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inla-manjaro-no-pardiso

fernandomayer
Jun 25th, 2024
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  1. > inla.pardiso.check()
  2.  
  3.  
  4.     GitId: 39a87469400fa169507c508366eca6ae7588cfdf - Wed Jun 19 21:58:47 2024 +0900
  5.     Error:24 Reason: License file for PARDISO: Not found
  6.     Line:649 Function: GMRFLib_pardiso_check_install
  7.  
  8.  [1] ""                                                                            
  9.  [2] ""                                                                            
  10.  [3] "*************************************************************************** "
  11.  [4] "CONTAINS Runtime Modules of Parallel Sparse Linear Solver PARDISO Vers. 8.2"
  12.  [5] "Copyright Panua Technologies, Switzerland [2022-2023], All Rights Reserved."
  13.  [6] "No license file found. Please see                                  "        
  14.  [7] "            https://www.panua.ch/products/pardiso                         "  
  15.  [8] "where to place the license file panua.lic "                                  
  16.  [9] "*************************************************************************** "
  17. [10] "FAILURE: PARDISO IS NOT INSTALLED OR NOT WORKING"                            
  18. > n = 100; a = 1; b = 1; tau = 100
  19. > z = rnorm(n)
  20. > eta = a + b*z
  21. > scale = exp(rnorm(n))
  22. > prec = scale*tau
  23. > y = rnorm(n, mean = eta, sd = 1/sqrt(prec))
  24. > data = list(y=y, z=z)
  25. > formula = y ~ 1+z
  26. > result = inla(formula, family = "gaussian", data = data,
  27. +     verbose = TRUE)
  28.  
  29.  
  30. ***************************************************************************
  31. CONTAINS Runtime Modules of Parallel Sparse Linear Solver PARDISO Vers. 8.2
  32. Copyright Panua Technologies, Switzerland [2022-2023], All Rights Reserved.
  33. No license file found. Please see                                  
  34.             https://www.panua.ch/products/pardiso                        
  35. where to place the license file panua.lic
  36. ***************************************************************************
  37.     Read ntt 16 1 with max.threads 16
  38.     Found num.threads = 16:1 max_threads = 16
  39.  
  40.     39a87469400fa169507c508366eca6ae7588cfdf - Wed Jun 19 21:58:47 2024 +0900
  41. Report bugs to <help@r-inla.org>
  42. Set reordering to id=[0] and name=[default]
  43. Process file[/tmp/RtmpfHspX1/file5aea3a30ac8b/Model.ini] threads[16] max.threads[16] blas_threads[1] nested[16:1]
  44. inla_build...
  45.     number of sections=[10]
  46.     parse section=[0] name=[INLA.libR] type=[LIBR]
  47.     inla_parse_libR...
  48.         section[INLA.libR]
  49.             R_HOME=[/usr/local/lib64/R]
  50.     parse section=[7] name=[INLA.Expert] type=[EXPERT]
  51.     inla_parse_expert...
  52.         section[INLA.Expert]
  53.             disable.gaussian.check=[0]
  54.             Measure dot.product.gain=[No]
  55.             cpo.manual=[0]
  56.             jp.file=[(null)]
  57.             jp.model=[(null)]
  58.     parse section=[1] name=[INLA.Model] type=[PROBLEM]
  59.     inla_parse_problem...
  60.         name=[INLA.Model]
  61.         R-INLA version = [24.06.19]
  62.         R-INLA build date = [19893]
  63.         Build tag = [devel]
  64.         System memory = [31.0Gb]
  65.         Cores = (Physical= 16, Logical= 16)
  66.         'char' is signed
  67.         BUFSIZ is 8192
  68.         openmp.strategy=[default]
  69.         pardiso-library installed and working? = [no]
  70.         smtp = [taucs]
  71.         strategy = [default]
  72.         store results in directory=[/tmp/RtmpfHspX1/file5aea3a30ac8b/results.files]
  73.         output:
  74.             gcpo=[0]
  75.                 num.level.sets=[-1]
  76.                 size.max=[32]
  77.                 strategy=[Posterior]
  78.                 correct.hyperpar=[1]
  79.                 epsilon=[0.005]
  80.                 prior.diagonal=[0.0001]
  81.                 keep=[]
  82.                 remove.fixed=[1]
  83.                 remove=[]
  84.             cpo=[0]
  85.             po=[0]
  86.             dic=[0]
  87.             kld=[1]
  88.             mlik=[1]
  89.             q=[0]
  90.             graph=[0]
  91.             hyperparameters=[1]
  92.             config=[0]
  93.             config.lite=[0]
  94.             likelihood.info=[0]
  95.             internal.opt=[1]
  96.             save.memory=[0]
  97.             summary=[1]
  98.             return.marginals=[1]
  99.             return.marginals.predictor=[0]
  100.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  101.             ncdf=[0]  [ ]
  102.     parse section=[3] name=[Predictor] type=[PREDICTOR]
  103.     inla_parse_predictor ...
