% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GPModel.R
\name{set_prediction_data}
\alias{set_prediction_data}
\title{Set prediction data for a \code{GPModel}}
\usage{
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)
}
\arguments{
\item{gp_model}{A \code{GPModel}}

\item{vecchia_pred_type}{A \code{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:
\itemize{
   \item{"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 }
   \item{"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) }
   \item{"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}
   \item{"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 }
   \item{"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 }
}}

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

\item{cg_delta_conv_pred}{a \code{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}

\item{nsim_var_pred}{an \code{integer} specifying the number of samples when simulation 
is used for calculating predictive variances
Internal default values if NULL: 
\itemize{
   \item{ 500 for grouped random effects }
   \item{ 1000 for gp_approx = "vecchia" and gp_approx = "full_scale_tapering" }
   \item{ 100 for gp_approx = "full_scale_vecchia" }
   }}

\item{rank_pred_approx_matrix_lanczos}{an \code{integer} specifying the rank 
of the matrix for approximating predictive covariances obtained using the Lanczos algorithm
Default value if NULL: 1000}

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

\item{group_rand_coef_data_pred}{A \code{vector} or \code{matrix} with covariate data 
for grouped random coefficients (if there are some in the \code{GPModel})}

\item{gp_coords_pred}{A \code{matrix} with prediction coordinates (=features) for 
Gaussian process (if there is a GP in the \code{GPModel})}

\item{gp_rand_coef_data_pred}{A \code{vector} or \code{matrix} with covariate data for 
Gaussian process random coefficients (if there are some in the \code{GPModel})}

\item{cluster_ids_pred}{A \code{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 \code{GPModel})}

\item{X_pred}{A \code{matrix} with prediction covariate data for the 
fixed effects linear regression term (if there is one in the \code{GPModel})}
}
\description{
Set the data required for making predictions with a \code{GPModel}
}
\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])
}

}
\author{
Fabio Sigrist
}
