[R-br] Erro 'vmmin' is not finite

[Pedro Henrique Melo Albuquerque]

Olá pessoal,

Estou precisando de ajuda para entender e consertar o erro que surge na
minha programação.

Eu tenho um procedimento iterativo que em determinado ponto a função de
otimização dá o seguinte erro:

 

Error in optim(par = theta.old, fn = fun, gr = gradient, control = control,
:   initial value in 'vmmin' is not finite

 

Quando eu testo a função no ponto inicial ela produz valor igual a
-1035.843. Não sei bem o que está acontecendo e por enquanto estou sem
idéias de como resolver.

Alguém poderia me ajudar ?

Segue parte da programação para quem quiser tentar reproduzir.

 

 

#Inicio

library(maxLik)

library(fGarch)

 

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5207,0.874081407,0.695217411,0.87330987,0.867308158,0.618816656,0.715744141,
0.779316139,0.634121334,0.663156424,0.79565288,0.618462102,0.758425701,0.649
295069,0.654578326,0.968102041,

0.586000844,0.729388358,0.693805148,0.78454375,0.8302603,0.560009662,0.57304
5932,0.793391099,0.684560175,0.592219206,0.819346833,0.573665175,0.664914961
,0.699485503,0.790357518,0.644411815,0.596521438,0.918622798,0.999038909,0.5
94936037,0.595958546,0.921734961,0.600028996,0.959009247,0.999910341,0.67108
8958,0.785997956,0.650955684,0.724125029,0.654040802,0.829841842,0.662011521
,0.788093992,0.703696317,0.870569492,0.757489597,0.739795721,0.748535808,0.6
86979964,0.682717943,0.787643285,0.723711912,0.805741943,0.590927275,0.99755
6895,0.64840838,0.661889103,0.989500072,0.982231792,0.692535101,0.690063528,
0.594358254,0.9925995,0.965018898,0.92757299,0.659744062,0.63002245,0.974092
049,0.999954887,0.740196217,0.776767992,0.828745515,0.715414763,0.694721201,
0.857298938,0.754909306,0.807906406,0.645004739,0.626724701,0.689024936,0.62
2229816,0.663402821,0.878294617,0.954762073,0.656399771,0.6157987,0.94756303
5,0.943198804,0.971839455,0.695809694,0.906346386,0.817018685,0.605465126,0.
676896148,0.819709313,0.753347152,0.95011815,0.578918279,0.573864141,0.97252
262,0.969661156,0.992915989,0.613029752,0.866082706,0.88393885,0.71486094,0.
617578923,0.691191174,0.642997539,0.923256706,0.978189236,0.931994137,0.9322
48551,0.99766111,0.584027046,0.992606263,0.982024995,0.617169752,0.967345274
,0.638602639,0.849646732,0.864977108,0.643223732,0.880563758,0.858599919,0.9
31545755,0.700790135,0.795512685,0.773768516,0.664288555,0.726119411,0.90539
6625,0.666052195,0.699863726,0.759263114,0.744838523,0.88245163,0.783840382,
0.980313853,0.623512672,0.861550068,0.782189372,0.906617513,0.995452209,0.84
8789134,0.986859983,0.954139946,0.538423798,0.638453294,0.761764366,0.960889
052,0.994377177,0.964514777,0.839783688,0.998024476,0.998301832,0.580251363,
0.710979202,0.700236606,0.812105889,0.894042212,0.709414168,0.921785151,0.64
4864393,0.996288146,0.562441878,0.986116715,0.649342464,0.615535578,0.896079
29,0.639143388,0.867704269,0.999076104,0.67507475,0.590902579,0.623403026,0.
940860136,0.97937101,0.975442167,0.997914837,0.64977211,0.667187825,0.805216
121,0.828705859,0.677201741,0.614412219,0.749892144,0.618371011,0.653470524,
0.794472922,0.731627473,0.908917194,0.634181636,0.844188581,0.62523488,0.916
89965,0.841598013,0.594219872,0.897070533,0.673481118,0.940002034,0.64812906
6,0.910421594,0.888298847,0.724710156,0.548758434,0.611983107,0.972595913,0.
670013555,0.780457467,0.58276699,0.88595725,0.621889273,0.670999719,0.549725
921,0.541546041,0.720969875,0.964505552,0.993120581,0.993643502,0.95806962,0
.613409357,0.999575086,0.870137807,0.972261886,0.996691765,0.621927293,0.843
450885,0.735742806,0.985257464,0.7396242,0.692469107,0.702823721,0.616738905
,0.683069009,0.75940048,0.709895616,0.712800926,0.981637564,0.99724576,0.922
893008,0.715184072,0.606768017,0.924924836,0.969282494,0.967233975,0.9995317
72,0.924296586,0.98061557,0.798092177,0.649278708,0.989529417,0.862500981,0.
875804724,0.618357049,0.985839594,0.96320607,0.928532838,0.752358845,0.62746
6244,0.986217601,0.921366983,0.667844862,0.642641528,0.947330653,0.793585344
,0.974511258,0.948234325,