  104.         section=[Predictor]
  105.         dir=[predictor]
  106.         PRIOR->name=[loggamma]
  107.         hyperid=[53001|Predictor]
  108.         PRIOR->from_theta=[function (x) <<NEWLINE>>exp(x)]
  109.         PRIOR->to_theta = [function (x) <<NEWLINE>>log(x)]
  110.         PRIOR->PARAMETERS=[1, 1e-05]
  111.         initialise log_precision[13.8155]
  112.         fixed=[1]
  113.         user.scale=[1]
  114.         n=[100]
  115.         m=[0]
  116.         ndata=[100]
  117.         compute=[1]
  118.         read offsets from file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7]
  119.         read n=[200] entries from file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7]
  120.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 0/100  (idx,y) = (0, 0)
  121.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 1/100  (idx,y) = (1, 0)
  122.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 2/100  (idx,y) = (2, 0)
  123.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 3/100  (idx,y) = (3, 0)
  124.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 4/100  (idx,y) = (4, 0)
  125.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 5/100  (idx,y) = (5, 0)
  126.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 6/100  (idx,y) = (6, 0)
  127.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 7/100  (idx,y) = (7, 0)
  128.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 8/100  (idx,y) = (8, 0)
  129.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea25f30ec7] 9/100  (idx,y) = (9, 0)
  130.         A=[(null)]
  131.         Aext=[(null)]
  132.         AextPrecision=[1e+08]
  133.         output:
  134.             summary=[1]
  135.             return.marginals=[1]
  136.             return.marginals.predictor=[0]
  137.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  138.             ncdf=[0]  [ ]
  139.     parse section=[2] name=[INLA.Data1] type=[DATA]
  140.     inla_parse_data [section 1]...
  141.         tag=[INLA.Data1]
  142.         family=[GAUSSIAN]
  143.         likelihood=[GAUSSIAN]
  144.         file->name=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea7d0e7323]
  145.         file->name=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea8eb8681]
  146.         file->name=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea7a811c00]
  147.         file->name=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea510d6ec7]
  148.         read n=[300] entries from file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea7d0e7323]
  149.             0/100  (idx,a,y,d) = (0, 1, 1.02769, 1)
  150.             1/100  (idx,a,y,d) = (1, 1, 1.09805, 1)
  151.             2/100  (idx,a,y,d) = (2, 1, 2.65996, 1)
  152.             3/100  (idx,a,y,d) = (3, 1, 0.704929, 1)
  153.             4/100  (idx,a,y,d) = (4, 1, -0.262727, 1)
  154.             5/100  (idx,a,y,d) = (5, 1, -0.526566, 1)
  155.             6/100  (idx,a,y,d) = (6, 1, 0.717622, 1)
  156.             7/100  (idx,a,y,d) = (7, 1, 1.11993, 1)
  157.             8/100  (idx,a,y,d) = (8, 1, -0.525064, 1)
  158.             9/100  (idx,a,y,d) = (9, 1, 2.46091, 1)
  159.         likelihood.variant=[0]
  160.         initialise log_precision[4]
  161.         fixed0=[0]
  162.         PRIOR0->name=[loggamma]
  163.         hyperid=[65001|INLA.Data1]
  164.         PRIOR0->from_theta=[function (x) <<NEWLINE>>exp(x)]
  165.         PRIOR0->to_theta = [function (x) <<NEWLINE>>log(x)]
  166.         PRIOR0->PARAMETERS0=[1, 5e-05]
  167.         initialise log_precision offset[72.0873]
  168.         fixed1=[1]
  169.         PRIOR1->name=[none]
  170.         hyperid=[65002|INLA.Data1]
  171.         PRIOR1->from_theta=[function (x) <<NEWLINE>>exp(x)]
  172.         PRIOR1->to_theta = [function (x) <<NEWLINE>>log(x)]
  173.         PRIOR1->PARAMETERS1=[]
  174.         Link model   [IDENTITY]
  175.         Link order   [-1]
  176.         Link variant [-1]
  177.         Link a       [1]
  178.         Link ntheta  [0]
  179.         mix.use[0]
  180.     section=[4] name=[(Intercept)] type=[LINEAR]
  181.     inla_parse_linear...
