tskrr.fit: Carry out a two-step kernel ridge regression

In xnet: Two-Step Kernel Ridge Regression for Network Predictions

Description

This function provides an interface for two-step kernel ridge regression. To use this function, you need at least one kernel matrix and one label matrix. It's the internal engine used by the function tskrr.

Usage

tskrr.fit(y, k, g = NULL, lambda.k = NULL, lambda.g = NULL, ...)

Arguments

y	a matrix representing the links between the nodes of both networks.
k	an object of class eigen containing the eigen decomposition of the first kernel matrix.
g	an optional object of class eigen containing the eigen decomposition of the second kernel matrix. If NULL, the network is considered to be homogeneous.
lambda.k	a numeric value for the lambda parameter tied to the first kernel.
lambda.g	a numeric value for the lambda parameter tied to the second kernel. If NULL, the model is fit using the same value for lambda.k and lambda.g
...	arguments passed to other functions. Currently ignored.

Details

This function is mostly available for internal use. In most cases, it makes much more sense to use tskrr, as that function returns an object one can work with. The function tskrr.fit could be useful when doing simulations or fitting algorithms, as the information returned from this function is enough to use the functions returned by get_loo_fun.

Value

a list with three elements:

• k : the hat matrix for the rows

• g : the hat matrix for the columns (or NULL) for homogeneous networks.

• pred : the predictions

Examples

data(drugtarget)

K <- eigen(targetSim)
G <- eigen(drugSim)

res <- tskrr.fit(drugTargetInteraction,K,G,
                 lambda.k = 0.01, lambda.g = 0.05)

xnet documentation built on Feb. 4, 2020, 9:10 a.m.