Summary of GP model fit

Prints the summary of a class GP object estimated by GP_fit

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

Arguments

object

a class GP object estimated by GP_fit

...

for compatibility with generic method summary

Details

prints the summary of the GP object (object), by calling print.GP

See also

print.GP for more description of the output;
GP_fit for more information on estimating the model;
summary for more description on the summary function.

Author

Blake MacDonald, Hugh Chipman, Pritam Ranjan

Examples

## 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)
summary(GPmodel)
#> 
#> Number Of Observations: n = 5
#> Input Dimensions: d = 1
#> 
#> Correlation: Exponential (power = 1.95)
#> Correlation Parameters: 
#>      beta_hat
#> [1] 0.6433793
#> 
#> sigma^2_hat: [1] 7.262407
#> 
#> delta_lb(beta_hat): [1] 0
#> 
#> nugget threshold parameter: 20
#> 

## 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 <- 10
d <- 2
set.seed(1)
x <- lhs::maximinLHS(n, d) 
y <- computer_simulator(x)
GPmodel <- GP_fit(x, y)
#> Warning: NaNs produced
#> Error in GP_deviance(beta = row, X = X, Y = Y, nug_thres = nug_thres,     corr = corr): Infinite values of the Deviance Function, 
#>             unable to find optimum parameters 
summary(GPmodel)
#> 
#> Number Of Observations: n = 5
#> Input Dimensions: d = 1
#> 
#> Correlation: Exponential (power = 1.95)
#> Correlation Parameters: 
#>      beta_hat
#> [1] 0.6433793
#> 
#> sigma^2_hat: [1] 7.262407
#> 
#> delta_lb(beta_hat): [1] 0
#> 
#> nugget threshold parameter: 20
#>