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

In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

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

Description

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

Usage

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

Arguments

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

Value

A GPModel

Author(s)

Fabio Sigrist

Examples

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)

gpboost documentation built on Sept. 11, 2024, 7:04 p.m.