cvMultiKernel: Cross-validation for multivariate kernel regression

In turgeonmaxime/multiKernel: Multivariate prediction using kernel-machine regression

Description

This function performs cross-validation for multivariate kernel regression

Usage

cvMultiKernel(response, covariate, confounder = NULL, kernel = c("linear",
  "quadratic", "gaussian"), intercept = TRUE, tau, K = 5, pure = FALSE,
  ...)

Arguments

response	matrix of response variables
covariate	matrix of covariate variables, which are included in the kernel.
confounder	matrix or data.frame of confounder variables, which are not included in the kernel.
kernel	Type of kernel to use.
intercept	Should we include an intercept?
tau	Tuning parameter.
K	number of folds for cross-validation.
pure	Logical. Use the pure R version?
...	Extra parameters to be passed to the kernel function.

Value

Returns the estimated prediction error, as well as a separate value for each fold

turgeonmaxime/multiKernel documentation built on June 1, 2019, 2:56 a.m.