Description Usage Arguments Details Value Author(s) See Also
Conduct Gaussian process regression based on the estimated ensemble kernel matrix.
1 2 3 4 5 6 7 8 9 10 |
Y |
(matrix, n*1) The vector of response variable. |
X |
(matrix, n*d_fix) The fixed effect matrix. |
K_list |
(list of matrices) A nested list of kernel term matrices. The first level corresponds to each base kernel function in kern_func_list, the second level corresponds to each kernel term specified in the formula. |
mode |
(character) A character string indicating which tuning parameter criteria is to be used. |
strategy |
(character) A character string indicating which ensemble strategy is to be used. |
beta_exp |
(numeric/character) A numeric value specifying the parameter
when strategy = "exp" |
lambda |
(numeric) A numeric string specifying the range of tuning parameter to be chosen. The lower limit of lambda must be above 0. |
... |
Additional parameters to pass to estimate_ridge. |
After obtaining the ensemble kernel matrix, we can calculate the output of Gaussian process regression.
lambda |
(numeric) The selected tuning parameter based on the estimated ensemble kernel matrix. |
beta |
(matrix, d_fixed*1) Fixed effect estimates. |
alpha |
(matrix, n*1) Kernel effect estimates. |
K |
(matrix, n*n) Estimated ensemble kernel matrix. |
u_hat |
(vector of length K) A vector of weights of the kernels in the library. |
kern_term_effect |
(matrix, n*n) Estimated ensemble kernel effect matrix. |
base_est |
(list) The detailed estimation results of K kernels. |
Wenying Deng
strategy: ensemble
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