[R] Fine tunning rgenoud
Paul Smith
phhs80 at gmail.com
Wed Jul 4 16:22:38 CEST 2007
On 7/4/07, Paul Smith <phhs80 at gmail.com> wrote:
> On 7/4/07, RAVI VARADHAN <rvaradhan at jhmi.edu> wrote:
> > My point is that it might be better to try multiple (feasible) starting values for constrOptim to ensure that you have a good local minimum, since it appears that constrOptim converges to a boundary solution where the gradient is non-zero. That is why my code could be useful.
>
> Thanks, Ravi. I have used your function, which works pretty fine.
> However, constrOptim returns solutions markedly different, depending
> on the starting values. That is true that I am expecting a solution in
> the boundary, but should not constrOptim find boundary solutions
> correctly? The set of solution that I got is below.
Unless, there are many local optimal solutions...
Paul
> --------------------------------
>
> 2.67682495728743e-08 0.676401684216637 5.18627076390355e-09 0.00206463986063195 0.87185968612836 4.32039325909089e-11 0.999999999996234
> 3.71711020733097e-08 0.539853580957444 1.82592937615235e-08 0.00206941041763503 0.93305250393447 2.08076621230984e-11 0.999999999995774
> 1.55648443014316e-08 0.356047772992972 8.61341165816411e-09 0.00207149128044574 0.939531540703735 2.55211186629222e-12 0.999999999999424
> 2.20685747493755e-07 0.575689534431218 5.30976753476747e-08 0.00210500604605837 0.588947341576757 3.1310360048386e-10 0.999999999998789
> 1.92961662926727e-08 0.773588030510204 1.04841835042200e-08 0.00206723852358352 0.816755014708394 3.89478290348532e-11 0.999999999997794
> 0.000279824051289082 0.000310992385522886 1.01467522935252e-06 3.11645639181419e-05 0.00249801538651552 3.0978819115532e-05 7.11821104872585e-06
> 2.81901448690893e-07 0.381718731525906 4.72860507882539e-08 0.00206807672109157 0.769178513763055 1.39278079797628e-09 0.999999999967123
> 5.58938545019597e-05 0.00171253668169328 4.54005998518212e-09 0.00165663757292733 0.00247994862102590 6.20992250482468e-06 0.419169641865998
> 1.03300938985890e-08 0.438357835603591 6.89854079723234e-09 0.00206693286138396 0.977554885433201 1.17209206267609e-10 0.99999999996921
> 7.63336821363444e-05 0.00177141538041517 1.88050423143828e-10 0.00169507950991094 0.00249739505142207 8.4814984916537e-06 0.470929220605509
> 9.16005846107533e-09 0.682179815036755 1.63255733785783e-09 0.00206922107327189 0.919323193130209 5.71436138398897e-11 0.99999999999629
> 1.40968913167328e-08 0.343606628343661 1.33227447885302e-08 0.00206789984370423 0.343671264496824 1.11679312116211e-11 0.999999999999822
> 4.76054734844857e-09 0.593022549313178 2.28102966623129e-09 0.00206625165098398 0.947562121256448 8.9437610753173e-11 0.999999999999992
> 1.96950784184139e-07 0.579488113726155 1.61915231214025e-07 0.00208000350528798 1.00891340595040 1.22248906754713e-10 0.999999999996493
> 8.1448937742933e-09 0.441088618716555 4.54846390087941e-09 0.00207634940425852 0.446155700100820 4.81439647816238e-12 0.99999999999939
> 4.82439218405912e-08 0.557771049256698 3.53737879481732e-08 0.0020663035737319 0.588137767965923 2.6568947800491e-11 0.999999999988615
> 2.43086751126363e-08 0.522927598354163 2.26886829089137e-08 0.00206533531066324 0.611696593543814 4.51226610050184e-11 0.999999999999087
> 3.05498959434100e-08 0.465522202845817 1.09246302124670e-08 0.00207004066920179 0.465583376966915 3.24213847202457e-11 0.999999999997366
> 1.88687179088788e-07 0.783614197203923 4.51346471059839e-08 0.00222403775221293 0.786422171740329 8.17865794171933e-10 0.999999999986103
> 1.0154423824979e-08 0.302657777579883 9.06923080122203e-09 0.00206615353968094 0.359722316646974 8.27866320956902e-12 0.99999999998461
