Prezado Prof. Paulo, novamente, obrigado, realmente eu estava fazendo esta confusão. Sem sombra de dúvida, era exatamente isso que eu estava acreditando.
Quanto a distância dos pontos, eles podem ser relativamente próximos sim, se comparados com a máxima distância entre dois pontos na área de estudo. Pois pontos vizinhos estão, aproximadamente, equidistantes 1 km um dos outros, e a máxima distância entre pontos chega a quase 15 graus de latitude. É uma área de estudo é consideravelmente grande. Mas este não seria um problema para aplicação do método, certo Prof.? abs _____________________________ Rodrigo Sant'Ana Oceanógrafo Em 18 de setembro de 2012 16:34, Paulo Justiniano <[email protected]> escreveu: > Caro Rodrigo > > O fato de com matern Kappa > 4 dar problema quase certamente está ligado com > o provavel fato de que voce tem localizacoes muito proximas comparadas com a > maior distancia entrew pontos na área > > quant a escolha do GaUSSIANO VOCE ESTÁ FAZENDO UMA CONFUSÃO: > a distribuicao normal multivariada da resposta nao tem NADA a ver > com o modelo gaussiano de funcao de correlacao > > é uma simples coincidencia de nomes pelo fato da segunda ter uma expressao > que coincide com o nucleo de uma distribuicao normal. > > Mas distribuicao a resposta e funcao de correlacao sao coisas distintas > (da mesma forma que o modelo exponencial nao tem NADAa ver com a > distribuicaoi de resposta que pode perfeitamente ser normal > > > > On Tue, 18 Sep 2012, Rodrigo Sant'Ana wrote: > >> Prof. Paulo, >> >> muito obrigado, muito obrigado mesmo.. Mesmo sem reproduzir o >> exemplo, o Sr. atingiu o nervo da questão. >> Adicionei, um nugget maior que zero a fim de testar o procedimento, e >> rodou sem apresentar o Erro anterior. >> Utilizei também, conforme sua orientação, um modelo Matérn com kappa >> 4, pois com valores superiores o problema anterior se repetiu. e o >> mesmo rodou perfeitamente. >> >> Quanto ao fato da insistência em rodar um modelo gaussiano vem da >> origem destes dados. Eles foram simulados por uma distribuição normal >> multivariada, buscando-se assemelhar a distribuição de uma espécie de >> peixe em uma área conhecida, de onde posteriormente foram tiradas >> amostras aleatórias variando o "n" e o desenho amostral empregado. O >> intuito final é verificar as diferenças entre os distintos >> procedimentos amostrais em estimar a biomassa total do recurso em >> relação ao total simulado pela distribuição normal multivariada, sob o >> enfoque de métodos distintos de expansão/estimação. De todo modo, >> outros modelos também foram ajustados também (esférico, matern, >> exponencial) para comparação com o modelo Gaussiano, o qual era >> esperado o melhor ajuste. >> >> Novamente, muito obrigado por seus ensinamentos Prof. Paulo. Muito >> obrigado mesmo! >> >> _______________ >> Rodrigo Sant'Ana >> Oceanógrafo >> >> >> Em 17 de setembro de 2012 17:55, Paulo Justiniano >> <[email protected]> escreveu: >>> >>> Não tentei reproduzir seu exemplo mas suas informações dão uma pista do >>> problema >>> >>> O modelo Gaussiano de função de correlação é muito suave na origem. >>> A consequyencioa disto é q de que facilmente pode-se gerar >>> inderteminações >>> numéricas com este modelo. >>> >>> Isto ocorre quando o parametro de nugget está em zero aliado a >>> coordenadas >>> muito proximas de pontos. A suavidade da função acaba gerando >>> linhas/colunas >>> na matrix de correlação não distinguiveis nomericamente