gpboost (version 0.6.1)

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, 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

gp_model

A GPModel

group_data_pred

A vector or matrix with labels of 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 IDs / labels 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 covariate data for the linear regression term (if there is one in the GPModel)

Value

A GPModel

Examples

# NOT RUN {
library(gpboost)
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])
# }