* using log directory ‘/srv/hornik/tmp/CRAN_pretest/CopulaREMADA.Rcheck’ * using R Under development (unstable) (2024-10-16 r87241) * using platform: x86_64-pc-linux-gnu * R was compiled by Debian clang version 18.1.8 (9) Debian flang-new version 18.1.8 (9) * running under: Debian GNU/Linux trixie/sid * using session charset: UTF-8 * checking for file ‘CopulaREMADA/DESCRIPTION’ ... OK * this is package ‘CopulaREMADA’ version ‘1.7.1’ * checking CRAN incoming feasibility ... [4s/5s] NOTE Maintainer: ‘Aristidis K. Nikoloulopoulos ’ No Authors@R field in DESCRIPTION. Please add one, modifying Authors@R: person(given = c("Aristidis", "K."), family = "Nikoloulopoulos", role = c("aut", "cre"), email = "A.Nikoloulopoulos@uea.ac.uk") as necessary. * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘CopulaREMADA’ can be installed ... [8s/8s] OK * checking package directory ... OK * checking for future file timestamps ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... [1s/1s] OK * checking whether the package can be loaded with stated dependencies ... [1s/1s] OK * checking whether the package can be unloaded cleanly ... [1s/1s] OK * checking whether the namespace can be loaded with stated dependencies ... [1s/1s] OK * checking whether the namespace can be unloaded cleanly ... [1s/1s] OK * checking loading without being on the library search path ... [1s/1s] OK * checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [11s/11s] OK * checking Rd files ... [0s/0s] OK * checking Rd metadata ... OK * checking Rd line widths ... OK * checking Rd cross-references ... NOTE Found the following Rd file(s) with Rd \link{} targets missing package anchors: CopulaREMADA.Rd: meshgrid FactorCopulaREMADA.Rd: meshgrid VineCopulaREMADA.Rd: meshgrid Vuong.Rd: meshgrid hybridCopulaREMADA.Rd: meshgrid imperfect.CopulaREMADA.Rd: meshgrid vineVuong.Rd: meshgrid Please provide package anchors for all Rd \link{} targets not in the package itself and the base packages. * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... [3s/3s] OK * checking data for ASCII and uncompressed saves ... OK * checking examples ... [7s/7s] ERROR Running examples in ‘CopulaREMADA-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: imperfectCopulaREMADA > ### Title: Maximum likelihood estimation of bivariate copula mixed models > ### for meta-analysis of diagnostic accuracy studies without a gold > ### standard > ### Aliases: imperfectCopulaREMADA imperfectCopulaREMADA.norm > ### imperfectCopulaREMADA.beta > ### Keywords: maximum likelihood copula distribution > > ### ** Examples > > data(Pap) > attach(Pap) > > nq=30 > gl=gauss.quad.prob(nq,"uniform") > mgrid<- meshgrid(gl$n,gl$n) > > tau2par=tau2par.bvn > qcond=qcondbvn > > select.random=c(1,2) > > start=c(rep(0.6,5),rep(0.5,2),-0.1) > est.norm=imperfectCopulaREMADA.norm(y11,y10,y01,y00,gl,mgrid, + qcond,tau2par,select.random,start) iteration = 0 Step: [1] 0 0 0 0 0 0 0 0 Parameter: [1] 0.4054651 0.4054651 0.4054651 0.4054651 0.4054651 -0.6931472 -0.6931472 [8] -0.1000000 Function Value [1] 10810.67 Gradient: [1] 11.55052 88.35064 -230.10236 -396.73811 -456.78500 -216.91625 -364.95845 [8] -132.56157 iteration = 1 Step: [1] -0.01155052 -0.08835064 0.23010236 0.39673811 0.45678500 0.21691625 [7] 0.36495845 0.13256157 Parameter: [1] 0.39391459 0.31711447 0.63556747 0.80220322 0.86225010 -0.47623093 [7] -0.32818873 0.03256157 Function Value [1] 10176.34 Gradient: [1] 19.37118 -24.63076 -428.71520 -416.73574 -389.94194 -250.63646 -169.09004 [8] -68.23593 iteration = 2 Step: [1] -0.03130966 -0.04887894 0.67095853 0.80117670 0.82330714 0.46317755 [7] 0.50065496 0.18937086 Parameter: [1] 0.36260493 0.26823553 1.30652600 1.60337992 1.68555724 -0.01305338 [7] 0.17246622 0.22193243 Function Value [1] 9416.483 Gradient: [1] 7.28821 -23.61922 -142.87718 -107.73177 -143.23916 -109.70741 -61.39980 [8] 88.09886 iteration = 3 Step: [1] -0.003169751 0.007756379 0.037375588 0.019268471 0.098271123 [6] 0.076606618 0.071127074 -0.146979292 Parameter: [1] 0.35943518 0.27599191 1.34390159 1.62264839 1.78382836 0.06355324 0.24359330 [8] 0.07495314 Function Value [1] 9377.453 Gradient: [1] -0.9359319 -21.1039569 -129.3892508 -99.1186375 -116.9125398 [6] -87.5177702 -47.1381027 22.0352485 iteration = 4 Step: [1] 0.20354359 0.14706825 0.41276453 0.13771304 0.55324514 0.44848386 0.36863950 [8] 0.05346286 Parameter: [1] 0.5629788 0.4230602 1.7566661 1.7603614 2.3370735 0.5120371 0.6122328 [8] 0.1284160 Function Value [1] 9280.599 Gradient: [1] 2.5855097 -8.7096996 18.4750966 -7.3096787 -37.6478896 -12.1995054 [7] -0.4456324 21.6390799 iteration = 5 Step: [1] 0.02790964 0.17028216 -0.13482921 0.05423297 0.35610430 0.12128276 [7] -0.11095732 -0.02714202 Parameter: [1] 0.5908884 0.5933423 1.6218369 1.8145944 2.6931778 0.6333199 0.5012755 [8] 0.1012740 Function Value [1] 9267.734 Gradient: [1] -2.296136e+00 -4.844784e+00 -6.260811e+00 -1.724617e+01 -1.570543e+01 [6] -1.788649e-01 1.818989e-04 1.451955e+01 iteration = 6 Step: [1] 0.09426724 0.13565282 -0.07697082 0.19247075 0.30380279 -0.02501774 [7] -0.06148525 -0.08360155 Parameter: [1] 0.68515566 0.72899514 1.54486609 2.00706515 2.99698059 0.60830212 0.43979024 [8] 0.01767242 Function Value [1] 9262.309 Gradient: [1] -3.6726196 -1.9840354 1.0394606 8.1987973 -7.0939268 0.3949062 0.5229322 [8] 5.7499747 iteration = 7 Step: [1] 0.091561122 0.077522287 -0.009547378 -0.011870103 0.205973003 [6] -0.012162080 -0.028835156 -0.036436090 Parameter: [1] 0.77671678 0.80651743 1.53531871 1.99519505 3.20295360 0.59614004 [7] 0.41095508 -0.01876367 Function Value [1] 9260.71 Gradient: [1] -1.5551941 0.3000587 -3.5069660 3.3541940 -3.5828704 -1.3774224 0.1269582 [8] 1.2606615 iteration = 8 Step: [1] 0.069968544 0.030885416 0.023224209 -0.032934843 0.157417871 [6] 0.007763114 -0.012032562 -0.017799337 Parameter: [1] 0.8466853 0.8374028 1.5585429 1.9622602 3.3603715 0.6039032 0.3989225 [8] -0.0365630 Function Value [1] 9260.164 Gradient: [1] -0.26536145 1.63002005 -0.50827362 0.29567080 -1.63591133 -1.49619882 [7] 0.02449815 -1.42668614 iteration = 9 Step: [1] 0.027812810 -0.002208215 0.007813951 -0.011930195 0.071327094 [6] 0.019413667 -0.015106787 -0.001053992 Parameter: [1] 0.8744981 0.8351946 1.5663569 1.9503300 3.4316986 0.6233168 0.3838157 [8] -0.0376170 Function Value [1] 9260.063 Gradient: [1] -0.3626137 1.9591935 0.4433514 -0.7994458 -1.0021752 0.9987416 -0.7765266 [8] -1.8314895 iteration = 10 Step: [1] 0.0183306639 -0.0138051238 0.0073278896 -0.0026150033 0.0464592792 [6] -0.0018207404 -0.0146681361 0.0007865891 Parameter: [1] 0.89282880 0.82138950 1.57368476 1.94771500 3.47815784 0.62149608 [7] 0.36914759 -0.03683041 Function Value [1] 9260.015 Gradient: [1] -0.4385165 1.9084255 2.4456634 -0.2482415 -0.7756754 0.9142659 -1.6449285 [8] -2.2191180 iteration = 11 Step: [1] 0.0209225795 -0.0225999708 0.0029709656 -0.0001173374 0.0458587995 [6] -0.0022265653 -0.0203169002 0.0065233293 Parameter: [1] 0.91375138 0.79878953 1.57665573 1.94759767 3.52401664 0.61926952 [7] 0.34883069 -0.03030708 Function Value [1] 9259.97 Gradient: [1] -0.5613383 1.6825325 3.4936849 0.1667820 -0.6896126 0.8035822 -2.9567782 [8] -2.0056486 iteration = 12 Step: [1] 2.063778e-02 -2.119821e-02 8.595864e-05 1.553364e-03 3.170843e-02 [6] -2.880671e-03 -1.352937e-02 9.603086e-03 Parameter: [1] 0.93438916 0.77759132 1.57674169 1.94915103 3.55572507 0.61638885 [7] 0.33530132 -0.02070399 Function Value [1] 9259.933 Gradient: [1] -0.4686735 1.3788685 3.9117886 0.5177811 -0.7193933 0.4363901 -3.8067355 [8] -1.2813616 iteration = 13 Step: [1] 0.024961801 -0.020145277 -0.003329574 0.003309095 0.026169449 [6] -0.001662805 -0.006219416 0.012859902 Parameter: [1] 0.959350959 0.757446047 1.573412113 1.952460125 3.581894514 [6] 0.614726040 0.329081906 -0.007844091 Function Value [1] 9259.887 Gradient: [1] 0.01710578 1.02443119 3.60955518 0.70089399 -0.79374108 -0.09259202 [7] -4.06386971 -0.08314419 iteration = 14 Step: [1] 0.017683440 -0.011529317 -0.007423554 0.005147908 0.008435048 [6] 0.001701143 0.008224381 0.011767777 Parameter: [1] 0.977034399 0.745916730 1.565988558 1.957608033 3.590329562 0.616427183 [7] 0.337306287 0.003923687 Function Value [1] 9259.832 Gradient: [1] 0.4945978 0.7544113 2.2766387 0.5350516 -0.8438384 -0.4626290 -3.2002954 [8] 1.1772936 iteration = 15 Step: [1] 0.009102046 -0.002085427 -0.008303427 0.005569559 0.003686811 [6] 0.005670163 0.018560813 0.004498443 Parameter: [1] 0.98613645 0.74383130 1.55768513 1.96317759 3.59401637 0.62209735 0.35586710 [8] 0.00842213 Function Value [1] 9259.782 Gradient: [1] 0.5810834 0.6673672 0.6814753 0.2154525 -0.7479793 -0.4764988 -1.5452388 [8] 1.7548482 iteration = 16 Step: [1] 0.010978059 -0.003106183 -0.004235143 0.003233980 0.024122385 [6] 0.006844360 0.011738856 -0.003082454 Parameter: [1] 0.997114505 0.740725120 1.553449988 1.966411572 3.618138758 0.628941705 [7] 0.367605956 0.005339675 Function Value [1] 9259.751 Gradient: [1] 0.62250729 0.63258994 0.05628803 0.07010616 -0.52187610 -0.28170325 [7] -0.40813575 1.45682861 iteration = 17 Step: [1] 0.007380723 -0.008051350 -0.001999936 0.001590801 0.033802024 [6] 0.006610416 0.005427339 -0.007498114 Parameter: [1] 1.004495227 0.732673770 1.551450052 1.968002373 3.651940783 [6] 0.635552122 0.373033295 -0.002158439 Function Value [1] 9259.729 Gradient: [1] 0.40270570 0.50772906 -0.15325025 -0.01902173 -0.25610511 0.27478382 [7] 0.26143425 0.59669946 iteration = 18 Step: [1] 4.705337e-03 -1.218275e-02 3.911110e-04 -5.612604e-05 3.000913e-02 [6] 2.375756e-06 -2.765219e-03 -4.882697e-03 Parameter: [1] 1.009200564 0.720491024 1.551841163 1.967946247 3.681949913 [6] 0.635554497 0.370268076 -0.007041136 Function Value [1] 9259.718 Gradient: [1] 0.51925341 0.29841976 0.07043445 0.01770420 -0.13702631 0.15828482 [7] 0.23001121 -0.08310781 iteration = 19 Step: [1] -0.0079505558 -0.0117411587 0.0005631170 -0.0009818367 0.0146460797 [6] -0.0017251674 -0.0039020162 -0.0026760727 Parameter: [1] 1.001250009 0.708749865 1.552404280 1.966964410 3.696595993 [6] 0.633829330 0.366366059 -0.009717209 Function Value [1] 9259.712 Gradient: [1] 0.111330326 0.062666004 -0.008614515 -0.054474493 -0.072366490 [6] 0.574260412 0.006073606 -0.487203579 iteration = 20 Step: [1] -0.0044701410 -0.0090217651 0.0011323014 -0.0008095721 0.0110104702 [6] -0.0060194568 -0.0033392265 0.0013173076 Parameter: [1] 0.996779868 0.699728100 1.553536581 1.966154838 3.707606463 [6] 0.627809873 0.363026833 -0.008399901 Function Value [1] 9259.708 Gradient: [1] 0.22472886 -0.11501106 0.13370633 0.01230728 -0.08966048 0.16223385 [7] -0.20561674 -0.49882146 iteration = 21 Step: [1] -0.0083396340 -0.0030173173 0.0006011157 -0.0007520608 0.0026832748 [6] -0.0052942325 -0.0001477834 0.0027697402 Parameter: [1] 0.988440234 0.696710783 1.554137697 1.965402777 3.710289738 [6] 0.622515641 0.362879050 -0.005630161 Function Value [1] 9259.705 Gradient: [1] 0.105092113 -0.175848982 0.034853852 0.000698756 -0.098650163 [6] 0.068603185 -0.201356670 -0.301564796 iteration = 22 Step: [1] -0.0066919264 0.0007804786 0.0005404427 -0.0006744683 0.0060469344 [6] -0.0050115130 0.0011672966 0.0032011613 Parameter: [1] 0.9817483 0.6974913 1.5546781 1.9647283 3.7163367 0.6175041 0.3640463 [8] -0.0024290 Function Value [1] 9259.703 Gradient: [1] 0.02754859 -0.13646604 -0.06422654 -0.01958114 -0.08039831 -0.04158937 [7] -0.07714516 -0.05266702 iteration = 23 Step: [1] -0.0014826070 0.0020380283 0.0002651946 -0.0002576086 0.0075821621 [6] -0.0015969477 0.0005992105 0.0011481925 Parameter: [1] 0.980265700 0.699529290 1.554943334 1.964470700 3.723918834 [6] 0.615907180 0.364645557 -0.001280807 Function Value [1] 9259.703 Gradient: [1] 0.008521965 -0.066263965 -0.074642114 -0.030066320 -0.044162254 [6] -0.064970664 0.006653863 0.020056177 iteration = 24 Step: [1] 5.388860e-04 1.617782e-03 1.362478e-04 -1.562344e-05 7.058692e-03 [6] 3.739463e-06 -1.100975e-04 2.100880e-04 Parameter: [1] 0.980804586 0.701147071 1.555079582 1.964455077 3.730977526 [6] 0.615910920 0.364535459 -0.001070719 Function Value [1] 9259.702 Gradient: [1] 0.008212737 -0.008687493 -0.036562740 -0.024142324 -0.009928202 [6] -0.044383341 0.030320734 0.012696546 iteration = 25 Step: [1] 5.561393e-04 2.235167e-04 -2.383956e-06 1.017380e-04 2.032700e-03 [6] 4.561267e-04 -3.290247e-04 -1.018848e-04 Parameter: [1] 0.981360725 0.701370588 1.555077198 1.964556815 3.733010226 [6] 0.616367046 0.364206435 -0.001172604 Function Value [1] 9259.702 Gradient: [1] 0.0062955223 0.0022137101 -0.0099214805 -0.0118691529 -0.0006319911 [6] -0.0184081728 0.0132822606 -0.0029049261 iteration = 26 Parameter: [1] 0.981515385 0.701293595 1.555054260 1.964631937 3.733317747 [6] 0.616596071 0.364045980 -0.001187124 Function Value [1] 9259.702 Gradient: [1] 0.0024538167 0.0013988029 -0.0005649783 -0.0034553385 0.0004599464 [6] -0.0043219188 0.0019426807 -0.0041181920 Relative gradient close to zero. Current iterate is probably solution. > > start=c(rep(0.6,5),rep(0.1,2),-0.1) > est.beta=imperfectCopulaREMADA.beta(y11,y10,y01,y00,gl,mgrid, + qcond,tau2par,select.random,start) iteration = 0 Step: [1] 0 0 0 0 0 0 0 0 Parameter: [1] 0.4054651 0.4054651 0.4054651 0.4054651 0.4054651 -2.1972246 -2.1972246 [8] -0.1000000 Function Value [1] 10618.26 Gradient: [1] 2.496872 54.396040 -208.919757 -330.829143 -458.613187 -93.404705 [7] -146.855742 -60.874247 iteration = 1 Step: [1] -0.002496872 -0.054396040 0.208919757 0.330829143 0.458613187 [6] 0.093404705 0.146855742 0.060874247 Parameter: [1] 0.40296824 0.35106907 0.61438487 0.73629425 0.86407830 -2.10381987 [7] -2.05036884 -0.03912575 Function Value [1] 10152.6 Gradient: [1] 15.50682 -20.04966 -451.37266 -448.05140 -389.90740 -113.09194 -81.81022 [8] -96.31432 iteration = 2 Step: [1] -0.001550682 0.002004966 0.045137266 0.044805140 0.038990740 [6] 0.011309194 0.008181022 0.009631432 Parameter: [1] 0.40141755 0.35307403 0.65952213 0.78109939 0.90306904 -2.09251068 [7] -2.04218781 -0.02949432 Function Value [1] 10094.83 Gradient: [1] 17.20764 -24.58563 -438.72480 -435.98455 -379.01948 -112.87289 -78.21827 [8] -81.27220 iteration = 3 Step: [1] -0.080199063 0.184185001 0.193574002 0.208956222 0.168510134 [6] 0.150105117 -0.009676443 -0.383844682 Parameter: [1] 0.3212185 0.5372590 0.8530961 0.9900556 1.0715792 -1.9424056 -2.0518643 [8] -0.4133390 Function Value [1] 9946.026 Gradient: [1] -20.56547 -102.41974 -403.29371 -377.41794 -306.32832 -166.04569 -86.71551 [8] -480.26590 iteration = 4 Step: [1] -0.06779338 0.39036609 0.11204582 0.10482896 0.04005151 0.27356256 [7] -0.02884064 -0.07310587 Parameter: [1] 0.2534251 0.9276251 0.9651419 1.0948846 1.1116307 -1.6688430 -2.0807049 [8] -0.4864449 Function Value [1] 9802.008 Gradient: [1] -12.26388 -97.89592 -347.02963 -351.13464 -250.65273 -162.27993 -60.14237 [8] -325.24102 iteration = 5 Step: [1] -0.03644092 0.23850210 0.06779923 0.08219262 0.00917216 0.18002206 [7] -0.02480708 -0.04695742 Parameter: [1] 0.2169842 1.1661272 1.0329412 1.1770772 1.1208028 -1.4888209 -2.1055120 [8] -0.5334023 Function Value [1] 9717.665 Gradient: [1] -22.00120 -85.69987 -300.46179 -307.02936 -242.20431 -132.58325 -59.42000 [8] -271.83386 iteration = 6 Step: [1] 0.48345715 0.42961328 -0.24582806 0.19410016 0.28827486 0.18589623 [7] 0.04841635 -0.09831337 Parameter: [1] 0.7004413 