e daí a >>> singularidade (numérica) >>> >>> Este fato é bem conhecido e por isto não se recomenda o uso do Gaussiano, >>> ou >>> se recomenda combinado com algum nugget (mas ai somente para predicao) >>> >>> Se quiser obter algo semelhante ao gaussiano (por que usar este modelo tb >>> é >>> uma pergunta relevante - o fato de que "todo mundo usa" nao é resposta >>> válida) sugiro usar o Matern com um valor mais elevado de kappa (4.5 ou >>> superior. >>> >>> >>> >>> On Mon, 17 Sep 2012, Rodrigo Sant'Ana wrote: >>> >>>> Pessoal, >>>> >>>> no email enviando anteriormente existiam alguns erros no CMR, bem como >>>> a identificação do problema estava muito vaga. Seguem as correções no >>>> mesmo para que seja possível reproduzi-lo. Desculpem-me pelos erros >>>> anteriores. >>>> >>>> Identificação do problema: Erro em solve.default(v$varcov, xmat) : >>>> sistema é computacionalmente >>>> singular: condição recíproca número = 6.36899e-20 >>>> >>>> Função utilizada: geoR::likfit >>>> >>>> Dúvida: Busquei como solução, passar pelo argumento control da função >>>> geoR::likfit algumas mundanças na optimização do modelo, tais como, >>>> control = list(method = "CG"); Mas não obtive exito. Rodando outros >>>> modelos >>>> que não o Gaussiano para a mesma base de dados (ex: Exponencial, >>>> Matern, Esférico) o problema não ocorre. >>>> Teria como passar uma modificação para matrix svd?? Acho que é ai onde >>>> esta ocorrendo o Erro, não? >>>> >>>> Código Minimo Reproduzível >>>> >>>> ## Carrega o pacote geoR >>>> library(geoR) >>>> >>>> ## Entrada dos dados >>>> dados.geo <- structure(list(coords = structure(c(-42.2064355778, >>>> -42.3032008959, >>>> -41.5452059035, -42.1580529187, -41.1420170777, -42.9724943467, >>>> -41.7306727634, -43.2224714187, -42.601560627, -43.0773234414, >>>> -43.0692596649, -42.61768818, -42.6499432861, -42.7708999338, >>>> -42.6338157331, -43.0128132293, -42.2951371194, -42.319328449, >>>> -41.7387365399, -43.0692596649, -42.3032008959, -42.3596473316, >>>> -42.4402850967, -43.4643847142, -43.6579153505, -43.7304893392, >>>> -43.8272546573, -42.7547723808, -44.2143159301, -43.7224255627, >>>> -43.117642324, -43.4724484907, -44.3272088013, -43.7466168922, >>>> -43.4160020551, -44.149805718, -43.8595097634, -44.6175047559, >>>> -44.1981883771, -44.2143159301, -44.6497598619, -45.0690762407, >>>> -44.9884384756, -44.5368669907, -44.8836093809, -44.5852496498, >>>> -44.7707165096, -45.1497140059, -45.5448390551, -44.948119593, >>>> -45.1497140059, -45.6254768203, -46.1334947407, -45.0610124642, >>>> -45.4722650665, -45.6254768203, -45.2787344301, -45.5932217142, >>>> -45.1900328884, -45.9480278809, -45.9641554339, -46.2141325059, >>>> -45.5125839491, -46.4963646839, -46.6253851082, -45.4077548544, >>>> -45.6335405968, -46.6495764377, -46.6737677673, -46.6253851082, >>>> -45.6496681498, -46.1415585173, -45.9641554339, -45.746433468, >>>> -46.5528111195, -45.99641054, -47.0688928165, -46.0447931991, >>>> -46.1334947407, -46.4318544718, -46.3834718127, -46.52861979, >>>> -46.6737677673, -47.4478903128, -46.4318544718, -47.2624234529, >>>> -47.4398265362, -47.310806112, -46.9882550514, -46.3270253771, >>>> -46.810851968, -46.8592346271, -46.8269795211, -47.6817398317, >>>> -47.1898494643, -47.4962729718, -47.4801454188, -46.9721274983, >>>> -47.6978673847, -47.0043826044, -47.6575485022, -47.7059311612, >>>> -47.8913980211, -47.4156352067, -47.843015362, -47.6736760552, >>>> -47.4720816423