# print.GP: GP model fit Summary In GPfit: Gaussian Processes Modeling

## Description

Prints the summary of a class `GP` object estimated by `GP_fit`

## Usage

 ```1 2``` ```## S3 method for class 'GP' print(x, ...) ```

## Arguments

 `x` a class `GP` object estimated by `GP_fit` `...` for compatibility with generic method `print`

## Details

Prints the summary of the class `GP` object. It returns the number of observations, input dimension, parameter estimates of the GP model, lower bound on the nugget, and the nugget threshold parameter (described in `GP_fit`).

## Author(s)

Blake MacDonald, Hugh Chipman, Pritam Ranjan

`GP_fit` for more information on estimating the model;
`print` for more description on the `print` function.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33``` ```## 1D example n <- 5 d <- 1 computer_simulator <- function(x){ x <- 2 * x + 0.5 y <- sin(10 * pi * x) / (2 * x) + (x - 1)^4 return(y) } set.seed(3) x <- lhs::maximinLHS(n, d) y <- computer_simulator(x) GPmodel <- GP_fit(x, y) print(GPmodel) ## 2D Example: GoldPrice Function computer_simulator <- function(x) { x1 <- 4*x[,1] - 2 x2 <- 4*x[,2] - 2 t1 <- 1 + (x1 + x2 + 1)^2*(19 - 14*x1 + 3*x1^2 - 14*x2 + 6*x1*x2 + 3*x2^2) t2 <- 30 + (2*x1 -3*x2)^2*(18 - 32*x1 + 12*x1^2 + 48*x2 - 36*x1*x2 + 27*x2^2) y <- t1*t2 return(y) } n <- 30 d <- 2 set.seed(1) x <- lhs::maximinLHS(n, d) y <- computer_simulator(x) GPmodel <- GP_fit(x,y) print(GPmodel, digits = 3) ```