1.5957405 0.7871131 1.3711774 1.4090777 -1.3029247 -2.0570956 [8] -0.6317157 Function Value [1] 9675.725 Gradient: [1] 41.01031 -14.14175 -422.79161 -293.13737 -85.63093 -136.44384 -54.29157 [8] -146.02790 iteration = 7 Step: [1] 0.42362717 0.20981700 0.17281066 -0.04859217 -0.05481834 0.05678784 [7] 0.18496871 -0.09658649 Parameter: [1] 1.1240685 1.8055575 0.9599238 1.3225852 1.3542593 -1.2461369 -1.8721269 [8] -0.7283022 Function Value [1] 9662.607 Gradient: [1] 120.57157 24.54034 -300.11863 -316.46689 -109.20415 -197.34415 -54.57551 [8] -161.76755 iteration = 8 Step: [1] 0.246521315 -0.002949208 0.008692833 -0.075065093 -0.011267600 [6] 0.398462201 0.555452368 -0.104259195 Parameter: [1] 1.3705898 1.8026083 0.9686166 1.2475201 1.3429917 -0.8476747 -1.3166745 [8] -0.8325614 Function Value [1] 9648.714 Gradient: [1] 93.38481 40.55080 -271.11563 -325.89916 -81.54112 -103.57390 -27.36932 [8] -234.15943 iteration = 9 Step: [1] -0.008361066 0.090606617 -0.050811092 0.124025281 -0.087065573 [6] -0.059883446 0.274501317 -0.019097687 Parameter: [1] 1.3622288 1.8932149 0.9178055 1.3715454 1.2559262 -0.9075581 -1.0421732 [8] -0.8516590 Function Value [1] 9641.026 Gradient: [1] 91.41675 25.74663 -274.94358 -271.12991 -133.83730 -115.68886 -19.53137 [8] -163.98454 iteration = 10 Step: [1] -0.56284955 0.33812391 0.21383834 0.37121713 0.14847963 -0.06539288 [7] -0.90028682 -0.01335753 Parameter: [1] 0.7993792 2.2313388 1.1316438 1.7427625 1.4044058 -0.9729510 -1.9424600 [8] -0.8650166 Function Value [1] 9529.859 Gradient: [1] 64.19082 62.61006 -154.05045 -135.62810 -80.06877 -56.46920 -101.17860 [8] -421.95343 iteration = 11 Step: [1] -0.97637486 0.19114744 0.08441787 0.13204991 -0.06552754 -0.24523346 [7] 0.51796669 -0.03228282 Parameter: [1] -0.1769957 2.4224863 1.2160617 1.8748124 1.3388783 -1.2181845 -1.4244933 [8] -0.8972994 Function Value [1] 9523.964 Gradient: [1] -138.24934 -77.14774 -132.66014 -62.28728 -133.42334 -114.74871 -43.70556 [8] -563.00972 iteration = 12 Step: [1] 0.389825596 -0.639573179 0.021102215 0.008257428 -0.025614700 [6] 0.450934059 -0.188620371 0.033500962 Parameter: [1] 0.2128299 1.7829131 1.2371639 1.8830699 1.3132636 -0.7672504 -1.6131137 [8] -0.8637984 Function Value [1] 9486.196 Gradient: [1] -85.46223 -80.85686 -95.32202 -57.40501 -164.81969 -64.33503 -58.13786 [8] -436.65925 iteration = 13 Step: [1] 0.42502225 0.24690983 -0.15123374 0.19862023 -0.03314176 0.24271868 [7] 0.55234062 -0.07605485 Parameter: [1] 0.6378522 2.0298229 1.0859302 2.0816901 1.2801218 -0.5245317 -1.0607731 [8] -0.9398533 Function Value [1] 9475.503 Gradient: [1] 35.735509 22.876684 -120.179180 -53.896232 -149.500070 -5.979256 [7] -25.384159 -474.730236 iteration = 14 Step: [1] -0.187633784 -0.117145027 0.009149495 -0.063472465 0.100575608 [6] 0.076003835 -0.610375599 0.119160772 Parameter: [1] 0.4502184 1.9126779 1.0950797 2.0182176 1.3806974 -0.4485279 -1.6711487 [8] -0.8206925 Function Value [1] 9445.008 Gradient: [1] -7.578477 -7.647149 -146.587840 -61.834834 -119.912173 -24.356979 [7] -62.034629 -265.757806 iteration = 15 Step: [1] -0.02164407 -0.18112034 0.04455973 0.07281310 0.01947654 0.25812097 [7] 0.01163402 0.01290196 Parameter: [1] 0.4285743 1.7315576 1.1396394 2.0910307 1.4001740 -0.1904069 -1.6595147 [8] -0.8077906 Function Value [1] 9427.093 Gradient: [1] -25.02690 -23.27658 -114.51836 -41.43020 -121.08139 -15.28809 -60.20625 [8] -253.91499 iteration = 16 Step: [1] -0.005165266 -0.206369594 0.049743606 0.149343658 0.093363457 [6] 0.562922641 0.162581891 0.033064799 Parameter: [1] 0.4234091 1.5251880 1.1893830 2.2403744 1.4935374 0.3725157 -1.4969328 [8] -0.7747258 Function Value [1] 9394.908 Gradient: [1] -59.62791 -25.16707 -71.94603 -12.12984 -88.46163 15.91998 -49.26050 [8] -180.89275 iteration = 17 Step: [1] 0.2314758076 -0.2143148049 0.0159998775 