gpboost (version 1.6.4)

set_prediction_data.GPModel: Set prediction data for a GPModel

Description

Set the data required for making predictions with a GPModel

Usage

# S3 method for GPModel
set_prediction_data(gp_model, vecchia_pred_type = NULL,
  num_neighbors_pred = NULL, cg_delta_conv_pred = NULL,
  nsim_var_pred = NULL, rank_pred_approx_matrix_lanczos = NULL,
  group_data_pred = NULL, group_rand_coef_data_pred = NULL,
  gp_coords_pred = NULL, gp_rand_coef_data_pred = NULL,
  cluster_ids_pred = NULL, X_pred = NULL)

Value

A GPModel

Arguments

gp_model

A GPModel

vecchia_pred_type

A string specifying the type of Vecchia approximation used for making predictions. Default value if vecchia_pred_type = NULL: "order_obs_first_cond_obs_only". Available options:

• "order_obs_first_cond_obs_only": Vecchia approximation for the observable process and observed training data is ordered first and the neighbors are only observed training data points

• "order_obs_first_cond_all": Vecchia approximation for the observable process and observed training data is ordered first and the neighbors are selected among all points (training + prediction)

• "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

• "order_pred_first": Vecchia approximation for the observable process and prediction data is ordered first for making predictions. This option is only available for Gaussian likelihoods

num_neighbors_pred

an integer specifying the number of neighbors for the Vecchia approximation for making predictions. Default value if NULL: num_neighbors_pred = 2 * num_neighbors

cg_delta_conv_pred

a numeric specifying the tolerance level for L2 norm of residuals for checking convergence in conjugate gradient algorithms when being used for prediction Default value if NULL: 1e-3

nsim_var_pred

an integer specifying the number of samples when simulation is used for calculating predictive variances Internal default values if NULL:

• 500 for grouped random effects

• 1000 for gp_approx = "vecchia" and gp_approx = "full_scale_tapering"

• 100 for gp_approx = "full_scale_vecchia"

rank_pred_approx_matrix_lanczos

an integer specifying the rank of the matrix for approximating predictive covariances obtained using the Lanczos algorithm Default value if NULL: 1000

group_data_pred

A vector or matrix with elements being group levels for which predictions are made (if there are grouped random effects in the GPModel)

group_rand_coef_data_pred

A vector or matrix with covariate data for grouped random coefficients (if there are some in the GPModel)

gp_coords_pred

A matrix with prediction coordinates (=features) for Gaussian process (if there is a GP in the GPModel)

gp_rand_coef_data_pred

A vector or matrix with covariate data for Gaussian process random coefficients (if there are some in the GPModel)

cluster_ids_pred

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)

X_pred

A matrix with prediction covariate data for the fixed effects linear regression term (if there is one in the GPModel)

Author

Fabio Sigrist

Examples

# \donttest{
data(GPBoost_data, package = "gpboost")
set.seed(1)
train_ind <- sample.int(length(y),size=250)
gp_model <- GPModel(group_data = group_data[train_ind,1], likelihood="gaussian")
set_prediction_data(gp_model, group_data_pred = group_data[-train_ind,1])
# }