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

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) serie=c(-0.079365084,-0.069496156,0.751957634,0.589682298,0.137093637,-0.441 328623,-0.315022903,-0.712451064,-1.018074469,0.040120362,0.91835343,0.58453 6266,0.108604444,0.433242111,0.058933309,0.225589817,0.693192915,0.213820667 ,1.187100752,0.172546055,-0.258930857,0.115163161,0.229929206,0.601060818,0. 408183205,-0.056855872,0.359542444,-0.103945202,-0.378895973,-0.247054478,-0 .390830296,-0.095556625,0.448324307,0.12365056,0.227877045,0.501396443,0.348 547289,0.487805845,0.28967922,0.363247268,0.463779702,0.295694172,0.45109397 6,-0.018372221,0.21107703,0.557664745,0.245845787,0.181719112,-0.573537008,- 0.009130336,0.428227159,0.634404883,0.39671858,0.502648936,0.606982137,0.080 060494,-0.142374112,-0.849546804,0.376479469,-0.080554938,-0.55670432,-0.360 815834,-0.461936422,0.163280152,0.036248301,0.162954952,0.171720409,-1.44620 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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