gpboost (version 1.5.6)

predict_training_data_random_effects.GPModel: Predict ("estimate") training data random effects for a GPModel

Description

Predict ("estimate") training data random effects for a GPModel

Usage

# S3 method for GPModel
predict_training_data_random_effects(gp_model,
  predict_var = FALSE)

Value

A GPModel

Arguments

gp_model

A GPModel

predict_var

A boolean. If TRUE, the (posterior) predictive variances are calculated

Author

Fabio Sigrist

Examples

# \donttest{
data(GPBoost_data, package = "gpboost")
# Add intercept column
X1 <- cbind(rep(1,dim(X)[1]),X)
X_test1 <- cbind(rep(1,dim(X_test)[1]),X_test)

gp_model <- fitGPModel(group_data = group_data[,1], y = y, X = X1, likelihood="gaussian")
all_training_data_random_effects <- predict_training_data_random_effects(gp_model)
first_occurences <- match(unique(group_data[,1]), group_data[,1])
unique_training_data_random_effects <- all_training_data_random_effects[first_occurences]
head(unique_training_data_random_effects)
# }