0.0819323834 0.0978044536 [6] 0.3593612076 0.3037172905 -0.0004290303 Parameter: [1] 0.6548849 1.3108732 1.2053829 2.3223068 1.5913419 0.7318769 -1.1932155 [8] -0.7751548 Function Value [1] 9383.116 Gradient: [1] -50.231707 -5.039316 -53.648033 -5.359127 -52.188519 35.630759 [7] -32.033993 -152.172212 iteration = 18 Step: [1] 0.46468596 -0.40148953 0.03176292 0.02302580 0.12927372 0.27324602 [7] 0.39866490 -0.00543627 Parameter: [1] 1.1195708 0.9093836 1.2371458 2.3453326 1.7206156 1.0051230 -0.7945506 [8] -0.7805911 Function Value [1] 9382.406 Gradient: [1] 216.9629142 24.6885666 -26.1567060 -14.9315256 -18.2027970 [6] -0.3298411 -7.1470586 -172.5051316 iteration = 19 Step: [1] -0.37832328 0.33565344 0.03630352 -0.07559986 -0.05709602 -0.25317273 [7] -0.10895114 0.02185555 Parameter: [1] 0.7412476 1.2450371 1.2734493 2.2697327 1.6635196 0.7519502 -0.9035017 [8] -0.7587355 Function Value [1] 9364.478 Gradient: [1] 10.481825 14.606740 -24.664716 -8.542514 -23.599969 19.661724 [7] -15.266081 -128.305997 iteration = 20 Step: [1] -0.01208815 -0.08521782 0.03356540 -0.03627340 0.03592425 0.03278788 [7] 0.09851882 0.02824576 Parameter: [1] 0.7291594 1.1598192 1.3070147 2.2334593 1.6994438 0.7847381 -0.8049829 [8] -0.7304897 Function Value [1] 9357.264 Gradient: [1] 8.884586 19.979625 -14.950867 -6.237128 -27.455715 21.800817 [7] -14.024123 -155.116029 iteration = 21 Step: [1] 0.004586629 -0.141227918 0.017970350 -0.041309515 0.050588523 [6] 0.064428685 0.060667909 0.040229786 Parameter: [1] 0.7337460 1.0185913 1.3249851 2.1921498 1.7500323 0.8491668 -0.7443150 [8] -0.6902600 Function Value [1] 9345.628 Gradient: [1] 11.941112 22.251534 -22.148292 -4.797927 -32.617208 34.170500 [7] -20.684149 -185.450610 iteration = 22 Step: [1] -0.02575482 -0.09484098 0.02923715 -0.07546775 0.04330300 -0.02171347 [7] 0.10590966 0.04005953 Parameter: [1] 0.7079912 0.9237503 1.3542222 2.1166820 1.7933353 0.8274533 -0.6384053 [8] -0.6502004 Function Value [1] 9332.134 Gradient: [1] -2.016808 16.076130 -28.611024 -4.651104 -40.191599 26.566615 [7] -21.021378 -149.158703 iteration = 23 Step: [1] -0.01637078 -0.53310974 0.17014486 -0.28902825 0.26214164 0.10354400 [7] 0.61871945 0.16134294 Parameter: [1] 0.6916204 0.3906406 1.5243671 1.8276538 2.0554770 0.9309973 -0.0196859 [8] -0.4888575 Function Value [1] 9308.875 Gradient: [1] 13.933453 -4.654370 -10.852063 -23.283709 -48.340665 51.530564 3.813064 [8] -2.935996 iteration = 24 Step: [1] -0.12617554 0.07468763 0.07755786 -0.13915143 0.16033014 -0.42019960 [7] 0.14681619 0.04141299 Parameter: [1] 0.5654449 0.4653282 1.6019250 1.6885024 2.2158071 0.5107977 0.1271303 [8] -0.4474445 Function Value [1] 9297.828 Gradient: [1] 11.69027018 -0.08662028 -17.66820385 -52.12800523 -27.67644895 [6] 12.91916851 10.70477992 1.97013287 iteration = 25 Step: [1] -0.049574394 0.106588755 -0.001783853 0.134954544 0.087790082 [6] -0.169104396 -0.177360063 -0.005672204 Parameter: [1] 0.51587051 0.57191699 1.60014111 1.82345690 2.30359720 0.34169333 [7] -0.05022977 -0.45311670 Function Value [1] 9285.445 Gradient: [1] 17.8058399 1.3676654 0.7286525 -16.9582809 -23.4214753 18.6712332 [7] 7.6693286 -9.3005419 iteration = 26 Step: [1] -0.34781798 0.43728813 0.05398943 0.35306565 0.54823701 -1.08040441 [7] -0.67222582 0.04703436 Parameter: [1] 0.1680525 1.0092051 1.6541305 2.1765225 2.8518342 -0.7387111 -0.7224556 [8] -0.4060823 Function Value [1] 9284.016 Gradient: [1] -13.2839850 4.3733081 61.7110811 49.9709138 -2.5753221 -14.3274901 [7] 0.1971493 -51.1082835 iteration = 27 Step: [1] 0.14860216 -0.24528287 -0.05032055 -0.16470741 -0.15271741 0.43718115 [7] 0.20819513 0.01012906 Parameter: [1] 0.3166547 0.7639222 1.6038100 2.0118151 2.6991168 -0.3015299 -0.5142605 [8] -0.3959533 Function Value [1] 9271.037 Gradient: [1] 0.9118885 2.4747042 25.8931143 