GPModelGeneric 'fit' method for a GPModel
fit(gp_model, y, X, params, fixed_effects = NULL)a GPModel
A vector with response variable data
A matrix with covariate data for fixed effects ( = linear regression term)
A list with parameters for the model fitting / optimization
optimizer_cov Optimizer used for estimating covariance parameters. Options: "gradient_descent", "fisher_scoring", and "nelder_mead". Default = "gradient_descent"
optimizer_coef Optimizer used for estimating linear regression coefficients, if there are any (for the GPBoost algorithm there are usually none). Options: "gradient_descent", "wls", and "nelder_mead". Gradient descent steps are done simultaneously with gradient descent steps for the covariance parameters. "wls" refers to doing coordinate descent for the regression coefficients using weighted least squares. Default="wls" for Gaussian data and "gradient_descent" for other likelihoods.
maxit Maximal number of iterations for optimization algorithm. Default=1000.
delta_rel_conv Convergence criterion: stop optimization if relative change in parameters is below this value. Default=1E-6.
init_coef Initial values for the regression coefficients (if there are any, can be NULL). Default=NULL.
init_cov_pars Initial values for covariance parameters of Gaussian process and random effects (can be NULL). Default=NULL.
lr_coef Learning rate for fixed effect regression coefficients if gradient descent is used. Default=0.1.
lr_cov Learning rate for covariance parameters. If <= 0, internal default values are used. Default value = 0.1 for "gradient_descent" and 1. for "fisher_scoring"
use_nesterov_acc If TRUE Nesterov acceleration is used. This is used only for gradient descent. Default=TRUE
acc_rate_coef Acceleration rate for regression coefficients (if there are any) for Nesterov acceleration. Default=0.5.
acc_rate_cov Acceleration rate for covariance parameters for Nesterov acceleration. Default=0.5.
momentum_offset Number of iterations for which no mometum is applied in the beginning. Default=2.
trace If TRUE, information on the progress of the parameter optimization is printed. Default=FALSE.
convergence_criterion The convergence criterion used for terminating the optimization algorithm. Options: "relative_change_in_log_likelihood" (default) or "relative_change_in_parameters".
std_dev If TRUE, (asymptotic) standard deviations are calculated for the covariance parameters
A vector of optional external fixed effects which are held fixed during training.