> 8.91008717665837e-08 0.0020661526864997 3.08619455858999e-09 0.00206579199039568 0.00275523149199496 9.55650084108725e-09 0.985185595958656
> 1.25320647920029e-07 0.635217955401437 7.44627883600107e-08 0.00206656250455391 0.855937507707323 3.70326032870889e-10 0.999999999998375
> 2.57618374406559e-08 0.636499151952225 1.09822023878715e-08 0.00206677354204888 0.772636071860102 8.99370944431481e-11 0.999999999978744
> 1.09474196877990e-08 0.501469973722704 1.19992915868609e-10 0.00206117941606503 0.501594064757161 1.34320044786225e-11 0.999999999991232
> 5.24203710193977e-05 0.000127998340144109 3.33258623630601e-09 7.55779680724378e-05 0.00248898574263025 5.82411313482383e-06 0.0221497278110802
> 3.80217498132259e-07 0.57664568703189 1.01755510162620e-08 0.00207232950382402 0.944031557945531 5.30703662426069e-10 0.999999999995957
> 1.45159816281038e-09 0.391742001993341 1.13492553980291e-09 0.00206615324883312 0.73041632635671 2.05351961803669e-11 0.99999999997684
> 8.74006318627465e-05 0.00176059707830211 1.94489765002966e-09 0.00167319599216734 0.00234472182706612 9.710963192258e-06 0.358282221037878
> 0.000238275046444342 0.000264776586825777 4.40904795740029e-10 2.64911355650347e-05 0.00259858019547791 2.64749446522330e-05 1.83580740341509e-07
> 7.39730651399581e-10 0.00908091738218919 2.48425610406978e-10 0.00206667909063917 0.00928420674840742 5.18131986840764e-11 0.99999875366957
> 6.76489417503509e-08 0.66948409231674 3.03852881114497e-08 0.00207327594614506 0.82038550137061 2.64031575134214e-11 0.999999999984622
> 2.47578596556463e-05 0.00192273935278028 1.23710433232059e-10 0.00189797520967688 0.00249714264227544 2.75085723641778e-06 0.654395200885401
> 1.89137722020867e-08 0.611106915103611 3.89703191870382e-09 0.00210184590871973 0.624411027730728 8.5135512646264e-11 0.99999999999684
> 0.00019176743398086 0.000561608946531094 3.86469382998825e-10 0.000369840972771214 0.00250444089363261 2.13074457599853e-05 0.0571352017772342
> 0.000185606084629350 0.000206269128591342 1.56795453162326e-11 2.06627190417154e-05 0.00249993097016385 2.06228887085015e-05 1.71605199821395e-06
> 6.07957336199056e-08 0.483682520205442 5.5863930138716e-09 0.00206322767692575 0.483682695616455 5.34362969147144e-11 0.999999999999643
> 2.32535804244419e-08 0.479995736120651 1.71422519965926e-08 0.00207338945556393 0.510141538832558 1.48964674702430e-11 0.999999999993732
> 8.9183702782689e-06 0.00206653282852738 1.75762003328053e-10 0.00205761314408722 0.00252353219843752 9.9090949737504e-07 0.833268509557228
> 1.98549228223182e-06 0.251617257564206 1.05359270191879e-06 0.0022260243792503 0.251624161164046 9.60072596752024e-08 0.99999999999983
> 3.98028438135354e-08 0.547004412551238 3.46420325883517e-08 0.00206593124574590 0.780233389945956 4.31912137883551e-11 0.999999999999248
> 7.415833973742e-09 0.411955745727431 6.63826778226352e-09 0.00206692655795474 0.422974654599724 6.0360458732987e-12 0.999999999999735
> 2.31198818168677e-09 0.00651214491263961 1.32346818065092e-09 0.00206600061742381 0.0308298577099849 1.09702273280814e-10 0.99987783901442
> 4.17312889043775e-08 0.535841994679291 2.16650907799191e-08 0.00206804295529002 0.59999577506709 1.18576996500002e-10 0.999999999999237
> 3.06571185479614e-08 0.210504230265054 2.05714086443132e-08 0.00206707615217700 0.218748559037881 2.15131848511398e-14 0.999999999993557
> 2.75615889053932e-09 0.410462493146348 1.61078464820130e-09 0.00206420001019636 0.410565659594555 9.40738331890563e-11 0.999999999999952
> 4.56613193593397e-09 0.408415058997493 2.33397373332447e-09 0.00206711241339086 0.532349316444773 6.74887208834456e-11 0.99999999999929
