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 numeric covariate data for the
fixed effects linear regression term (if there is one)
A list with parameters for the model fitting / optimization
optimizer_cov Optimizer used for estimating covariance parameters. Options: "gradient_descent", "fisher_scoring", "nelder_mead", "bfgs", "adam". 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", "nelder_mead", "bfgs", "adam". 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. If 'optimizer_cov' is set to "nelder_mead", "bfgs", or "adam", 'optimizer_coef' is automatically also set to the same value.
maxit Maximal number of iterations for optimization algorithm. Default=1000
delta_rel_conv Convergence tolerance. The algorithm stops if the relative change in eiher the (approximate) log-likelihood or the parameters is below this value. For "bfgs" and "adam", the L2 norm of the gradient is used instead of the relative change in the log-likelihood. Default=1E-6
convergence_criterion The convergence criterion used for terminating the optimization algorithm. Options: "relative_change_in_log_likelihood" (default) or "relative_change_in_parameters"
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 momentum is applied in the beginning. Default=2
trace If TRUE, information on the progress of the parameter optimization is printed. Default=FALSE
std_dev If TRUE, approximate standard deviations are calculated for the covariance and linear regression parameters (= square root of diagonal of the inverse Fisher information for Gaussian likelihoods and square root of diagonal of a numerically approximated inverse Hessian for non-Gaussian likelihoods)
A vector of optional external fixed effects which are held fixed during training.
Fabio Sigrist