CRAN Package Check Results for Package rlibkriging

Last updated on 2025-02-21 02:52:23 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9-1 266.56 236.43 502.99 OK
r-devel-linux-x86_64-debian-gcc 0.9-1 210.56 155.80 366.36 OK
r-devel-linux-x86_64-fedora-clang 0.9-1 948.04 ERROR
r-devel-linux-x86_64-fedora-gcc 0.9-1 974.11 ERROR
r-devel-macos-arm64 0.9-1 186.00 OK
r-devel-macos-x86_64 0.9-1 379.00 OK
r-devel-windows-x86_64 0.9-1 357.00 281.00 638.00 OK
r-patched-linux-x86_64 0.9-1 302.92 215.52 518.44 NOTE
r-release-linux-x86_64 0.9-1 294.00 214.47 508.47 NOTE
r-release-macos-arm64 0.9-1 185.00 NOTE
r-release-macos-x86_64 0.9-1 364.00 NOTE
r-release-windows-x86_64 0.9-1 372.00 266.00 638.00 NOTE
r-oldrel-macos-arm64 0.9-1 184.00 NOTE
r-oldrel-macos-x86_64 0.9-1 331.00 NOTE
r-oldrel-windows-x86_64 0.9-1 423.00 326.00 749.00 NOTE

Check Details

Version: 0.9-1
Check: tests
Result: ERROR Running ‘test-AllKrigingConcistency.R’ [8s/10s] Running ‘test-KrigingCopy.R’ Running ‘test-KrigingFit.R’ Running ‘test-KrigingLeaveOneOut.R’ Running ‘test-KrigingLeaveOneOut_3d.R’ Running ‘test-KrigingLogLik.R’ Running ‘test-KrigingLogLikGradHess.R’ [25s/19s] Running ‘test-KrigingMethods.R’ Running ‘test-KrigingPredict.R’ [29s/32s] Running ‘test-KrigingSimulate.R’ Running ‘test-KrigingUpdate.R’ Running ‘test-KrigingUpdateSimulate.R’ Running ‘test-LinearAlgebra.R’ Running ‘test-NoiseKrigingFit.R’ [11s/11s] Running ‘test-NoiseKrigingLogLik.R’ Running ‘test-NoiseKrigingMethods.R’ Running ‘test-NoiseKrigingPredict.R’ [18s/23s] Running ‘test-NoiseKrigingSimulate.R’ [11s/14s] Running ‘test-NoiseKrigingUpdate.R’ Running ‘test-NoiseKrigingUpdateSimulate.R’ [8s/11s] Running ‘test-NuggetKrigingFit.R’ [14s/12s] Running ‘test-NuggetKrigingLogLik.R’ [16s/15s] Running ‘test-NuggetKrigingLogMargPost.R’ [25s/20s] Running ‘test-NuggetKrigingMethods.R’ Running ‘test-NuggetKrigingPredict.R’ [18s/22s] Running ‘test-NuggetKrigingSimulate.R’ Running ‘test-NuggetKrigingUpdate.R’ Running ‘test-NuggetKrigingUpdateSimulate.R’ Running ‘test-RobustGaSP-Nugget.R’ Running ‘test-RobustGaSP.R’ Running ‘test-RobustGaSPtrendlinear.R’ Running ‘test-RobustGaSPvsKrigingLMP.R’ Running ‘test-RobustGaSPvsNuggetKrigingLMP.R’ Running ‘test-SaveLoad.R’ Running ‘test-asDiceKriging.R’ [31s/31s] Running ‘test-estimnone.R’ Running ‘test-normalize.R’ Running ‘test-rlibkriging-demo.R’ Running ‘test-unstableLL.R’ Running the tests in ‘tests/test-RobustGaSP.R’ failed. Complete output: > 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) 2016-2025 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 / Fit: 1D") > > f = function(x) 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) The upper bounds of the range parameters are 184.9743 The initial values of range parameters are 3.699485 Start of the optimization 1 : The number of iterations is 30 The value of the marginal posterior function is 2.497978 Optimized range parameters are 0.1921691 Optimized nugget parameter is 0 Convergence: TRUE The initial values of range parameters are 0.05223118 Start of the optimization 2 : The number of iterations is 30 The value of the marginal posterior function is 1.035387 Optimized range parameters are 0.05296527 Optimized nugget parameter is 0 Convergence: TRUE > #library(rlibkriging) > r <- Kriging(y, X, + kernel="matern5_2", + regmodel = "constant", normalize = FALSE, + optim = "BFGS", + objective = "LMP") OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead. OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set). > # m = as.list(r) > > # Check lmp function > > lmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1); + # print(dim(X)); + apply(X,1, + function(x) { + #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost + y=RobustGaSP:::neg_log_marginal_post_approx_ref(param=(x),nugget=0, nugget.est=model@nugget.est, + R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, + CL=model@CL, + a=0.2, + b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]), + kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha + ) + y})} > lmp_rgasp(1) [1] -1.901254 > > plot(lmp_rgasp,xlim=c(0.01,6)) > abline(v=(log(k@beta_hat))) > > lmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1); + # print(dim(X)); + apply(X,1, + function(x) { + y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1))$logMargPost + y})} > lmp_lk(1) [1] -1.901254 > > lines(seq(0.1,6,,5),lmp_lk(seq(0.1,6,,5)),col='red') > abline(v=(log(1/as.list(r)$theta)),col='red') > > precision <- 1e-3 > test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp is the same that lk/lmp one"), + expect_equal(lmp_rgasp(1),lmp_lk(1),tol = precision)) Test passed 🥳 > test_that(desc=paste0("RobustGaSP / Fit: 1D / fitted theta is the same that RobustGaSP one"), + expect_equal(as.list(r)$theta[1],1/k@beta_hat,tol = precision)) Test passed 🥳 > > > > dlmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1); + # print(dim(X)); + apply(X,1, + function(x) { + + # print(RobustGaSP:::log_marginal_lik_deriv(param=(x),nugget=0,nugget_est=model@nugget.est, + # R0=model@R0,X=model@X, zero_mean=model@zero_mean, + # output=model@output, + # kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha)) + # + # print(RobustGaSP:::log_approx_ref_prior_deriv(param=(x),nugget=0, nugget_est=model@nugget.est, + # CL=model@CL, + # a=0.2, + # b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]))) + + + #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost + y=RobustGaSP:::neg_log_marginal_post_approx_ref_deriv(param=(x),nugget=0, nugget.est=model@nugget.est, + R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, + CL=model@CL, + a=0.2, + b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]), + kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha + ) + y})} > dlmp_rgasp(1) [1] -1.703845 > > dlmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1); + apply(X,1, + function(x) { + y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1),TRUE)$logMargPostGrad + y})} > -exp(-1)*dlmp_lk(1) [1] -1.703845 > > precision <- 1e-3 > test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp deriv is the same that lk/lmp deriv"), + expect_equal(dlmp_rgasp(1),-exp(-1)*dlmp_lk(1),tol = precision)) Test passed 🎉 > > > # Check predict > > ntest <- 10 > 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 🥇 > > > ## RobustGaSP examples > > #--------------------------------------- > # a 1 dimensional example > #--------------------------------------- > context("RobustGaSP / 1 dimensional example") > > > input=10*seq(0,1,1/14) > output<-higdon.1.data(input) > #the following code fit a GaSP with zero mean by setting zero.mean="Yes" > model<- rgasp(design = input, response = output, zero.mean="No") The upper bounds of the range parameters are 670.0756 The initial values of range parameters are 13.40151 Start of the optimization 1 : The number of iterations is 30 The value of the marginal posterior function is -10.48964 Optimized range parameters are 13.2106 Optimized nugget parameter is 0 Convergence: TRUE The initial values of range parameters are 0.08888889 Start of the optimization 2 : The number of iterations is 30 The value of the marginal posterior function is -15.24592 Optimized range parameters are 0.1706386 Optimized nugget parameter is 