R Under development (unstable) (2024-08-21 r87038 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
>  Sys.setenv('OMP_THREAD_LIMIT'=2)
>  library(rlibkriging)

Attaching package: 'rlibkriging'

The following objects are masked from 'package:base':

    load, save

> 
> ##library(rlibkriging, lib.loc="bindings/R/Rlibs")
> ##library(testthat)
> 
> library(RobustGaSP)
#########
##
## Robust Gaussian Stochastic Process, RobustGaSP Package
## Copyright (C) 2016-2024 Mengyang Gu, Jesus Palomo and James O. Berger
#########

Attaching package: 'RobustGaSP'

The following object is masked from 'package:rlibkriging':

    simulate

The following object is masked from 'package:stats':

    simulate

> 
> context("RobustGaSP-Nugget / Fit: 1D")
> 
> f = function(x) 10*(1-1/2*(sin(12*x)/(1+x)+2*cos(7*x)*x^5+0.7))
> #plot(f)
> n <- 5
> set.seed(123)
> X <- as.matrix(runif(n))
> y = f(X)
> #points(X,y)
> k = RobustGaSP::rgasp(design=X,response=y,nugget.est = TRUE)
The upper bounds of the range parameters are 184.9743 Inf 
The initial values of range parameters are 3.699485 
Start of the optimization  1  : 
The number of iterations is  13 
 The value of the  marginal posterior  function is  -6.32876 
 Optimized range parameters are 0.3498584 
 Optimized nugget parameter is 0.02539959 
 Convergence:  TRUE 
The initial values of range parameters are 0.05223118 
Start of the optimization  2  : 
The number of iterations is  24 
 The value of the  marginal posterior  function is  -6.32876 
 Optimized range parameters are 0.3498584 
 Optimized nugget parameter is 0.02539959 
 Convergence:  TRUE 
> #library(rlibkriging)
> r <- NuggetKriging(y, X,
+   kernel="matern5_2",
+   regmodel = "constant", normalize = FALSE,
+   optim = "none",
+   objective = "LMP", 
+   parameters=list(
+     theta=matrix(1/k@beta_hat),is_theta_estim=F,
+     nugget=k@nugget*k@sigma2_hat,is_nugget_estim=F,
+     sigma2=k@sigma2_hat,is_sigma2_estim=F))
> # m = as.list(r)
> 
> # Check predict
> 
> ntest <- 100
> Xtest <- seq(0,1,,ntest)
> Ytest_rgasp <- predict(k,matrix(Xtest,ncol=1))
> Ytest_libK <- predict(r,Xtest)
> 
> plot(f)
> points(X,y)
> lines(Xtest,Ytest_rgasp$mean,col='blue')
> polygon(c(Xtest,rev(Xtest)),
+         c(Ytest_rgasp$mean+2*Ytest_rgasp$sd,rev(Ytest_rgasp$mean-2*Ytest_rgasp$sd)),
+         col=rgb(0,0,1,0.1), border=NA)
> 
> lines(Xtest,Ytest_libK$mean,col='red')
> polygon(c(Xtest,rev(Xtest)),
+         c(Ytest_libK$mean+2*Ytest_libK$stdev,rev(Ytest_libK$mean-2*Ytest_libK$stdev)),
+         col=rgb(1,0,0,0.1), border=NA)
> 
> precision <- 1e-3
> test_that(desc=paste0("pred mean is the same that RobustGaSP one"),
+           expect_equal(predict(r,0.7)$mean[1],predict(k,matrix(0.7))$mean,tol = precision))
Test passed 🥳
> test_that(desc=paste0("pred sd is the same that RobustGaSP one"),
+           expect_equal(predict(r,0.7)$stdev[1],predict(k,matrix(0.7))$sd,tol = precision))
Test passed 🥳
> 
> 
> 
> context("RobustGaSP-Nugget / Fit: 2D (Branin)")
> 
> f = function(X) apply(X,1,DiceKriging::branin)
> n <- 15
> set.seed(1234)
> X <- cbind(runif(n),runif(n))
> y = f(X)
> model = NULL
> r = NULL
> library(RobustGaSP)
> k = rgasp(design=X,response=y, nugget.est = T)
The upper bounds of the range parameters are 58.61045 56.09173 Inf 
The initial values of range parameters are 1.172209 1.121835 
Start of the optimization  1  : 
The number of iterations is  30 
 The value of the  marginal posterior  function is  -61.1843 
 Optimized range parameters are 0.7974221 2.544159 
 Optimized nugget parameter is 1.716403e-13 
 Convergence:  TRUE 
The initial values of range parameters are 0.2437167 0.2332433 
Start of the optimization  2  : 
The number of iterations is  30 
 The value of the  marginal posterior  function is  -61.1843 
 Optimized range parameters are 0.7974571 2.54443 
 Optimized nugget parameter is 3.708305e-14 
 Convergence:  TRUE 
> #library(rlibkriging)
> r <- NuggetKriging(y, X,
+   kernel="matern5_2",
+   regmodel = "constant", normalize = FALSE,
+   optim = "none",
+   objective = "LMP",
+   parameters=list(
+     theta=matrix(1/k@beta_hat,nrow=2),is_theta_estim=F,
+     nugget=k@nugget*k@sigma2_hat,is_nugget_estim=F,
+     sigma2=k@sigma2_hat,is_sigma2_estim=F))
>   
> 
> # Check predict
> x2=0.8
> f1 = function(x) f(cbind(x,x2))
> 
> ntest <- 100
> Xtest <- seq(0,1,,ntest)
> Ytest_rgasp <- predict(k,matrix(cbind(Xtest,x2),ncol=2))
> Ytest_libK <- predict(r,cbind(Xtest,x2))
> 
> plot(f1)
> lines(Xtest,Ytest_rgasp$mean,col='blue')
> polygon(c(Xtest,rev(Xtest)),
+         c(Ytest_rgasp$mean+2*Ytest_rgasp$sd,rev(Ytest_rgasp$mean-2*Ytest_rgasp$sd)),
+         col=rgb(0,0,1,0.1), border=NA)
> 
> lines(Xtest,Ytest_libK$mean,col='red')
> polygon(c(Xtest,rev(Xtest)),
+         c(Ytest_libK$mean+2*Ytest_libK$stdev,rev(Ytest_libK$mean-2*Ytest_libK$stdev)),
+         col=rgb(1,0,0,0.1), border=NA)
> 
> precision <- 1e-1
> test_that(desc=paste0("pred mean is the same that RobustGaSP one"),
+           expect_equal(predict(r,c(0.7,x2))$mean[1],predict(k,matrix(c(0.7,x2),ncol=2))$mean,tol = precision))
Test passed 😸
> test_that(desc=paste0("pred sd is the same that RobustGaSP one"),
+           expect_equal(predict(r,c(0.7,x2))$stdev[1],predict(k,matrix(c(0.7,x2),ncol=2))$sd,tol = precision))
Test passed 🌈
> 
> proc.time()
   user  system elapsed 
   1.73    0.40    2.12