  182.         section[(Intercept)]
  183.         dir=[fixed.effect00000001]
  184.         file for covariates=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54]
  185.         read n=[200] entries from file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54]
  186.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 0/100  (idx,y) = (0, 1)
  187.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 1/100  (idx,y) = (1, 1)
  188.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 2/100  (idx,y) = (2, 1)
  189.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 3/100  (idx,y) = (3, 1)
  190.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 4/100  (idx,y) = (4, 1)
  191.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 5/100  (idx,y) = (5, 1)
  192.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 6/100  (idx,y) = (6, 1)
  193.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 7/100  (idx,y) = (7, 1)
  194.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 8/100  (idx,y) = (8, 1)
  195.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea9e6ef54] 9/100  (idx,y) = (9, 1)
  196.         prior mean=[0]
  197.         prior precision=[0]
  198.         compute=[1]
  199.         output:
  200.             summary=[1]
  201.             return.marginals=[1]
  202.             return.marginals.predictor=[0]
  203.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  204.             ncdf=[0]  [ ]
  205.     section=[5] name=[z] type=[LINEAR]
  206.     inla_parse_linear...
  207.         section[z]
  208.         dir=[fixed.effect00000002]
  209.         file for covariates=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725]
  210.         read n=[200] entries from file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725]
  211.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 0/100  (idx,y) = (0, -0.0818444)
  212.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 1/100  (idx,y) = (1, -0.0779361)
  213.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 2/100  (idx,y) = (2, 1.60972)
  214.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 3/100  (idx,y) = (3, -0.172882)
  215.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 4/100  (idx,y) = (4, -1.23245)
  216.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 5/100  (idx,y) = (5, -1.52436)
  217.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 6/100  (idx,y) = (6, -0.276537)
  218.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 7/100  (idx,y) = (7, 0.147262)
  219.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 8/100  (idx,y) = (8, -1.37884)
  220.         file=[/tmp/RtmpfHspX1/file5aea3a30ac8b/data.files/file5aea160ab725] 9/100  (idx,y) = (9, 1.51115)
  221.         prior mean=[0]
  222.         prior precision=[0.001]
  223.         compute=[1]
  224.         output:
  225.             summary=[1]
  226.             return.marginals=[1]
  227.             return.marginals.predictor=[0]
  228.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  229.             ncdf=[0]  [ ]
  230.     parse section=[9] name=[INLA.pardiso] type=[PARDISO]
  231.     inla_parse_pardiso...
  232.         section[INLA.pardiso]
  233.         verbose[0]
  234.         debug[0]
  235.         parallel.reordering[1]
  236.         nrhs[-1]
  237.     parse section=[8] name=[INLA.lp.scale] type=[LP.SCALE]
  238.     inla_parse_lp_scale...
  239.         section[INLA.lp.scale]
  240.  
  241.     Index table: number of entries[3], total length[102]
  242.         tag                            start-index     length
  243.         Predictor                               0        100
  244.         (Intercept)                           100          1
  245.         z                                     101          1
  246.     List of hyperparameters:
  247.         theta[0] = [Log precision for the Gaussian observations]
  248.  
  249.     parse section=[6] name=[INLA.Parameters] type=[INLA]
  250.     inla_parse_INLA...