0.97578019,0.67314542,0.920207674,0.667646453,0.641221885,0.842653,0.6318296
43,0.767633084,0.75757261,0.768270012,0.628310532,0.721399779,0.91384076,0.8
88446219,0.993928594,0.939274163,0.991429179,0.997995568,0.764600969,0.68059
4583,0.84194063,0.632913287,0.776391519,0.900678662,0.985460733,0.958995928,
0.971716036,0.895416895,0.80582933,0.702694842,0.71495025,0.655151501,0.6326
99182,0.816961443,0.896100083,0.938921787,0.683317546,0.643639284,0.97240359
6,0.992950418,0.987134616,0.642151784,0.955614119,0.841021152,0.665787218,0.
792365429,0.716490751,0.787229657,0.821286555,0.644510993,0.752213217,0.7907
01389,0.978082111,0.99878154,0.771458028,0.703904158,0.910464325,0.992550966
,0.979105418,0.720549316,0.668474588,0.914222529,0.72567178,0.751182536,0.95
0114977,0.802149271,0.764949297,0.952016818,0.746163695,0.690671368,0.737816
672,0.791562655,0.854394324,0.887326321,0.799516003,0.870765949,0.945261654,
0.957930785,0.647247382,0.640101292,0.891296005,0.688851155,0.975991025,0.67
7180995,0.637955633,0.769257313,0.755243339,0.920400602,0.900945779,0.724313
703,0.671826593,0.680238004,0.850420144,0.898614863,0.664585643,0.750726295,
0.77799195,0.707385531,0.819850917,0.723238854,0.818977947,0.679345983,0.671
744213,0.761151462,0.715151783,0.640218999,0.88416553,0.816762067,0.99431608
,0.767328803,0.778655576,0.635787138,0.677570692,0.98833844,0.707616895,0.96
3085202,0.973634124,0.946722795,0.816848655,0.738369458,0.637082037,0.986227
047,0.920899833,0.961424609,0.87309134,0.76742554,0.670526903,0.679612956,0.
765805462,0.626932715,0.792323692,0.704449784,0.671816921,0.619161289,0.7148
77397,0.965451408,0.913756578,0.908883298,0.605886492,0.650624335,0.61810662
2,0.721065563,0.971044289,0.764088248,0.715968442,0.622633189,0.787877274,0.
698771595,0.750841695,0.826437682,0.749961833,0.620803827,0.945403202,0.8333
16665,0.685169181,0.772001698,0.9764608,0.900842761,0.663953521,0.964945521,
0.710723963,0.892719718,0.648237826,0.633466579,0.980004284,0.906169871,0.61
1745673,0.635712727,0.643710338,0.967728475,0.99766852,0.888805445,0.9171269
13,0.946768469,0.997498664,0.945736585,0.648908276,0.685097836,0.989742507,0
.90018473,0.819952148,0.999139787,0.774037242,0.756165481,0.99195587,0.98376
4786,0.705539966,0.685540673,0.993207472,0.851865862,0.858842894,0.731227184
,0.86738221,0.920486697,0.757414511,0.950832437,0.832410392,0.756281003,0.78
2941702,0.903850749,0.911639758,0.93922286,0.67721019,0.783874952,0.83136808
7,0.749785101,0.676940583,0.855320704,0.879669734,0.86667928,0.781364512,0.6
66479773,0.67956775,0.652799869,0.889864061,0.708391824,0.65533963,0.8179699
17,0.984098742,0.876140577,0.74185076,0.648178741,0.715017497,0.867709461,0.
861594815,0.845196826,0.891786128,0.739950827,0.670025659,0.740873997,0.7661
03804,0.826431419,0.85739409,0.707463358,0.799520437,0.942985006,0.983164999
,0.853730886,0.634223083,0.67812926,0.924294198,0.97972925,0.919578568,0.871
882032,0.65136436,0.777115514,0.788432424,0.769049063,0.721502526,0.77080897
8,0.811571727,0.64891799,0.83604454,0.626021432,0.85667734,0.845712672,0.832
367934,0.651240775,