17.8300063 -6.9540696 -2.3771099 [7] 1.5819205 -35.4935219 iteration = 28 Step: [1] 0.028104318 -0.119407039 -0.022379675 -0.057541485 0.042063752 [6] 0.088622638 0.005912345 0.035786328 Parameter: [1] 0.3447590 0.6445152 1.5814303 1.9542737 2.7411806 -0.2129073 -0.5083481 [8] -0.3601670 Function Value [1] 9268.563 Gradient: [1] 2.8181839 -1.1120846 11.0093655 4.3633577 -6.8620137 -0.4696267 [7] 3.3096239 -22.8764347 iteration = 29 Step: [1] -0.009672135 -0.072609137 -0.031406476 -0.020119786 0.131413513 [6] -0.025693913 -0.139672298 0.056483840 Parameter: [1] 0.3350869 0.5719061 1.5500238 1.9341539 2.8725941 -0.2386012 -0.6480204 [8] -0.3036831 Function Value [1] 9266.229 Gradient: [1] -1.4679536 -3.5744597 -2.3334811 -4.2886910 -5.2203989 0.7976796 [7] 1.7810853 -19.8237904 iteration = 30 Step: [1] -0.042480433 -0.057972497 -0.030156602 0.003515064 0.255572578 [6] -0.132736302 -0.257766808 0.098137148 Parameter: [1] 0.2926064 0.5139336 1.5198672 1.9376689 3.1281666 -0.3713375 -0.9057872 [8] -0.2055460 Function Value [1] 9264.081 Gradient: [1] -11.176831 -5.374599 -13.975401 -8.256241 -2.795762 1.018365 -1.599125 [8] -16.846927 iteration = 31 Step: [1] -0.020210398 -0.012955357 0.006778337 0.008292169 0.139696947 [6] -0.083980732 -0.095539941 0.054131459 Parameter: [1] 0.2723960 0.5009782 1.5266456 1.9459611 3.2678636 -0.4553182 -1.0013272 [8] -0.1514145 Function Value [1] 9263.198 Gradient: [1] -12.6197665 -5.0383260 -11.7219834 -5.5909757 -1.8582103 0.9744635 [7] -2.6913810 -13.7472089 iteration = 32 Step: [1] -0.018138363 -0.008869663 0.028548539 0.007734987 0.200289839 [6] -0.117914357 -0.072679114 0.081767842 Parameter: [1] 0.25425768 0.49210855 1.55519411 1.95369609 3.46815343 -0.57323260 [7] -1.07400627 -0.06964666 Function Value [1] 9262.303 Gradient: [1] -14.0899374 -3.2525313 -3.2969191 0.2079510 -0.6406897 -0.3956175 [7] -2.9272145 -7.0213991 iteration = 33 Step: [1] 0.019372820 -0.007770718 0.016874485 -0.007159468 0.039556673 [6] 0.002741021 0.039395364 0.023452997 Parameter: [1] 0.27363050 0.48433784 1.57206860 1.94653662 3.50771011 -0.57049158 [7] -1.03461091 -0.04619366 Function Value [1] 9261.802 Gradient: [1] -13.2139339 -2.4070723 0.9439379 1.4159143 -0.2967842 0.1935423 [7] -1.4065478 -4.0278264 iteration = 34 Step: [1] 0.007369754 -0.001658691 0.001883678 -0.001734124 0.006294940 [6] 0.003227700 0.006845532 0.004621423 Parameter: [1] 0.28100025 0.48267915 1.57395228 1.94480250 3.51400505 -0.56726388 [7] -1.02776538 -0.04157224 Function Value [1] 9261.683 Gradient: [1] -12.8025731 -2.2581007 1.3623500 1.3361754 -0.2555671 0.3317436 [7] -1.1458055 -3.4441873 iteration = 35 Step: [1] 0.012256070 -0.001011609 0.001038934 -0.001533294 0.007087438 [6] 0.004672268 0.006651397 0.005732171 Parameter: [1] 0.29325632 0.48166754 1.57499121 1.94326920 3.52109248 -0.56259161 [7] -1.02111398 -0.03584007 Function Value [1] 9261.508 Gradient: [1] -11.9549823 -2.0434145 1.6545551 1.1077711 -0.2291866 0.4571484 [7] -0.8717456 -2.7111892 iteration = 36 Step: [1] 0.0129360961 0.0002991850 -0.0001061621 -0.0005215015 0.0052852783 [6] 0.0043376177 0.0046006612 0.0047272229 Parameter: [1] 0.30619242 0.48196672 1.57488505 1.94274770 3.52637776 -0.55825399 [7] -1.01651332 -0.03111285 Function Value [1] 9261.346 Gradient: [1] -10.8435561 -1.8263363 1.7913491 0.8619347 -0.2310277 0.5100082 [7] -0.6651974 -2.0938169 iteration = 37 Step: [1] 0.0112019739 0.0011510886 -0.0008251606 0.0003206906 0.0037098488 [6] 0.0031949483 0.0025725397 0.0034677805 Parameter: [1] 0.31739439 0.48311781 1.57405989 1.94306839 3.53008761 -0.55505905 [7] -1.01394078 -0.02764507 Function Value [1] 9261.22 Gradient: [1] -9.7630582 -1.6344584 1.8149033 0.6664129 -0.2475624 0.5120855 -0.5309911 [8] -1.6334179 iteration = 38 Step: [1] 0.096349651 0.015560038 -0.011969840 0.008268544 0.028626709 [6] 0.023153246 0.013773312 0.026558794 Parameter: [1] 0.413744042 0.498677847 1.562090048 1.951336937 3.558714319 [6] -0.531905799 -1.000167465 -0.001086273 Function Value [1] 9260.591 Gradient: [1] -5.40506699 -0.08609823 0.79820619 -0.11203823 -0.25346143 2.02586489 [7] 0.24631540 1.81562609 iteration = 39 Step: [1] 8.110980e-04 1.465455e-04 -9.498142e-05 1.237218e-04 4.392800e-04 [6] -1.240248e-04 -7.982568e-05 2.620452e-04 Parameter: [1] 0.4145551398 0.4988243929 1.5619950667 1.9514606592 3.5591535990 [6] -0.5320298234 -1.0002472909 -0.0008242282 Function Value [1] 9260.587 Gradient: [1] -5.43988608 -0.07424751 0.78971639 -0.09773339 -0.25061362 2.05533433 [7] 0.24688131 1.84490273 iteration = 40 Step: [1] 8.142115e-04 1.466341e-04 -9.494382e-05 1.238508e-04 4.406894e-04 [6] -1.254467e-04 -7.970065e-05 2.627728e-04 Parameter: [1] 0.4153693512 0.4989710270 1.5619001229 1.9515845100 3.5595942884 [6] -0.5321552700 -1.0003269916 -0.0005614553 Function Value [1] 9260.582 Gradient: [1] -5.47525269 -0.06241316 0.78120808 -0.08327936 -0.24773218 2.08508754 [7] 0.24744353 1.87421392 iteration = 41 Step: [1] 8.174039e-04 1.467365e-04 -9.491175e-05 1.239876e-04 4.421523e-04 [6] -1.268903e-04 -7.958461e-05 2.635315e-04 Parameter: [1] 0.4161867551 0.4991177636 1.5618052111 1.9517084976 3.5600364407 [6] -0.5322821604 -1.0004065762 -0.0002979239 Function Value [1] 9260.578 Gradient: [1] -5.51111407 -0.05059155 0.77268707 -0.06868177 -0.24481865 2.11510269 [7] 0.24800387 1.90355968 iteration = 42 Step: [1] 8.206673e-04 1.468512e-04 -9.488464e-05 1.241313e-04 4.436634e-04 [6] -1.283541e-04 -7.947703e-05 2.643179e-04 Parameter: [1] 4.170074e-01 4.992646e-01 1.561710e+00 1.951833e+00 3.560480e+00 [6] -5.324105e-01 -1.000486e+00 -3.360592e-05 Function Value [1] 9260.573 Gradient: [1] -5.54741928 -0.03878631 0.76415686 -0.05394903 -0.24187611 2.14536522 [7] 0.24856232 1.93293999 iteration = 43 Step: [1] 8.239937e-05 1.469777e-05 -9.486183e-06 1.242813e-05 4.452188e-05 [6] -1.298376e-05 -7.937767e-06 2.651297e-05 Parameter: [1] 4.170898e-01 4.992793e-01 1.561701e+00 1.951845e+00 3.560525e+00 [6] -5.324235e-01 -1.000494e+00 -7.092945e-06 Function Value [1] 9260.573 Gradient: [1] -5.55108090 -0.03760397 0.76330424 -0.05246412 -0.24157933 2.14841020 [7] 0.24861853 1.93588312 iteration = 44 Step: [1] 8.243305e-06 1.469909e-06 -9.485983e-07 1.242966e-06 4.453773e-06 [6] -1.299873e-06 -7.936834e-07 2.652126e-06 Parameter: [1] 4.170981e-01 4.992808e-01 1.561700e+00 1.951846e+00 3.560529e+00 [6] -5.324248e-01 -1.000495e+00 -4.440820e-06 Function Value [1] 9260.573 Gradient: [1] -5.55144652 -0.03748573 0.76321968 -0.05231591 -0.24154940 2.14871579 [7] 0.24862560 1.93617961 iteration = 45 Step: [1] 8.243640e-06 1.469923e-06 -9.485962e-07 1.242981e-06 4.453931e-06 [6] -1.300024e-06 -7.936763e-07 2.652208e-06 Parameter: [1] 4.171063e-01 4.992823e-01 1.561699e+00 1.951848e+00 3.560534e+00 [6] -5.324261e-01 -1.000496e+00 -1.788612e-06 Function Value [1] 9260.573 Gradient: [1] -5.5518121 -0.0373675 0.7631340 -0.0521677 -0.2415200 2.1490196 0.2486309 [8] 1.9364725 iteration = 46 Parameter: [1] 4.171071e-01 4.992824e-01 1.561699e+00 1.951848e+00 3.560534e+00 [6] -5.324262e-01 -1.000496e+00 -1.523383e-06 Function Value [1] 9260.573 Gradient: [1] -5.55184852 -0.03735659 0.76312585 -0.05215278 -0.24151688 2.14905049 [7] 0.24863084 1.93650339 Successive iterates within tolerance. Current iterate is probably solution. Error in solve.default(est$hessian) : system is computationally singular: reciprocal condition number = 4.25702e-18 Calls: imperfectCopulaREMADA.beta -> diag -> solve -> solve.default Execution halted * checking PDF version of manual ... [3s/3s] OK * checking HTML version of manual ... [1s/1s] OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * DONE Status: 1 ERROR, 2 NOTEs