  251.         section[INLA.Parameters]
  252.             lincomb.derived.correlation.matrix = [No]
  253.         global_node.factor = 2.000
  254.         global_node.degree = 2147483647
  255.         reordering = -1
  256.         constr.marginal.diagonal = 1.49e-08
  257. Contents of ai_param 0x5eb137ca8480
  258.     Optimiser: DEFAULT METHOD
  259.         Option for GSL-BFGS2: tol  = 0.1
  260.         Option for GSL-BFGS2: step_size = 1
  261.         Option for GSL-BFGS2: epsx = 0.001
  262.         Option for GSL-BFGS2: epsf = 0.002
  263.         Option for GSL-BFGS2: epsg = 0.005
  264.         Restart: 0
  265.         Optimise: try to be smart: Yes
  266.         Optimise: use directions: Yes
  267.         Mode restart: Yes
  268.         Mode fixed: No
  269.         Mode use_mode: No
  270.         parallel linesearch [0]
  271.     Gaussian approximation:
  272.         tolerance_func = 0.002
  273.         tolerance_step = 5e-06
  274.         optpar_fp = 0
  275.         optpar_nr_step_factor = -0.1
  276.     Gaussian data: Yes
  277.     Strategy:   Use a mean-skew corrected Gaussian by fitting a Skew-Normal
  278.     Fast mode:  On
  279.     Use linear approximation to log(|Q +c|)? Yes
  280.         Method:  Compute the derivative exact
  281.     Parameters for improved approximations
  282.         Number of points evaluate:   9
  283.         Step length to compute derivatives numerically:  0.0001
  284.         Stencil to compute derivatives numerically:  5
  285.         Cutoff value to construct local neigborhood:     0.0001
  286.     Log calculations:    On
  287.     Log calculated marginal for the hyperparameters:     On
  288.     Integration strategy:    Automatic (GRID for dim(theta)=1 and 2 and otherwise CCD)
  289.         f0 (CCD only):   1.100
  290.         dz (GRID only):  0.750
  291.         Adjust weights (GRID only):  On
  292.         Difference in log-density limit (GRID only):     6.000
  293.         Skip configurations with (presumed) small density (GRID only):   On
  294.     Gradient is computed using Central difference with step-length 0.005000
  295.     Hessian is computed using Central difference with step-length 0.070711
  296.     Hessian matrix is forced to be a diagonal matrix? [No]
  297.     Compute effective number of parameters? [Yes]
  298.     Perform a Monte Carlo error-test? [No]
  299.     Interpolator [Auto]
  300.     CPO required diff in log-density [3]
  301.     Stupid search mode:
  302.         Status     [On]
  303.         Max iter   [1000]
  304.         Factor     [1.05]
  305.     Numerical integration of hyperparameters:
  306.         Maximum number of function evaluations [100000]
  307.         Relative error ....................... [1e-05]
  308.         Absolute error ....................... [1e-06]
  309.     To stabilise the numerical optimisation:
  310.         Minimum value of the -Hessian [-inf]
  311.         Strategy for the linear term [Keep]
  312.     CPO manual calculation[No]
  313.     VB correction is [Enabled]
  314.         strategy                    = [mean]
  315.         verbose                     = [Yes]
  316.         f_enable_limit_mean         = [30]
  317.         f_enable_limit_var          = [25]
  318.         f_enable_limit_mean_max     = [1024]
  319.         f_enable_limit_variance_max = [768]
  320.         iter_max                    = [25]
  321.         emergency                   = [25.00]
  322.         hessian_update              = [2]
  323.         hessian_strategy            = [full]
  324.     Misc options:
  325.         Hessian correct skewness only [1]
  326. inla_build: check for unused entries in[/tmp/RtmpfHspX1/file5aea3a30ac8b/Model.ini]
  327. inla_INLA_preopt_experimental...
  328.     Strategy = [DEFAULT]
  329.     Mode....................... [Compact]
  330.     Setup...................... [0.01s]
  331.     Sparse-matrix library...... [taucs]
  332.     sort L..................... [no]
  333.     OpenMP strategy............ [small]
  334.     num.threads................ [16:1]
  335.     blas.num.threads........... [1]
  336.     Density-strategy........... [High]
  337.     Size of graph.............. [2]
  338.     Number of constraints...... [0]
  339.     Optimizing sort2_id........ [309]
  340.     Optimizing sort2_dd........ [381]
  341.     Optimizing Qx-strategy..... serial[0.354] parallel [0.646] choose[serial]
  342.     Optimizing pred-strategy... plain [0.664] data-rich[0.336] choose[data-rich]
  343.     Found optimal reordering=[metis] nnz(L)=[2] and use_global_nodes(user)=[no]
  344.  
  345.     List of hyperparameters:
  346.         theta[0] = [Log precision for the Gaussian observations]
  347.  
  348.  
