summary.GPModel: Summary for a 'GPModel'

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

Summary for a GPModel

Description

Summary for a GPModel

Usage

## S3 method for class 'GPModel'
summary(object, ...)

Arguments

object	a GPModel
...	(not used, ignore this, simply here that there is no CRAN warning)

Value

Summary of a (fitted) GPModel

Author(s)

Fabio Sigrist

Examples

# See https://github.com/fabsig/GPBoost/tree/master/R-package for more 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)

#--------------------Grouped random effects model: single-level random effect----------------
gp_model <- fitGPModel(group_data = group_data[,1], y = y, X = X1,
                       likelihood="gaussian", params = list(std_dev = TRUE))
summary(gp_model)



#--------------------Gaussian process model----------------
gp_model <- fitGPModel(gp_coords = coords, cov_function = "exponential",
                       likelihood="gaussian", y = y, X = X1, params = list(std_dev = TRUE))
summary(gp_model)

gpboost documentation built on Oct. 24, 2023, 9:09 a.m.