0 Convergence: TRUE > model Call: rgasp(design = input, response = output, zero.mean = "No") Mean parameters: 2.174187e-10 Variance parameter: 4249.587 Range parameters: 13.2106 Noise parameter: 0 > > testing_input = as.matrix(seq(0,10,1/100)) > model.predict<-predict(model,testing_input) > names(model.predict) [1] "mean" "lower95" "upper95" "sd" > > #########plot predictive distribution > testing_output=higdon.1.data(testing_input) > plot(testing_input,model.predict$mean,type='l',col='blue', + xlab='input',ylab='output') > polygon( c(testing_input,rev(testing_input)),c(model.predict$lower95, + rev(model.predict$upper95)),col = "grey80", border = FALSE) > lines(testing_input, testing_output) > lines(testing_input,model.predict$mean,type='l',col='blue') > lines(input, output,type='p') > > ## mean square erros > mean((model.predict$mean-testing_output)^2) [1] 4.63608e-05 > > model_libK = Kriging(matrix(output,ncol=1), matrix(input,ncol=1), + kernel="matern5_2", + regmodel = "constant", normalize = FALSE, + optim = "BFGS", + objective = "LMP", parameters = NULL) > > lines(testing_input,predict(model_libK,testing_input)$mean,type='l',col='red') > polygon( + c(testing_input,rev(testing_input)), + c( + predict(model_libK,testing_input)$mean+2*predict(model_libK,testing_input)$stdev, + rev(predict(model_libK,testing_input)$mean-2*predict(model_libK,testing_input)$stdev)), + col = rgb(1,0,0,0.1), border = FALSE) > > precision <- 1e-3 > test_that(desc=paste0("RobustGaSP / 1 dimensional example / pred mean is the same that RobustGaSP one"), + expect_equal(predict(model_libK,0.7)$mean[1],predict(model,matrix(0.7))$mean,tol = precision)) ── Failure: RobustGaSP / 1 dimensional example / pred mean is the same that RobustGaSP one ── predict(model_libK, 0.7)$mean[1] not equal to predict(model, matrix(0.7))$mean. 1/1 mismatches [1] 0.621 - 0.623 == -0.00162 Error: ! Test failed Backtrace: ▆ 1. ├─testthat::test_that(...) 2. │ └─withr (local) `<fn>`() 3. └─reporter$stop_if_needed() 4. └─rlang::abort("Test failed", call = NULL) Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.9-1
Check: tests
Result: ERROR Running ‘test-AllKrigingConcistency.R’ Running ‘test-KrigingCopy.R’ Running ‘test-KrigingFit.R’ Running ‘test-KrigingLeaveOneOut.R’ Running ‘test-KrigingLeaveOneOut_3d.R’ Running ‘test-KrigingLogLik.R’ Running ‘test-KrigingLogLikGradHess.R’ [30s/30s] Running ‘test-KrigingMethods.R’ Running ‘test-KrigingPredict.R’ [24s/28s] Running ‘test-KrigingSimulate.R’ Running ‘test-KrigingUpdate.R’ Running ‘test-KrigingUpdateSimulate.R’ Running ‘test-LinearAlgebra.R’ Running ‘test-NoiseKrigingFit.R’ [11s/11s] Running ‘test-NoiseKrigingLogLik.R’ Running ‘test-NoiseKrigingMethods.R’ Running ‘test-NoiseKrigingPredict.R’ [17s/19s] Running ‘test-NoiseKrigingSimulate.R’ [10s/12s] Running ‘test-NoiseKrigingUpdate.R’ Running ‘test-NoiseKrigingUpdateSimulate.R’ Running ‘test-NuggetKrigingFit.R’ [15s/16s] Running ‘test-NuggetKrigingLogLik.R’ [10s/12s] Running ‘test-NuggetKrigingLogMargPost.R’ [18s/21s] Running ‘test-NuggetKrigingMethods.R’ Running ‘test-NuggetKrigingPredict.R’ [17s/19s] Running ‘test-NuggetKrigingSimulate.R’ Running ‘test-NuggetKrigingUpdate.R’ Running ‘test-NuggetKrigingUpdateSimulate.R’ Running ‘test-RobustGaSP-Nugget.R’ Running ‘test-RobustGaSP.R’ Running ‘test-RobustGaSPtrendlinear.R’ Running ‘test-RobustGaSPvsKrigingLMP.R’ Running ‘test-RobustGaSPvsNuggetKrigingLMP.R’ Running ‘test-SaveLoad.R’ Running ‘test-asDiceKriging.R’ [21s/26s] Running ‘test-estimnone.R’ Running ‘test-normalize.R’ Running ‘test-rlibkriging-demo.R’ Running ‘test-unstableLL.R’ Running the tests in ‘tests/test-RobustGaSP.R’ failed. Complete output: > 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) 2016-2025 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 / Fit: 1D") > > f = function(x) 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) The upper bounds of the range parameters are 184.9743 The initial values of range parameters are 3.699485 Start of the optimization 1 : The number of iterations is 30 The value of the marginal posterior function is 2.497978 Optimized range parameters are 0.1921691 Optimized nugget parameter is 0 Convergence: TRUE The initial values of range parameters are 0.05223118 Start of the optimization 2 : The number of iterations is 30 The value of the marginal posterior function is 1.035387 Optimized range parameters are 0.05296527 Optimized nugget parameter is 0 Convergence: TRUE > #library(rlibkriging) > r <- Kriging(y, X, + kernel="matern5_2", + regmodel = "constant", normalize = FALSE, + optim = "BFGS", + objective = "LMP") > # m = as.list(r) > > # Check lmp function > > lmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1); + # print(dim(X)); + apply(X,1, + function(x) { + #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost + y=RobustGaSP:::neg_log_marginal_post_approx_ref(param=(x),nugget=0, nugget.est=model@nugget.est, + R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, + CL=model@CL, + a=0.2, + b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]), + kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha + ) + y})} > lmp_rgasp(1) [1] -1.901254 > > plot(lmp_rgasp,xlim=c(0.01,6)) > abline(v=(log(k@beta_hat))) > > lmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1); + # print(dim(X)); + apply(X,1, + function(x) { + y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1))$logMargPost + y})} > lmp_lk(1) [1] -1.901254 > > lines(seq(0.1,6,,5),lmp_lk(seq(0.1,6,,5)),col='red') > abline(v=(log(1/as.list(r)$theta)),col='red') > > precision <- 1e-3 > test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp is the same that lk/lmp one"), + expect_equal(lmp_rgasp(1),lmp_lk(1),tol = precision)) Test passed 🥳 > test_that(desc=paste0("RobustGaSP / Fit: 1D / fitted theta is the same that RobustGaSP one"), + expect_equal(as.list(r)$theta[1],1/k@beta_hat,tol = precision)) Test passed 🥳 > > > > dlmp_rgasp = function(X, model=k) {if (!is.matrix(X)) X = matrix(X,ncol=1); + # print(dim(X)); + apply(X,1, + function(x) { + + # print(RobustGaSP:::log_marginal_lik_deriv(param=(x),nugget=0,nugget_est=model@nugget.est, + # R0=model@R0,X=model@X, zero_mean=model@zero_mean, + # output=model@output, + # kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha)) + # + # print(RobustGaSP:::log_approx_ref_prior_deriv(param=(x),nugget=0, nugget_est=model@nugget.est, + # CL=model@CL, + # a=0.2, + # b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]))) + + + #y=-logMargPostFun(r,matrix(unlist(x),ncol=2))$logMargPost + y=RobustGaSP:::neg_log_marginal_post_approx_ref_deriv(param=(x),nugget=0, nugget.est=model@nugget.est, + R0=model@R0,X=model@X, zero_mean=model@zero_mean,output=model@output, + CL=model@CL, + a=0.2, + b=1/(length(model@output))^{1/dim(as.matrix(model@input))[2]}*(0.2+dim(as.matrix(model@input))[2]), + kernel_type=rep(as.integer(3),ncol(X)),alpha=model@alpha + ) + y})} > dlmp_rgasp(1) [1] -1.703845 > > dlmp_lk = function(X) {if (!is.matrix(X)) X = matrix(X,ncol=1); + apply(X,1, + function(x) { + y=-logMargPostFun(r,matrix(unlist(exp(-(x))),ncol=1),TRUE)$logMargPostGrad + y})} > -exp(-1)*dlmp_lk(1) [1] -1.703845 > > precision <- 1e-3 > test_that(desc=paste0("RobustGaSP / Fit: 1D / rgasp/lmp deriv is the