Parameter docs shared by GPModel, gpb.cv, and gpboost
A string specifying the likelihood function (distribution) of the response variable
Default = "gaussian"
A vector or matrix whose columns are categorical grouping variables.
The elements being group levels defining grouped random effects.
The elements of 'group_data' can be integer, double, or character.
The number of columns corresponds to the number of grouped (intercept) random effects
A vector or matrix with numeric covariate data
for grouped random coefficients
A vector with integer indices that
indicate the corresponding categorical grouping variable (=columns) in 'group_data' for
every covariate in 'group_rand_coef_data'. Counting starts at 1.
The length of this index vector must equal the number of covariates in 'group_rand_coef_data'.
For instance, c(1,1,2) means that the first two covariates (=first two columns) in 'group_rand_coef_data'
have random coefficients corresponding to the first categorical grouping variable (=first column) in 'group_data',
and the third covariate (=third column) in 'group_rand_coef_data' has a random coefficient
corresponding to the second grouping variable (=second column) in 'group_data'
A vector of type logical (boolean).
Indicates whether intercept random effects are dropped (only for random coefficients).
If drop_intercept_group_rand_effect[k] is TRUE, the intercept random effect number k is dropped / not included.
Only random effects with random slopes can be dropped.
A matrix with numeric coordinates (= inputs / features) for defining Gaussian processes
A vector or matrix with numeric covariate data for
Gaussian process random coefficients
A string specifying the covariance function for the Gaussian process.
The following covariance functions are available:
"exponential", "gaussian", "matern", "powered_exponential", "wendland", and "exponential_tapered".
For "exponential", "gaussian", and "powered_exponential", we follow the notation and parametrization of Diggle and Ribeiro (2007).
For "matern", we follow the notation of Rassmusen and Williams (2006).
For "wendland", we follow the notation of Bevilacqua et al. (2019).
A covariance function with the suffix "_tapered" refers to a covariance function that is multiplied by
a compactly supported Wendland covariance function (= tapering)
A numeric specifying the shape parameter of the covariance function
(=smoothness parameter for Matern and Wendland covariance). For the Wendland covariance function,
we follow the notation of Bevilacqua et al. (2019)).
This parameter is irrelevant for some covariance functions such as the exponential or Gaussian
A numeric specifying the range parameter of the Wendland covariance function / taper.
We follow the notation of Bevilacqua et al. (2019)
A boolean. If TRUE, the Vecchia approximation is used
An integer specifying the number of neighbors for the Vecchia approximation
A string specifying the ordering used in the Vecchia approximation.
"none" means the default ordering is used, "random" uses a random ordering
A string specifying the type of Vecchia approximation used for making predictions.
"order_obs_first_cond_obs_only" = observed data is ordered first and the neighbors are only observed points,
"order_obs_first_cond_all" = observed data is ordered first and the neighbors are selected among all points
(observed + predicted), "order_pred_first" = predicted data is ordered first for making predictions,
"latent_order_obs_first_cond_obs_only" = Vecchia approximation for the latent process and observed data is
ordered first and neighbors are only observed points, "latent_order_obs_first_cond_all" = Vecchia approximation
for the latent process and observed data is ordered first and neighbors are selected among all points
an integer specifying the number of neighbors for the Vecchia approximation
for making predictions
a numeric specifying the olerance level for L2 norm of residuals for
checking convergence in conjugate gradient algorithm when being used for prediction
A vector with elements indicating independent realizations of
random effects / Gaussian processes (same values = same process realization).
The elements of 'cluster_ids' can be integer, double, or character.
A boolean. If TRUE, the data (groups, coordinates, covariate data for random coefficients)
is freed in R after initialization
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.
A vector or matrix with elements being group levels
for which predictions are made (if there are grouped random effects in the GPModel)
A vector or matrix with covariate data
for grouped random coefficients (if there are some in the GPModel)
A matrix with prediction coordinates (=features) for
Gaussian process (if there is a GP in the GPModel)
A vector or matrix with covariate data for
Gaussian process random coefficients (if there are some in the GPModel)
A vector with elements indicating the realizations of
random effects / Gaussian processes for which predictions are made
(set to NULL if you have not specified this when creating the GPModel)
A matrix with prediction covariate data for the
fixed effects linear regression term (if there is one in the GPModel)
A boolean. If TRUE, the (posterior)
predictive covariance is calculated in addition to the (posterior) predictive mean
A boolean. If TRUE, the (posterior)
predictive variances are calculated