, -47.1575943582, -47.06082904, -47.2221045703, >>>> -47.6252933961, -47.7381862673, -47.3188698885, -47.1253392521, >>>> -47.3027423355, -47.1092116991, -47.8026964794, -47.9317169037, >>>> -47.6333571726, -47.5365918544, -47.576910737, -47.0527652635, >>>> -47.3591887711, -46.9801912749, -47.7220587143, -47.2059770173, >>>> -47.5688469605, -48.0849286575, -47.6575485022, -47.9800995628, >>>> -47.560783184, -48.1978215287, -47.9559082332, -48.4800537067, >>>> -48.6332654605, -48.6493930135, -48.5445639188, -48.4961812598, >>>> -48.3671608355, -48.2381404113, -47.9962271158, -48.8106685439, >>>> -48.7139032257, -47.9155893506, -47.9317169037, -48.5606914719, >>>> -48.4074797181, -48.9477527446, -47.9720357863, -48.5203725893, >>>> -48.7461583317, -48.0607373279, -49.0848369454, -48.8671149795, >>>> -49.0929007219, -48.3913521651, -48.9638802976, -49.1977298166, >>>> -48.5606914719, -49.2461124757, -48.3268419529, -48.7300307787, >>>> -48.1736301991, -49.407388006, -48.4477986007, -49.0525818393, >>>> -48.5284363658, -49.7138115136, -48.7622858848, -49.5041533242, >>>> -49.6815564075, -49.0929007219, -49.3590053469, -49.3509415704, >>>> -49.6654288545, -49.8831508204, -49.8428319378, -49.8347681613, >>>> -49.9637885855, -49.8105768318, -50.1492554454, -49.8670232674, >>>> -50.0363625742, -50.455678953, -50.3669774113, -50.3024671992, >>>> -50.4072962939, -50.3589136348, -50.5121253886, -50.6492095894, >>>> -50.3831049643, -50.9153142144, -50.6814646954, -51.1491637333, >>>> -50.7540386841, -51.4959061235, -51.4797785704, -51.3991408053, >>>> -51.6007352182, -51.5281612295, -23.1939349125, -23.2357031667, >>>> -23.26911777, -23.3359469767, -23.36936158, -23.3777152309, >>>> -23.4027761834, >>>> -23.4111298342, -23.4194834851, -23.4278371359, -23.4361907867, >>>> -23.4528980884, -23.4612517392, -23.4779590409, -23.5280809459, >>>> -23.5280809459, -23.5614955492, -23.5614955492, -23.5698492001, >>>> -23.5782028509, -23.6366784067, -23.7118612643, -23.7619831693, >>>> -23.7619831693, -23.7619831693, -23.8121050743, -23.8288123759, >>>> -23.8538733284, -23.8622269793, -23.8705806301, -23.9039952335, >>>> -23.9039952335, -23.9541171385, -24.079421901, -24.1378974568, >>>> -24.1462511077, -24.2715558702, -24.2966168227, -24.3634460294, >>>> -24.3885069819, -24.5472263477, -24.6808847611, -24.7393603169, >>>> -24.7477139677, -24.7727749202, -24.7811285711, -24.8479577778, >>>> -24.9649088894, -25.1152746045, -25.1570428586, -25.3241158753, >>>> -25.3241158753, -25.340823177, -25.3658841295, -25.390945082, >>>> -25.3992987328, -25.4160060345, -25.4577742887, -25.4744815904, >>>> -25.5078961937, -25.5078961937, -25.5078961937, -25.5747254004, >>>> -25.6499082579, -25.6499082579, -25.6666155595, -25.6666155595, >>>> -25.6833228612, -25.7334447662, -25.8169812746, -25.8503958779, >>>> -25.8503958779, -25.8587495287, -25.8671031796, -25.9339323863, >>>> -25.9506396879, -26.0675907996, -26.0759444504, -26.0759444504, >>>> -26.2263101655, -26.2430174671, -26.301493023, -26.4435050872, >>>> -26.5604561988, -26.5855171513, -26.6439927072, -26.7191755647, >>>> -26.7609438189, -26.8611876289, -26.9614314389, -26.9781387406, >>>> -27.0199069947, -27.0616752489, -27.0700288998, -27.0867362014, >>>> -27.0950898523, -27.1452117573, -27.1702727098, -27.2621628689, >>>> -27.362406679, -27.4542968381, -27.5211260448, -27.5294796957, >>>> -27.5796016007, -27.5963089023, -27.613016204, -27.6213698548, >>>> -27.6297235057, -27.6714917