> 7.18189347926295e-08 0.456169544603848 5.38996419605601e-08 0.00206990174706466 1.10900729793115 1.09308927282691e-13 0.999999999990783
> 0.000188732057072893 0.000209745553735604 2.65570357321341e-11 2.10123232762018e-05 0.00249946399527847 2.09702247828353e-05 7.56804739408697e-07
> 5.85086685972612e-08 0.586106422015639 3.34740271798516e-08 0.00207017375045705 1.02476378656892 7.7586558103274e-11 0.999999999999751
> 0.000110963331385117 0.000134315151780862 1.07242866306447e-06 2.33497881113803e-05 0.00255535434807175 1.22100977811675e-05 0.0100715104652649
> 6.74756547544261e-05 0.00181277651670570 2.06882429964314e-10 0.0017453002881811 0.00248364499775618 7.49727080120661e-06 0.477865304416143
> 2.12169576142447e-09 0.258696699048681 3.83834275215625e-10 0.00206572907235368 0.593016720449582 2.47234004478185e-11 0.999999999976093
> 4.90290042793427e-08 0.845133894257049 4.26659941495083e-08 0.00206573635916222 0.846607006638287 2.63753283209851e-10 0.999999999994938
> 1.82215438136793e-08 0.80366504831158 1.78340820866931e-08 0.00225471081235068 0.805133180734986 2.65837229120276e-12 0.999999999992972
> 4.73449331282602e-08 0.284890209400889 3.18447541813436e-08 0.00206840481685483 0.808166776706798 4.05949074947464e-10 0.999999999996061
> 1.09692979675704e-08 0.504203767689845 1.91188777015599e-09 0.00206788476221373 0.706912076703946 4.06894266747559e-11 0.99999999999973
> 4.34112012988195e-08 0.458125660334097 6.46770375936903e-09 0.00207007623328698 0.625303714142111 1.01347377674089e-10 0.999999999992636
> 1.16509068624767e-07 0.598772124413396 1.83783072861908e-08 0.00207311616484422 0.649412674861061 1.72090788969858e-10 0.999999999982887
> 1.72904399298124e-08 0.58859841513479 5.56864060572789e-09 0.00206902943325815 0.834770114923498 4.44826394221343e-11 0.999999999998404
> 1.95167889112074e-07 0.680051903362475 4.07870548730064e-08 0.0021048787521164 0.758987053084469 3.35901569072651e-10 0.999999999999464
> 1.06657670445553e-08 0.705185947467634 5.468246700687e-09 0.00206717328235464 0.852353580488024 9.79781097317382e-11 0.999999999997673
> 3.38906655480421e-08 0.675066389889348 5.7356420037523e-09 0.00206939927716748 0.864714866878808 3.50701456652884e-10 0.999999999999926
> 4.72130896241037e-09 0.193480077858215 4.09642232386814e-09 0.00206639690357456 0.193772609031377 2.22379321541448e-11 0.999999999992884
> 3.55665252909232e-08 0.620669750121032 1.90641809388101e-08 0.00206864461846135 0.622207402604423 3.55684392133601e-11 0.999999999999635
> 3.43643575170166e-08 0.508032144170851 3.05576064051123e-08 0.0020666442030495 0.537473527046566 5.41317633959989e-12 0.999999999991207
> 2.07413577081273e-08 0.517006009937055 1.78794895322523e-08 0.00206609160101216 0.723546217083115 4.25770563404542e-11 0.999999999977351
> 2.9634111809255e-08 0.678167734702809 2.87136255305397e-08 0.00207005922065176 0.680058941948453 4.86294997869349e-11 0.999999999997674
> 2.70007729246723e-08 0.777109777637737 2.23198659183296e-08 0.00211784135373727 0.821904778895813 2.72480529179635e-10 0.999999999999417
> 1.21638582096581e-05 0.685066257234478 3.90995182181942e-06 0.00201558375343728 0.685516429216796 4.73765370536012e-08 0.999999999999993
> 6.21341332802845e-08 0.45153366532873 4.28836207599489e-08 0.00205228864113157 0.630886659065685 2.01031448944481e-10 0.999999999976095
> 1.19446880731148e-06 0.00204849378641721 5.41758686583886e-10 0.00204660358124349 0.00250002927315376 1.32657483581135e-07 0.926358671695374
> 4.23724996341138e-07 0.76589882078562 1.07976473375288e-07 0.00231084075578484 0.772123021534594 4.222800306051e-09 0.99999999999554