same that lk/lmp deriv"), + expect_equal(dlmp_rgasp(1),-exp(-1)*dlmp_lk(1),tol = precision)) Test passed 🎉 > > > # Check predict > > ntest <- 10 > 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 🥇 > > > ## RobustGaSP examples > > #--------------------------------------- > # a 1 dimensional example > #--------------------------------------- > context("RobustGaSP / 1 dimensional example") > > > input=10*seq(0,1,1/14) > output<-higdon.1.data(input) > #the following code fit a GaSP with zero mean by setting zero.mean="Yes" > model<- rgasp(design = input, response = output, zero.mean="No") The upper bounds of the range parameters are 670.0756 The initial values of range parameters are 13.40151 Start of the optimization 1 : The number of iterations is 30 The value of the marginal posterior function is -10.48964 Optimized range parameters are 13.2106 Optimized nugget parameter is 0 Convergence: TRUE The initial values of range parameters are 0.08888889 Start of the optimization 2 : The number of iterations is 30 The value of the marginal posterior function is -15.24592 Optimized range parameters are 0.1706386 Optimized nugget parameter is 0 Convergence: TRUE > model Call: rgasp(design = input, response = output, zero.mean = "No") Mean parameters: 2.174187e-10 Variance parameter: 4249.587 Range parameters: 13.2106 Noise parameter: 0 > > testing_input = as.matrix(seq(0,10,1/100)) > model.predict<-predict(model,testing_input) > names(model.predict) [1] "mean" "lower95" "upper95" "sd" > > #########plot predictive distribution > testing_output=higdon.1.data(testing_input) > plot(testing_input,model.predict$mean,type='l',col='blue', + xlab='input',ylab='output') > polygon( c(testing_input,rev(testing_input)),c(model.predict$lower95, + rev(model.predict$upper95)),col = "grey80", border = FALSE) > lines(testing_input, testing_output) > lines(testing_input,model.predict$mean,type='l',col='blue') > lines(input, output,type='p') > > ## mean square erros > mean((model.predict$mean-testing_output)^2) [1] 4.63608e-05 > > model_libK = Kriging(matrix(output,ncol=1), matrix(input,ncol=1), + kernel="matern5_2", + regmodel = "constant", normalize = FALSE, + optim = "BFGS", + objective = "LMP", parameters = NULL) > > lines(testing_input,predict(model_libK,testing_input)$mean,type='l',col='red') > polygon( + c(testing_input,rev(testing_input)), + c( + predict(model_libK,testing_input)$mean+2*predict(model_libK,testing_input)$stdev, + rev(predict(model_libK,testing_input)$mean-2*predict(model_libK,testing_input)$stdev)), + col = rgb(1,0,0,0.1), border = FALSE) > > precision <- 1e-3 > test_that(desc=paste0("RobustGaSP / 1 dimensional example / pred mean is the same that RobustGaSP one"), + expect_equal(predict(model_libK,0.7)$mean[1],predict(model,matrix(0.7))$mean,tol = precision)) ── Failure: RobustGaSP / 1 dimensional example / pred mean is the same that RobustGaSP one ── predict(model_libK, 0.7)$mean[1] not equal to predict(model, matrix(0.7))$mean. 1/1 mismatches [1] 0.621 - 0.623 == -0.00162 Error: ! Test failed Backtrace: ▆ 1. ├─testthat::test_that(...) 2. │ └─withr (local) `<fn>`() 3. └─reporter$stop_if_needed() 4. └─rlang::abort("Test failed", call = NULL) Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.9-1
Check: for GNU extensions in Makefiles
Result: NOTE GNU make is a SystemRequirements. Flavors: r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Version: 0.9-1
Check: installed package size
Result: NOTE installed size is 71.3Mb sub-directories of 1Mb or more: include 7.3Mb lib 37.3Mb libs 26.2Mb Flavors: r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64