0.943317844,0.731680468,0.751430142,0.758053166,0.689591337,0.625609732,0.71
8233986,0.996430774,0.946047256,0.619590101,0.648888405,0.673073613,0.793214
045,0.78967679,0.996867967,0.99697388,0.924799274,0.868407626,0.650038499,0.
664628537,0.852639538,0.995274172,0.961814579,0.624344989,0.860323057,0.7636
46624,0.900244566,0.848660032,0.650696881,0.623373149,0.955279869,0.75000044
7,0.999684509,0.989765881,0.933937399,0.636142224,0.634571217,0.663101409,0.
766940401,0.648085092,0.887723095,0.777611068,0.935973694,0.970997099,0.6746
96208,0.995232624,0.968523478,0.703226002,0.655455441,0.705463943,0.99370957
7,0.860047918,0.749826487,0.895254949,0.771806577,0.82226122,0.853413306,0.9
8644143,0.650748781,0.655510172,0.815715777,0.754802639,0.679725717,0.839522
614,0.696698898,0.631751217,0.911917972,0.811840087,0.64323545,0.910891164,0
.992163403,0.82885893,0.92466542,0.961311066,0.948969801,0.809618027,0.69629
5483,0.985301551,0.992002513,0.939415893,0.882999579,0.989470739,0.737462158
,0.721926366,0.998641175,0.770021979,0.867725658,0.650777757,0.922672091,0.6
59551419,0.773719777,0.822281624,0.657750955,0.717943782,0.679543307,0.82370
2966,0.644052581,0.951402435,0.882339065,0.969117773,0.744688898,0.96670639,
0.795841944,0.762912423,0.65500517,0.808541323,0.867899788,0.941626083,0.677
2683,0.803655756,0.985989399,0.978423332,0.788015555,0.930148806,0.882851789
,0.731670979,0.904935334,0.967556903,0.930389071,0.681017923,0.862284976,0.6
56846781,0.953295855,0.844363463,0.976087578,0.985577958,0.85675663,0.698811
253,0.937601869,0.774161887,0.678476759,0.943608953,0.917370747,0.731073608,
0.709211386,0.692096748,0.902167102,0.684261751,0.748894886,0.900612671,0.70
5749475,0.924614028,0.901060079,0.977472149,0.737464506,0.653441775,0.915069
172,0.673392143,0.856418518,0.924463351,0.919055672,0.863996033,0.857930911,
0.752185059,0.72804152,0.78289704,0.752011371,0.827623109,0.668972736,0.8712
66849,0.888240122,0.663584131,0.801988227,0.871934733,0.867706661,0.67438064
9,0.73467111,0.824003626,0.904066246,0.651934932,0.717132251,0.729806716,0.6
48062963,0.979751067,0.812263952,0.667168564,0.897291439,0.997898759,0.92888
03,0.724707529,0.765785386,0.728450855,0.796234892,0.82690726,0.647170696,0.
646271734,0.969156821,0.95135389,0.708430496,0.976871642,0.87422059,0.773064
548,0.81038885,0.721408849,0.656119714,0.734668398,0.823623824,0.680571447,0
.717395669,0.765637895,0.950921031,0.950666457,0.721578002,0.762727612,0.756
370276,0.675516964,0.906627641,0.851067174,0.739610534,0.733427428,0.6403192
36,0.860312101,0.855440687,0.930745658,0.814507721,0.77962743,0.805484166,0.
845829811,0.836484329,0.674916411,0.804515264,0.723884981,0.687811571,0.8623
09571,0.964524673,0.808386342)

 

matrizEsperanca=cbind(col1,col2)

 

 

 

parametros=c(0.208464913,6.95E-20,0.038663299,0.895792587,0.009160388,0.1062
25595,0.893674651,1.429250593,1.910965368,0.230183544)

parametros[1]=pi1=mean(matrizEsperancas[,1])

 

                                

 

#Verossimilhança ponderada 

logLikelihood=function(parametrosLike)

{

                matrizDensidade=matrix(0,length(vetor),2)

      sigma21=rep(1,length(serie))

      sigma22=rep(1,length(serie))

                nu1=parametrosLike[7]

                nu2=parametrosLike[8]

                mu1=parametrosLike[9]

                for(i in 2:length(vetor))

                {

                erro2=serie[i-1]^2

 
sigma21[i]=parametrosLike[1]+parametrosLike[2]*erro2+parametrosLike[3]*sigma
21[i-1]

 
sigma22[i]=parametrosLike[4]+parametrosLike[5]*erro2+parametrosLike[6]*sigma
22[i-1]

 

 
matrizDensidade[i,1]=log(dged(vetor[i],mu1,sqrt(sigma21[i]),nu1))

 
matrizDensidade[i,2]=log(dged(vetor[i],-(pi1/(1-pi1))*mu1,sqrt(sigma22[i]),n
u2))

                                                

                }

                somaLogLik=0

                for(i in 1:length(vetor))

                {

                                for(j in 1:ncol(matrizDensidade))

                                {

 
somaLogLik=somaLogLik+matrizEsperancas[i,j]*matrizDensidade[i,j]

                                }

                }

 

return(somaLogLik)

 

}

 

 

 

A = matrix(0,10,9)

A[1,1]=A[2,2]=A[3,3]=A[4,4]=A[5,5]=A[6,6]=A[7,7]=A[8,8]=1

A[9,2]=A[9,3]=-1

A[10,5]=A[10,6]=-1

B = matrix(0,10,1)

B[9]=B[10]=1

 

parametrosLike=parametros[2:length(parametros)]

 

logLikelihood(parametrosLike)

res = maxBFGS(logLikelihood, start=parametrosLike, constraints=list(ineqA=A,
ineqB=B))

 

#Muito Obrigado pela ajuda.

#Pedro

 

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