> 9.76490929801592e-09 0.611133237912937 3.40654501481527e-09 0.00206753317060016 0.624278286034974 1.35688861199835e-11 0.999999999991987
> 0.000185800168507657 0.000206604414024173 1.51257663200075e-10 2.08040548465085e-05 0.00249987187225613 2.06444402710859e-05 1.00090667437022e-05
> 4.87431144092831e-09 0.486985270937825 1.01147265793908e-09 0.00206873578985202 0.494229783529316 2.1163667211661e-12 0.999999999999217
> 1.58678140422157e-08 0.458475824861338 3.728478508397e-09 0.00206665340523142 0.822538576890515 6.73647051811321e-11 0.999999999992949
> 7.78797943133767e-07 0.00201714819466376 1.00271036821046e-09 0.00201517307886584 0.00243247068677390 8.6420943116519e-08 0.946415585057681
> 1.79734244345473e-07 0.618114350664356 8.41196223101054e-08 0.00209424634015987 1.09808725088463 1.36311179064841e-09 0.999999999999325
> 4.04750622932535e-06 0.00202435649740235 9.55231839434103e-11 0.00202030658534268 0.00249780762721203 4.49711157881299e-07 0.878051982789172
> 3.27944009298396e-08 0.710329613922295 2.02989735589882e-08 0.00206702754480486 0.713789321637634 4.43500632164969e-12 0.99999999998325
> 3.6211203640154e-08 0.486990872484964 2.23029819494578e-08 0.00206834819984283 0.925447188571729 1.43913590315638e-10 0.999999999999852
> 1.52117049757253e-08 0.499368102973816 5.70289700988348e-09 0.00206632041653347 0.688163413575525 6.86450078797379e-11 0.99999999998881
> 2.14084286484963e-08 0.669902287842233 1.64604388157258e-08 0.00206787121557579 0.675669448264383 3.05725070183645e-11 0.99999999999783
> 4.64383080200508e-05 0.00188125145190771 2.92580309919556e-10 0.00183481310287054 0.00250971056566956 5.15977640139516e-06 0.571340025768215
> 3.11099577796555e-08 0.319299482089934 8.84023352637676e-09 0.00206855023112842 0.441077204370492 1.10446087116967e-09 0.999999999995467
> 0.000102965203269986 0.0016493488546921 2.58445958349384e-10 0.00154638327053218 0.00250288634336625 1.14405481683120e-05 0.342341363715383
> 5.87912519479874e-09 0.00301357388024528 2.75211293457077e-10 0.00206603126487256 0.00686204622812863 6.1682829968701e-10 0.999931564740833
> 8.28694064868836e-08 0.673413934917665 5.55691871872657e-08 0.00206648968616976 0.690994737517309 8.53762213679993e-11 0.99999999999553
> 2.78964308878308e-08 0.631906605077265 1.87081150212841e-08 0.00206682302780812 1.04912853459322 1.19798263785958e-10 0.999999999998404
> 0.000186009655330342 0.000206938973112988 1.18525336022574e-09 2.09286186935030e-05 0.00249989443096258 2.06676012144089e-05 2.57319855839169e-06
> 5.79914576203232e-05 0.00181271191072469 1.15884908131972e-09 0.00175471081855071 0.0024812702174432 6.44334507197721e-06 0.509698530097913
> 2.0503183260964e-08 0.546450460261978 1.27059638843222e-08 0.00206791702172114 1.14082199649402 8.5751965370755e-12 0.99999999999683
> 3.66836690280629e-08 0.481874078118496 3.13611734401948e-08 0.00207354422550728 0.990376379663234 2.13707863395726e-10 0.999999999975761
> 1.77224405556878e-08 0.493128575789259 8.63845133717168e-09 0.00207307974398005 0.493553411995952 3.19150428367562e-11 0.999999999999902
> 0.000119234853016321 0.00194143059603129 2.65621637099599e-10 0.00182214114564480 0.00258385562559042 1.32482776896270e-05 0.564809333872708
> 1.91321783130778e-07 0.00205131977291163 2.45737561775750e-10 0.00205112100576657 0.00251498989989795 2.12303674145017e-08 0.981038377325434
> 2.04970869809e-09 0.61540968024267 1.80692316850342e-10 0.00206758160218205 0.616881623876956 9.88879753424694e-11 0.999999999998682
> 2.28380664111838e-07 0.495196927735637 2.03520983522517e-07 0.00206484911183903 0.495729865740038 3.10063601627842e-10 0.999999999998029
