a n*kern_size matrix indicating errors.

Value

A_hat	(list of length K) A list of projection matrices for every kernels in the kernel library.
error_mat	(matrix, n*K) A n\*kern_size matrix indicating errors.

Author(s)

Wenying Deng

References

Jeremiah Zhe Liu and Brent Coull. Robust Hypothesis Test for Nonlinear Effect with Gaus- sian Processes. October 2017.

Examples

estimate_base(n = 100, kern_size = 3, Y, X1, X2, kern_list,
mode = "loocv", lambda = exp(seq(-5, 5)))

IrisTeng/CVEK documentation built on May 31, 2019, 4:50 p.m.