  349. Compute initial values...
  350.     Iter[0] RMS(err) = 1.000, update with step-size = 1.094
  351.     Iter[1] RMS(err) = 0.084, update with step-size = 0.936
  352.     Iter[2] RMS(err) = 0.990, update with step-size = 1.043
  353.     Initial values computed in 0.0001 seconds
  354.         x[0] = 0.8545
  355.         x[1] = 0.8502
  356.         x[0] = 0.8545
  357.         x[1] = 0.8502
  358.  
  359. Optimise using DEFAULT METHOD
  360. Smart optimise part I: estimate gradient using forward differences
  361. Input Error: Incorrect nseps.
  362. Input Error: Incorrect nseps.
  363. inla.mkl: smtp-taucs.c:470: taucs_ccs_metis5: Assertion `0 == 1' failed.
  364. inla.mkl: smtp-taucs.c:470: taucs_ccs_metis5: Assertion `0 == 1' failed.
  365.  
  366.  *** inla.core.safe:  The inla program failed, but will rerun in case better initial values may help. try=1/1
  367.  
  368.  
  369. ***************************************************************************
  370. CONTAINS Runtime Modules of Parallel Sparse Linear Solver PARDISO Vers. 8.2
  371. Copyright Panua Technologies, Switzerland [2022-2023], All Rights Reserved.
  372. No license file found. Please see                                  
  373.             https://www.panua.ch/products/pardiso                        
  374. where to place the license file panua.lic
  375. ***************************************************************************
  376.     Read ntt 16 1 with max.threads 16
  377.     Found num.threads = 16:1 max_threads = 16
  378.  
  379.     39a87469400fa169507c508366eca6ae7588cfdf - Wed Jun 19 21:58:47 2024 +0900
  380. Report bugs to <help@r-inla.org>
  381. Set reordering to id=[0] and name=[default]
  382. Process file[/tmp/RtmpfHspX1/file5aea5c5a224d/Model.ini] threads[16] max.threads[16] blas_threads[1] nested[16:1]
  383. inla_build...
  384.     number of sections=[10]
  385.     parse section=[0] name=[INLA.libR] type=[LIBR]
  386.     inla_parse_libR...
  387.         section[INLA.libR]
  388.             R_HOME=[/usr/local/lib64/R]
  389.     parse section=[7] name=[INLA.Expert] type=[EXPERT]
  390.     inla_parse_expert...
  391.         section[INLA.Expert]
  392.             disable.gaussian.check=[0]
  393.             Measure dot.product.gain=[No]
  394.             cpo.manual=[0]
  395.             jp.file=[(null)]
  396.             jp.model=[(null)]
  397.     parse section=[1] name=[INLA.Model] type=[PROBLEM]
  398.     inla_parse_problem...
  399.         name=[INLA.Model]
  400.         R-INLA version = [24.06.19]
  401.         R-INLA build date = [19893]
  402.         Build tag = [devel]
  403.         System memory = [31.0Gb]
  404.         Cores = (Physical= 16, Logical= 16)
  405.         'char' is signed
  406.         BUFSIZ is 8192
  407.         openmp.strategy=[default]
  408.         pardiso-library installed and working? = [no]
  409.         smtp = [taucs]
  410.         strategy = [default]
  411.         store results in directory=[/tmp/RtmpfHspX1/file5aea5c5a224d/results.files]
  412.         output:
  413.             gcpo=[0]
  414.                 num.level.sets=[-1]
  415.                 size.max=[32]
  416.                 strategy=[Posterior]
  417.                 correct.hyperpar=[1]
  418.                 epsilon=[0.005]
  419.                 prior.diagonal=[0.0001]
  420.                 keep=[]
  421.                 remove.fixed=[1]
  422.                 remove=[]
  423.             cpo=[0]
  424.             po=[0]
  425.             dic=[0]
  426.             kld=[1]
  427.             mlik=[1]
  428.             q=[0]
  429.             graph=[0]
  430.             hyperparameters=[1]
  431.             config=[0]
  432.             config.lite=[0]
  433.             likelihood.info=[0]
  434.             internal.opt=[1]
  435.             save.memory=[0]
  436.             summary=[1]
  437.             return.marginals=[0]
  438.             return.marginals.predictor=[0]
  439.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  440.             ncdf=[0]  [ ]
  441.     parse section=[3] name=[Predictor] type=[PREDICTOR]
  442.     inla_parse_predictor ...