> 0.000142663207352814 0.00136226156979156 2.47224893929883e-11 0.00121959537442013 0.00249964872330666 1.58514632925203e-05 0.213308634647407
>
>
>
> > ----- Original Message -----
> > From: Paul Smith <phhs80 at gmail.com>
> > Date: Wednesday, July 4, 2007 6:00 am
> > Subject: Re: [R] Fine tunning rgenoud
> > To: R-help <r-help at stat.math.ethz.ch>
> >
> >
> > > On 7/4/07, RAVI VARADHAN <rvaradhan at jhmi.edu> wrote:
> > > > Here is another approach: I wrote an R function that would generate
> > > interior points as starting values for constrOptim. This might work
> > > better than the LP approach, since the LP approach gives you a
> > > starting value that is on the boundary of the feasible region, i.e a
> > > vertex of the polyhedron, whereas this new approach gives you points
> > > on the interior. You can generate as many points as you wish, but the
> > > approach is brute-force and is very inefficient - it takes on the
> > > order of a 1000 tries to find one feasible point.
> > >
> > > Thanks again, Ravi. Actually, the LP approach also works here. Let
> > > g(X) >= k be the constraints. Then, by solving a LP problem with the
> > > constraints
> > >
> > > g(X) >= (k+0.2)
> > >
> > > returns an interior starting value for constrOptim. I am aware that
> > > the new set of constraints may correspond to an impossible linear
> > > system, but it works in many cases.
> > >
> > > Paul
> > >
> > > > ----- Original Message -----
> > > > From: Paul Smith <phhs80 at gmail.com>
> > > > Date: Tuesday, July 3, 2007 7:32 pm
> > > > Subject: Re: [R] Fine tunning rgenoud
> > > > To: R-help <r-help at stat.math.ethz.ch>
> > > >
> > > >
> > > > > On 7/4/07, Ravi Varadhan <rvaradhan at jhmi.edu> wrote:
> > > > > > It should be easy enough to check that your solution is valid
> > > (i.e.
> > > > > a local
> > > > > > minimum): first, check to see if the solution satisfies all the
> > > > > > constraints; secondly, check to see if it is an interior point
> > > > > (i.e. none of
> > > > > > the constraints become equality); and finally, if the solution
> > > is an
> > > > > > interior point, check to see whether the gradient there is
> > > close to
> > > > > zero.
> > > > > > Note that if the solution is one of the vertices of the polyhedron,
> > > > > then the
> > > > > > gradient may not be zero.
> > > > >
> > > > > I am having bad luck: all constraints are satisfied, but the solution
> > > > > given by constrOptim is not interior; the gradient is not equal
> > > to
> > > > > zero.
> > > > >
> > > > > Paul
> > > > >
> > > > >
> > > > > > -----Original Message-----
> > > > > > From: r-help-bounces at stat.math.ethz.ch
> > > > > > [ On Behalf Of Paul Smith
> > > > > > Sent: Tuesday, July 03, 2007 5:10 PM
> > > > > > To: R-help
> > > > > > Subject: Re: [R] Fine tunning rgenoud
> > > > > >
> > > > > > On 7/3/07, Ravi Varadhan <rvaradhan at jhmi.edu> wrote:
> > > > > > > You had indicated in your previous email that you are having
> > > trouble
> > > > > > finding
> > > > > > > a feasible starting value for constrOptim(). So, you basically
> > > > > need to
> > > > > > > solve a system of linear inequalities to obtain a starting point.
> > > > > Have
> > > > > > you
> > > > > > > considered using linear programming? Either simplex() in the
> > > "boot"
> > > > > > package
> > > > > > > or solveLP() in "linprog" would work. It seems to me that you
> > > > > could use
> > > > > > any
> > > > > > > linear objective function in solveLP to obtain a feasible
> > > > > starting point.
> > > > > > > This is not the most efficient solution, but it might be
> > > worth a
> > > > > try.
