estimation: Conducting Gaussian Process Regression
In statmlhb/CVEK: Cross-Validated Kernel Ensemble

Description Usage Arguments Details Value Author(s) See Also Examples

Conduct gaussian process regression based on the estimated ensemble kernel matrix.

1 2	estimation(Y, X, K_list = list(NULL), mode = "loocv", strategy = "stack", beta_exp = 1, lambda = exp(seq(-10, 5)), ...)

`Y`	(vector of length n) Reponses of the dataframe.
`X`	(dataframe, n*p) Fixed effects variables in the dataframe (could contains several subfactors).
`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" `ensemble_exp`.
`lambda`	(numeric) A numeric string specifying the range of noise 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 outpur of gaussian process regression.

`lambda`	(numeric) The selected tuning parameter based on the estimated ensemble kernel matrix.
`beta`	(matrix, p*1) Fixed effects estimator of the model.
`alpha`	(vector of length n) Random effects estimator of the estimated ensemble kernel matrix.
`K`	(matrix, n*n) Estimated ensemble kernel matrix.
`u_hat`	(vector of length K) A vector of weights of the kernels in the library.
`base_est`	(list of length 6) The detailed estimation results of K kernels.

Wenying Deng

strategy: ensemble

result <- estimation(Y = CVEK:::model_matrices$y, 
X = CVEK:::model_matrices$X, 
K_list = CVEK:::model_matrices$K, 
mode = "loocv", strategy = "stack", 
beta_exp = 1, lambda = exp(seq(-10, 5)))