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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > > test_check("ngme2") Loading required package: ngme2 This is ngme2 of version 0.9.7 - See our homepage: https://davidbolin.github.io/ngme2 for more details. Attaching package: 'ngme2' The following object is masked from 'package:stats': ar List of 6 $ mean : num [1:3] -1.955 0.862 -1.791 $ sd : num [1:3] 0.893 0.877 0.904 $ 0.05q : num [1:3] -3.437 -0.546 -3.21 $ 0.95q : num [1:3] -0.529 2.367 -0.317 $ median: num [1:3] -1.93 0.819 -1.774 $ mode : num [1:3] -2.3 0.3 -2.3 - attr(*, "samples")= num [1:10, 1:500] -0.2038 0.7416 -0.0278 0.9352 2.5911 ... Starting estimation... iteration = : 1 grad.norm() = 508.376 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 490.69 pflug_sum = 248641, max_pflug_sum = 248641 --------------------------- iteration = : 3 grad.norm() = 464.671 pflug_sum = 475935, max_pflug_sum = 475935 --------------------------- iteration = : 4 grad.norm() = 442.36 pflug_sum = 681119, max_pflug_sum = 681119 --------------------------- iteration = : 5 grad.norm() = 419.916 pflug_sum = 866096, max_pflug_sum = 866096 --------------------------- iteration = : 6 grad.norm() = 393.325 pflug_sum = 1.03026e+06, max_pflug_sum = 1.03026e+06 --------------------------- iteration = : 7 grad.norm() = 367.99 pflug_sum = 1.17413e+06, max_pflug_sum = 1.17413e+06 --------------------------- iteration = : 8 grad.norm() = 331.495 pflug_sum = 1.29543e+06, max_pflug_sum = 1.29543e+06 --------------------------- iteration = : 9 grad.norm() = 295.475 pflug_sum = 1.39241e+06, max_pflug_sum = 1.39241e+06 --------------------------- iteration = : 10 grad.norm() = 267.686 pflug_sum = 1.47112e+06, max_pflug_sum = 1.47112e+06 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 0.278311251585783 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. Loading required package: Matrix This is rSPDE 2.5.2 - See https://davidbolin.github.io/rSPDE for vignettes and manuals. Attaching package: 'rSPDE' The following object is masked from 'package:ngme2': cross_validation Starting estimation... iteration = : 1 grad.norm() = 0.119028 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 0.0845627 pflug_sum = 0.643771, max_pflug_sum = 0.643771 --------------------------- iteration = : 3 grad.norm() = 0.0768732 pflug_sum = -0.104354, max_pflug_sum = 0.643771 --------------------------- iteration = : 4 grad.norm() = 0.0116744 pflug_sum = 0.521821, max_pflug_sum = 0.643771 --------------------------- iteration = : 5 grad.norm() = 0.0924795 pflug_sum = 0.765462, max_pflug_sum = 0.765462 --------------------------- iteration = : 6 grad.norm() = 0.0182361 pflug_sum = 0.898705, max_pflug_sum = 0.898705 --------------------------- iteration = : 7 grad.norm() = 0.0622531 pflug_sum = 0.579652, max_pflug_sum = 0.898705 --------------------------- iteration = : 8 grad.norm() = 0.050609 pflug_sum = 0.504831, max_pflug_sum = 0.898705 --------------------------- iteration = : 9 grad.norm() = 0.0231656 pflug_sum = 0.529228, max_pflug_sum = 0.898705 --------------------------- iteration = : 10 grad.norm() = 0.0562283 pflug_sum = 0.624516, max_pflug_sum = 0.898705 --------------------------- iteration = : 11 grad.norm() = 0.0229931 pflug_sum = -0.0545081, max_pflug_sum = 0.898705 --------------------------- iteration = : 12 grad.norm() = 0.0463687 pflug_sum = -0.0287632, max_pflug_sum = 0.898705 --------------------------- iteration = : 13 grad.norm() = 0.0271082 pflug_sum = -0.0386737, max_pflug_sum = 0.898705 --------------------------- iteration = : 14 grad.norm() = 0.0252547 pflug_sum = -0.0897751, max_pflug_sum = 0.898705 --------------------------- iteration = : 15 grad.norm() = 0.176207 pflug_sum = -0.402984, max_pflug_sum = 0.898705 --------------------------- iteration = : 16 grad.norm() = 0.0830797 pflug_sum = -0.726982, max_pflug_sum = 0.898705 --------------------------- iteration = : 17 grad.norm() = 0.0121385 pflug_sum = -0.820216, max_pflug_sum = 0.898705 --------------------------- iteration = : 18 grad.norm() = 0.0153467 pflug_sum = -0.796924, max_pflug_sum = 0.898705 --------------------------- iteration = : 19 grad.norm() = 0.0352049 pflug_sum = -0.65466, max_pflug_sum = 0.898705 --------------------------- iteration = : 20 grad.norm() = 0.0972474 pflug_sum = -0.702522, max_pflug_sum = 0.898705 --------------------------- iteration = : 21 grad.norm() = 0.0635173 pflug_sum = -1.20633, max_pflug_sum = 0.898705 --------------------------- iteration = : 22 grad.norm() = 0.0625852 pflug_sum = -1.33204, max_pflug_sum = 0.898705 --------------------------- iteration = : 23 grad.norm() = 0.117812 pflug_sum = -1.18645, max_pflug_sum = 0.898705 --------------------------- iteration = : 24 grad.norm() = 0.114813 pflug_sum = -1.71857, max_pflug_sum = 0.898705 --------------------------- iteration = : 25 grad.norm() = 0.0455932 pflug_sum = -1.52684, max_pflug_sum = 0.898705 --------------------------- iteration = : 26 grad.norm() = 0.0287309 pflug_sum = -1.4818, max_pflug_sum = 0.898705 --------------------------- iteration = : 27 grad.norm() = 0.0446366 pflug_sum = -1.21637, max_pflug_sum = 0.898705 --------------------------- iteration = : 28 grad.norm() = 0.0839318 pflug_sum = -1.02862, max_pflug_sum = 0.898705 --------------------------- iteration = : 29 grad.norm() = 0.0178665 pflug_sum = -1.14191, max_pflug_sum = 0.898705 --------------------------- iteration = : 30 grad.norm() = 0.0170158 pflug_sum = -1.0455, max_pflug_sum = 0.898705 --------------------------- iteration = : 31 grad.norm() = 0.024996 pflug_sum = -0.975043, max_pflug_sum = 0.898705 --------------------------- iteration = : 32 grad.norm() = 0.0600153 pflug_sum = -1.12404, max_pflug_sum = 0.898705 --------------------------- iteration = : 33 grad.norm() = 0.0107212 pflug_sum = -0.948211, max_pflug_sum = 0.898705 --------------------------- iteration = : 34 grad.norm() = 0.117185 pflug_sum = -1.12754, max_pflug_sum = 0.898705 --------------------------- iteration = : 35 grad.norm() = 0.0279467 pflug_sum = -1.21397, max_pflug_sum = 0.898705 --------------------------- iteration = : 36 grad.norm() = 0.0432204 pflug_sum = -1.39009, max_pflug_sum = 0.898705 --------------------------- iteration = : 37 grad.norm() = 0.042261 pflug_sum = -1.30145, max_pflug_sum = 0.898705 --------------------------- iteration = : 38 grad.norm() = 0.0187992 pflug_sum = -1.24702, max_pflug_sum = 0.898705 --------------------------- iteration = : 39 grad.norm() = 0.0257576 pflug_sum = -1.03398, max_pflug_sum = 0.898705 --------------------------- iteration = : 40 grad.norm() = 0.0434089 pflug_sum = -1.19778, max_pflug_sum = 0.898705 --------------------------- iteration = : 41 grad.norm() = 0.012598 pflug_sum = -1.29971, max_pflug_sum = 0.898705 --------------------------- iteration = : 42 grad.norm() = 0.114515 pflug_sum = -1.55805, max_pflug_sum = 0.898705 --------------------------- iteration = : 43 grad.norm() = 0.0430777 pflug_sum = -1.67294, max_pflug_sum = 0.898705 --------------------------- iteration = : 44 grad.norm() = 0.0791258 pflug_sum = -1.70669, max_pflug_sum = 0.898705 --------------------------- iteration = : 45 grad.norm() = 0.100382 pflug_sum = -1.37336, max_pflug_sum = 0.898705 --------------------------- iteration = : 46 grad.norm() = 0.0159872 pflug_sum = -1.35076, max_pflug_sum = 0.898705 --------------------------- iteration = : 47 grad.norm() = 0.0384051 pflug_sum = -1.43788, max_pflug_sum = 0.898705 --------------------------- iteration = : 48 grad.norm() = 0.0697459 pflug_sum = -1.37294, max_pflug_sum = 0.898705 --------------------------- iteration = : 49 grad.norm() = 0.0109498 pflug_sum = -1.47067, max_pflug_sum = 0.898705 --------------------------- iteration = : 50 grad.norm() = 0.0309797 pflug_sum = -1.86989, max_pflug_sum = 0.898705 --------------------------- iteration = : 51 grad.norm() = 0.0257023 pflug_sum = -1.91996, max_pflug_sum = 0.898705 --------------------------- iteration = : 52 grad.norm() = 0.101116 pflug_sum = -1.75489, max_pflug_sum = 0.898705 --------------------------- iteration = : 53 grad.norm() = 0.0903761 pflug_sum = -2.06926, max_pflug_sum = 0.898705 --------------------------- iteration = : 54 grad.norm() = 0.0999765 pflug_sum = -2.61328, max_pflug_sum = 0.898705 --------------------------- iteration = : 55 grad.norm() = 0.0916922 pflug_sum = -3.07193, max_pflug_sum = 0.898705 --------------------------- iteration = : 56 grad.norm() = 0.090293 pflug_sum = -3.62157, max_pflug_sum = 0.898705 --------------------------- iteration = : 57 grad.norm() = 0.0046624 pflug_sum = -3.3365, max_pflug_sum = 0.898705 --------------------------- iteration = : 58 grad.norm() = 0.0565452 pflug_sum = -3.13706, max_pflug_sum = 0.898705 --------------------------- iteration = : 59 grad.norm() = 0.00963944 pflug_sum = -2.90746, max_pflug_sum = 0.898705 --------------------------- iteration = : 60 grad.norm() = 0.012589 pflug_sum = -2.85907, max_pflug_sum = 0.898705 --------------------------- Pflug diagnostic satisfied: pflug_sum < 0.9 * max_pflug_sum for all chains. Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: 2.10252416577165 Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. [1] 0.2653202 5 x 5 sparse Matrix of class "dgCMatrix" [1,] 0.8660254 . . . . [2,] -0.5000000 1.0 . . . [3,] . -0.5 1.0 . . [4,] . . -0.5 1.0 . [5,] . . . -0.5 1 [1] 0.2653202 [1] 0.2536816 [1] 0.2536816 [1] "rho" "c1" "c2" "rho (1st)" "rho (2nd)" "sigma_1" [7] "sigma_2" "sigma_1" Starting estimation... iteration = : 1 grad.norm() = 74.7968 pflug_sum = 0, max_pflug_sum = 0 --------------------------- iteration = : 2 grad.norm() = 72.1429 pflug_sum = 5396.06, max_pflug_sum = 5396.06 --------------------------- iteration = : 3 grad.norm() = 69.1991 pflug_sum = 10377.5, max_pflug_sum = 10377.5 --------------------------- iteration = : 4 grad.norm() = 65.9925 pflug_sum = 14937.3, max_pflug_sum = 14937.3 --------------------------- iteration = : 5 grad.norm() = 62.4577 pflug_sum = 19048.7, max_pflug_sum = 19048.7 --------------------------- iteration = : 6 grad.norm() = 58.5884 pflug_sum = 22707.4, max_pflug_sum = 22707.4 --------------------------- iteration = : 7 grad.norm() = 54.2824 pflug_sum = 25886.1, max_pflug_sum = 25886.1 --------------------------- iteration = : 8 grad.norm() = 49.5932 pflug_sum = 28571.5, max_pflug_sum = 28571.5 --------------------------- iteration = : 9 grad.norm() = 44.6231 pflug_sum = 30778.9, max_pflug_sum = 30778.9 --------------------------- iteration = : 10 grad.norm() = 39.1702 pflug_sum = 32526.5, max_pflug_sum = 32526.5 --------------------------- Starting posterior sampling... Posterior sampling done. Average standard deviation of the posterior W: NA Use ngme_post_samples() to access posterior samples and ngme_result() to access latent model parameters. [ FAIL 0 | WARN 0 | SKIP 10 | PASS 361 ] ══ Skipped tests (10) ══════════════════════════════════════════════════════════ • On CRAN (2): 'test-compose-sum-ar1-matern.R:2:3', 'test-regression-fe-rank-check.R:25:3' • empty test (7): 'test-compose-bv.R:1:1', 'test-compose-bv.R:35:1', 'test-compose-bv.R:110:1', 'test-compose-bv.R:175:1', 'test-core-model-defs.R:20:1', 'test-core-model-defs.R:54:1', 'test-core-model-defs.R:77:1' • {INLA} is not installed. (1): 'test-core-fractional-model.R:77:3' [ FAIL 0 | WARN 0 | SKIP 10 | PASS 361 ] > > proc.time() user system elapsed 100.35 1.32 101.67