> > > > > > >
> > > > > > > I am aware of other methods for generating n-tuples that satisfy
> > > > > linear
> > > > > > > inequality constraints, but AFAIK those are not available in
> > > R.
> > > > > >
> > > > > > Thanks, Ravi. I had already conceived the solution that you suggest,
> > > > > > actually using "lpSolve". I am able to get a solution for my problem
> > > > > > with constrOptim, but I am not enough confident that the
> > > solution is
> > > > > > right. That is why I am trying to get a solution with rgenoud,
> > > but
> > > > > > unsuccessfully until now.
> > > > > >
> > > > > > Paul
> > > > > >
> > > > > >
> > > > > >
> > > > > > > -----Original Message-----
> > > > > > > From: r-help-bounces at stat.math.ethz.ch
> > > > > > > [ On Behalf Of Paul Smith
> > > > > > > Sent: Tuesday, July 03, 2007 4:10 PM
> > > > > > > To: R-help
> > > > > > > Subject: [R] Fine tunning rgenoud
> > > > > > >
> > > > > > > Dear All,
> > > > > > >
> > > > > > > I am trying to solve the following maximization problem, but
> > > I cannot
> > > > > > > have rgenoud giving me a reliable solution.
> > > > > > >
> > > > > > > Any ideas?
> > > > > > >
> > > > > > > Thanks in advance,
> > > > > > >
> > > > > > > Paul
> > > > > > >
> > > > > > > ----------------------------
> > > > > > > library(rgenoud)
> > > > > > >
> > > > > > > v <- 0.90
> > > > > > > O1 <- 10
> > > > > > > O2 <- 20
> > > > > > > O0 <- v*O1+(1-v)*O2
> > > > > > >
> > > > > > > myfunc <- function(x) {
> > > > > > > U0 <- x[1]
> > > > > > > U1 <- x[2]
> > > > > > > U2 <- x[3]
> > > > > > > q0 <- x[4]
> > > > > > > q1 <- x[5]
> > > > > > > q2 <- x[6]
> > > > > > > p <- x[7]
> > > > > > >
> > > > > > > if (U0 < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if (U1 < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if (U2 < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if ((U0-(U1+(O1-O0)*q1)) < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if ((U0-(U2+(O2-O0)*q2)) < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if ((U1-(U0+(O0-O1)*q0)) < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if ((U1-(U2+(O2-O1)*q2)) < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if((U2-(U0+(O0-O2)*q0)) < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if((U2-(U1+(O1-O2)*q1)) < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if(p < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if(p > 1)
> > > > > > > return(-1e+200)
> > > > > > > else if(q0 < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if(q1 < 0)
> > > > > > > return(-1e+200)
> > > > > > > else if(q2 < 0)
> > > > > > > return(-1e+200)
> > > > > > > else
> > > > > > >
> > > > > > return(p*(sqrt(q0)-(O0*q0+U0))+(1-p)*(v*(sqrt(q1)-(O1*q1+U1))+(1-v)*(sqrt(q2
> > > > > > > )-(O2*q2+U2))))
> > > > > > >
> > > > > > > }
> > > > > > >
> > > > > > genoud(myfunc,nvars=7,max=T,pop.size=6000,starting.values=runif(7),wait.gene
> > > > > > > rations=150,max.generations=300,boundary.enforcement=2)
> > > > > > >
> > > > > > > ______________________________________________
> > > > > > > R-help at stat.math.ethz.ch mailing list
> > > > > > >
> > > > > > > PLEASE do read the posting guide
> > > > > >
> > > > > > > and provide commented, minimal, self-contained, reproducible
> > > code.
> > > > > > >
> > > > > >
> > > > > > ______________________________________________
> > > > > > R-help at stat.math.ethz.ch mailing list
> > > > > >
> > > > > > PLEASE do read the posting guide
> > > > > > and provide commented, minimal, self-contained, reproducible code.
> > > > > >
> > > > >
> > > > > ______________________________________________
> > > > > R-help at stat.math.ethz.ch mailing list
> > > > >
> > > > > PLEASE do read the posting guide
> > > > > and provide commented, minimal, self-contained, reproducible code.
> > > >
> > > >
> > >
> > > ______________________________________________
> > > R-help at stat.math.ethz.ch mailing list
> > >
> > > PLEASE do read the posting guide
> > > and provide commented, minimal, self-contained, reproducible code.
> >
>
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