  443.         section=[Predictor]
  444.         dir=[predictor]
  445.         PRIOR->name=[loggamma]
  446.         hyperid=[53001|Predictor]
  447.         PRIOR->from_theta=[function (x) <<NEWLINE>>exp(x)]
  448.         PRIOR->to_theta = [function (x) <<NEWLINE>>log(x)]
  449.         PRIOR->PARAMETERS=[1, 1e-05]
  450.         initialise log_precision[13.8155]
  451.         fixed=[1]
  452.         user.scale=[1]
  453.         n=[100]
  454.         m=[0]
  455.         ndata=[100]
  456.         compute=[0]
  457.         read offsets from file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279]
  458.         read n=[200] entries from file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279]
  459.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 0/100  (idx,y) = (0, 0)
  460.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 1/100  (idx,y) = (1, 0)
  461.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 2/100  (idx,y) = (2, 0)
  462.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 3/100  (idx,y) = (3, 0)
  463.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 4/100  (idx,y) = (4, 0)
  464.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 5/100  (idx,y) = (5, 0)
  465.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 6/100  (idx,y) = (6, 0)
  466.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 7/100  (idx,y) = (7, 0)
  467.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 8/100  (idx,y) = (8, 0)
  468.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea73617279] 9/100  (idx,y) = (9, 0)
  469.         A=[(null)]
  470.         Aext=[(null)]
  471.         AextPrecision=[1e+08]
  472.         output:
  473.             summary=[1]
  474.             return.marginals=[0]
  475.             return.marginals.predictor=[0]
  476.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  477.             ncdf=[0]  [ ]
  478.     parse section=[2] name=[INLA.Data1] type=[DATA]
  479.     inla_parse_data [section 1]...
  480.         tag=[INLA.Data1]
  481.         family=[GAUSSIAN]
  482.         likelihood=[GAUSSIAN]
  483.         file->name=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea2396e0a2]
  484.         file->name=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea1ec213c7]
  485.         file->name=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea256fdc9b]
  486.         file->name=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea7ed2958]
  487.         read n=[300] entries from file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea2396e0a2]
  488.             0/100  (idx,a,y,d) = (0, 1, 1.02769, 1)
  489.             1/100  (idx,a,y,d) = (1, 1, 1.09805, 1)
  490.             2/100  (idx,a,y,d) = (2, 1, 2.65996, 1)
  491.             3/100  (idx,a,y,d) = (3, 1, 0.704929, 1)
  492.             4/100  (idx,a,y,d) = (4, 1, -0.262727, 1)
  493.             5/100  (idx,a,y,d) = (5, 1, -0.526566, 1)
  494.             6/100  (idx,a,y,d) = (6, 1, 0.717622, 1)
  495.             7/100  (idx,a,y,d) = (7, 1, 1.11993, 1)
  496.             8/100  (idx,a,y,d) = (8, 1, -0.525064, 1)
  497.             9/100  (idx,a,y,d) = (9, 1, 2.46091, 1)
  498.         likelihood.variant=[0]
  499.         initialise log_precision[4]
  500.         fixed0=[0]
  501.         PRIOR0->name=[loggamma]
  502.         hyperid=[65001|INLA.Data1]
  503.         PRIOR0->from_theta=[function (x) <<NEWLINE>>exp(x)]
  504.         PRIOR0->to_theta = [function (x) <<NEWLINE>>log(x)]
  505.         PRIOR0->PARAMETERS0=[1, 5e-05]
  506.         initialise log_precision offset[72.0873]
  507.         fixed1=[1]
  508.         PRIOR1->name=[none]
  509.         hyperid=[65002|INLA.Data1]
  510.         PRIOR1->from_theta=[function (x) <<NEWLINE>>exp(x)]
  511.         PRIOR1->to_theta = [function (x) <<NEWLINE>>log(x)]
  512.         PRIOR1->PARAMETERS1=[]
  513.         Link model   [IDENTITY]
  514.         Link order   [-1]
  515.         Link variant [-1]
  516.         Link a       [1]
  517.         Link ntheta  [0]
  518.         mix.use[0]
  519.     section=[4] name=[(Intercept)] type=[LINEAR]
  520.     inla_parse_linear...
