estimate_ridge: Estimating a Single Model
In CVEK: Cross-Validated Kernel Ensemble

Estimating projection matrices and parameter estimates for a single model.

estimate_ridge(
  Y,
  X,
  K,
  lambda,
  compute_kernel_terms = TRUE,
  converge_thres = 1e-04
)

`Y`	(matrix, n*1) The vector of response variable.
`X`	(matrix, n*d_fix) The fixed effect matrix.
`K`	(list of matrices) A nested list of kernel term matrices, corresponding to each kernel term specified in the formula for a base kernel function in kern_func_list.
`lambda`	(numeric) A numeric string specifying the range of tuning parameter to be chosen. The lower limit of lambda must be above 0.
`compute_kernel_terms`	(logic) Whether to computing effect for each individual terms. If FALSE then only compute the overall effect.
`converge_thres`	(numeric) The convergence threshold for computing kernel terms.

For a single model, we can calculate the output of gaussian process regression, the solution is given by

\hat{β}=[X^T(K+λ I)^{-1}X]^{-1}X^T(K+λ I)^{-1}y

\hat{α}=(K+λ I)^{-1}(y-\hat{β}X)

`beta`	(matrix, d_fixed*1) Fixed effect estimates.
`alpha`	(matrix, n*k_terms) Kernel effect estimates for each kernel term.
`kern_term_mat`	(matrix, n*k_terms) Kernel effect for each kernel term.
`A_list`	(list of length k_terms) Projection matrices for each kernel term.
`proj_matrix`	(list of length 4) Estimated projection matrices, combined across kernel terms.