  521.         section[(Intercept)]
  522.         dir=[fixed.effect00000001]
  523.         file for covariates=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064]
  524.         read n=[200] entries from file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064]
  525.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 0/100  (idx,y) = (0, 1)
  526.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 1/100  (idx,y) = (1, 1)
  527.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 2/100  (idx,y) = (2, 1)
  528.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 3/100  (idx,y) = (3, 1)
  529.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 4/100  (idx,y) = (4, 1)
  530.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 5/100  (idx,y) = (5, 1)
  531.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 6/100  (idx,y) = (6, 1)
  532.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 7/100  (idx,y) = (7, 1)
  533.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 8/100  (idx,y) = (8, 1)
  534.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea4cccb064] 9/100  (idx,y) = (9, 1)
  535.         prior mean=[0]
  536.         prior precision=[0]
  537.         compute=[1]
  538.         output:
  539.             summary=[1]
  540.             return.marginals=[0]
  541.             return.marginals.predictor=[0]
  542.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  543.             ncdf=[0]  [ ]
  544.     section=[5] name=[z] type=[LINEAR]
  545.     inla_parse_linear...
  546.         section[z]
  547.         dir=[fixed.effect00000002]
  548.         file for covariates=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea]
  549.         read n=[200] entries from file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea]
  550.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 0/100  (idx,y) = (0, -0.0818444)
  551.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 1/100  (idx,y) = (1, -0.0779361)
  552.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 2/100  (idx,y) = (2, 1.60972)
  553.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 3/100  (idx,y) = (3, -0.172882)
  554.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 4/100  (idx,y) = (4, -1.23245)
  555.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 5/100  (idx,y) = (5, -1.52436)
  556.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 6/100  (idx,y) = (6, -0.276537)
  557.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 7/100  (idx,y) = (7, 0.147262)
  558.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 8/100  (idx,y) = (8, -1.37884)
  559.         file=[/tmp/RtmpfHspX1/file5aea5c5a224d/data.files/file5aea6b218aea] 9/100  (idx,y) = (9, 1.51115)
  560.         prior mean=[0]
  561.         prior precision=[0.001]
  562.         compute=[1]
  563.         output:
  564.             summary=[1]
  565.             return.marginals=[0]
  566.             return.marginals.predictor=[0]
  567.             nquantiles=[3]  [ 0.025 0.5 0.975 ]
  568.             ncdf=[0]  [ ]
  569.     parse section=[9] name=[INLA.pardiso] type=[PARDISO]
  570.     inla_parse_pardiso...
  571.         section[INLA.pardiso]
  572.         verbose[0]
  573.         debug[0]
  574.         parallel.reordering[1]
  575.         nrhs[-1]
  576.     parse section=[8] name=[INLA.lp.scale] type=[LP.SCALE]
  577.     inla_parse_lp_scale...
  578.         section[INLA.lp.scale]
  579.  
  580.     Index table: number of entries[3], total length[102]
  581.         tag                            start-index     length
  582.         Predictor                               0        100
  583.         (Intercept)                           100          1
  584.         z                                     101          1
  585.     List of hyperparameters:
  586.         theta[0] = [Log precision for the Gaussian observations]
  587.  
  588.     parse section=[6] name=[INLA.Parameters] type=[INLA]
  589.     inla_parse_INLA...
  590.         section[INLA.Parameters]
  591.             lincomb.derived.correlation.matrix = [No]
  592.         global_node.factor = 2.000
  593.         global_node.degree = 2147483647
  594.         reordering = -1
  595.         constr.marginal.diagonal = 1.49e-08
  596. Contents of ai_param 0x5856c9e8d480
  597.     Optimiser: DEFAULT METHOD
  598.         Option for GSL-BFGS2: tol  = 0.1
  599.         Option for GSL-BFGS2: step_size = 1
  600.         Option for GSL-BFGS2: epsx = 0.002
  601.         Option for GSL-BFGS2: epsf = 0.004
  602.         Option for GSL-BFGS2: epsg = 0.01
  603.         Restart: 0
  604.         Optimise: try to be smart: No
  605.         Optimise: use directions: Yes
  606.         Mode restart: Yes
  607.         Mode fixed: No
  608.         Mode use_mode: No
  609.         parallel linesearch [0]
  610.     Gaussian approximation:
  611.         tolerance_func = 0.004
  612.         tolerance_step = 1e-05
  613.         optpar_fp = 0
  614.         optpar_nr_step_factor = -0.1
  615.     Gaussian data: Yes
  616.     Strategy:   Use the Gaussian approximation
  617.     Fast mode:  On
  618.     Use linear approximation to log(|Q +c|)? Yes
  619.         Method:  Compute the derivative exact
  620.     Parameters for improved approximations
  621.         Number of points evaluate:   9
  622.         Step length to compute derivatives numerically:  0.0001
  623.         Stencil to compute derivatives numerically:  5
  624.         Cutoff value to construct local neigborhood:     0.0001
  625.     Log calculations:    On
  626.     Log calculated marginal for the hyperparameters:     On
  627.     Integration strategy:    Use only the modal configuration (EMPIRICAL_BAYES)
  628.         f0 (CCD only):   1.100
  629.         dz (GRID only):  0.750
  630.         Adjust weights (GRID only):  On
  631.         Difference in log-density limit (GRID only):     6.000
  632.         Skip configurations with (presumed) small density (GRID only):   On
  633.     Gradient is computed using Central difference with step-length 0.005000
  634.     Hessian is computed using Central difference with step-length 0.070711
  635.     Hessian matrix is forced to be a diagonal matrix? [Yes]
  636.     Compute effective number of parameters? [Yes]
  637.     Perform a Monte Carlo error-test? [No]
  638.     Interpolator [Auto]
  639.     CPO required diff in log-density [3]
  640.     Stupid search mode:
  641.         Status     [On]
  642.         Max iter   [1000]
  643.         Factor     [1.05]
  644.     Numerical integration of hyperparameters:
  645.         Maximum number of function evaluations [100000]
  646.         Relative error ....................... [1e-05]
  647.         Absolute error ....................... [1e-06]
  648.     To stabilise the numerical optimisation:
  649.         Minimum value of the -Hessian [inf]
  650.         Strategy for the linear term [Keep]
  651.     CPO manual calculation[No]
  652.     VB-correction is [Disabled]
  653.     Misc options:
  654.         Hessian correct skewness only [1]
  655. inla_build: check for unused entries in[/tmp/RtmpfHspX1/file5aea5c5a224d/Model.ini]
  656. inla_INLA_preopt_experimental...
  657.     Strategy = [DEFAULT]
  658.     Mode....................... [Compact]
  659.     Setup...................... [0.00s]
  660.     Sparse-matrix library...... [taucs]
  661.     sort L..................... [no]
  662.     OpenMP strategy............ [small]
  663.     num.threads................ [16:1]
  664.     blas.num.threads........... [1]
  665.     Density-strategy........... [High]
  666.     Size of graph.............. [2]
  667.     Number of constraints...... [0]
  668.     Optimizing sort2_id........ [301]
  669.     Optimizing sort2_dd........ [381]
  670.     Optimizing Qx-strategy..... serial[0.284] parallel [0.716] choose[serial]
  671.     Optimizing pred-strategy... plain [0.664] data-rich[0.336] choose[data-rich]
  672.     Found optimal reordering=[metis] nnz(L)=[2] and use_global_nodes(user)=[no]
  673.  
  674.     List of hyperparameters:
  675.         theta[0] = [Log precision for the Gaussian observations]
  676.  
  677.  
  678. Compute initial values...
  679.     Iter[0] RMS(err) = 1.000, update with step-size = 1.094
  680.     Iter[1] RMS(err) = 0.084, update with step-size = 0.936
  681.     Iter[2] RMS(err) = 0.990, update with step-size = 1.043
  682.     Initial values computed in 0.0003 seconds
  683.         x[0] = 0.8545
  684.         x[1] = 0.8502
  685.         x[0] = 0.8545
  686.         x[1] = 0.8502
  687.  
  688. Optimise using DEFAULT METHOD
  689. Input Error: Incorrect nseps.
  690. Input Error: Incorrect nseps.
  691. inla.mkl: smtp-taucs.c:470: taucs_ccs_metis5: Assertion `0 == 1' failed.
  692. Error in inla.core.safe(formula = formula, family = family, contrasts = contrasts,  :
  693.  The inla-program exited with an error. Unless you interupted it yourself, please rerun with verbose=TRUE and check the output carefully.
  694.  If this does not help, please contact the developers at <[email protected]>.
  695. The inla program failed and the maximum number of tries has been reached.
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