598, -27.6714917598, -27.8051501732, >>>> -27.8719793799, -28.0223450949, -28.0641133491, -28.0975279524, >>>> -28.2228327149, -28.2980155724, -28.3147228741, -28.3230765249, >>>> -28.3899057316, -28.3982593824, -28.4567349383, -28.4817958908, >>>> -28.5319177958, -28.5653323991, -28.6572225583, -28.6572225583, >>>> -28.6822835108, -28.77417367, -28.7825273208, -28.8493565275, >>>> -28.924539385, -28.9579539884, -28.9579539884, -29.1918562117, >>>> -29.3004536726, -29.3255146251, -29.3422219267, -29.4424657368, >>>> -29.4675266893, -29.5092949434, -29.5761241501, -29.5928314518, >>>> -29.6095387535, -29.6513070076, -29.684721611, -29.7264898651, >>>> -29.7766117701, -29.8267336752, -29.8434409768, -29.8434409768, >>>> -30.035574946, -30.035574946, -30.0439285969, -30.0689895494, >>>> -30.2110016135, -30.2444162169, -30.3195990744, -30.4365501861, >>>> -30.4449038369, -30.4950257419, -30.5284403453, -30.5451476469, >>>> -30.5702085994, -30.5952695519, -30.6119768536, -30.6286841553, >>>> -30.6537451078, -30.6704524094, -30.7205743145, -30.7289279653, >>>> -30.845879077, -30.8792936803, -31.0630739987, -31.0714276495, >>>> -31.1299032053, -31.3303908254, -31.4306346354, -31.4389882862, >>>> -31.5141711437, -31.6561832079, -31.6812441604, -31.6812441604, >>>> -32.5834384506, -32.6753286097, -32.7672187689, -32.8256943248, >>>> -32.9175844839, -33.0763038498, -33.1264257548, -33.2350232157, >>>> -33.2767914698, -33.3853889307, -33.3853889307, -33.5608155982, >>>> -33.7195349641, -33.8615470282, -33.8699006791, -33.8866079807, >>>> -33.9701444891), .Dim = c(200L, 2L)), data = c(70.347798763, >>>> 76.2305279637, 56.7935202123, 91.2747172018, 36.6574714598, >>>> 74.5676848522, >>>> 80.45544927, 63.7985345319, 98.6062126882, 76.1440959745, 78.0342424622, >>>> 103.211817252, 103.469894054, 101.543807018, 113.430501289, >>>> 96.7287932817, >>>> 119.081813263, 119.357653249, 91.6231191698, 101.038405309, >>>> 123.946425703, >>>> 127.264845138, 129.378943162, 101.547939554, 90.634442506, >>>> 95.7125681057, >>>> 94.798698159, 136.088211184, 83.1631121458, 105.907150692, >>>> 134.683969664, >>>> 120.236176625, 93.2955838471, 135.734205354, 134.793068333, >>>> 146.056344048, >>>> 156.071027198, 118.33813258, 176.165459485, 178.101033469, >>>> 141.733992676, >>>> 68.1513901758, 85.906096787, 160.683155231, 106.672886988, >>>> 153.732695652, >>>> 126.282924957, 71.171101282, 46.3063932604, 94.8066154966, >>>> 79.6077933159, >>>> 66.3022759053, 53.8001871156, 81.8971879578, 75.2725941278, >>>> 76.3895591688, >>>> 78.1481792228, 84.0088538824, 79.2339837253, 95.3285594814, >>>> 94.9048569376, >>>> 79.7371402431, 94.8361107261, 75.6734008995, 56.8583003566, >>>> 93.9377590667, >>>> 114.925013457, 58.7902731855, 63.3651704313, 87.7623028428, >>>> 138.243923048, >>>> 174.14679455, 176.924595279, 155.265108276, 130.599056347, >>>> 197.328254597, >>>> 43.1264520732, 221.751423068, 226.374023855, 231.22155796, >>>> 242.436720324, >>>> 225.68515641, 211.182339909, 30.7901884494, 268.635407904, >>>> 66.9770777548, >>>> 43.4651532814, 69.6985834637, 162.069565781, 201.17261197, >>>> 206.563852105, >>>> 197.412358817, 200.569088904, 36.5304326403, 134.230440546, >>>> 68.9447508778, >>>> 77.3092256209, 179.991789488, 46.7833022819, 178.837329648, >>>> 67.6041455343, >>>> 61.735642779, 30.9986850158, 136.367462528, 40.2420355711, >>>> 74.1549949452, >>>> 125.706398802, 182.134045089, 183.669620178, 179.176911697, >>>> 96.3292516587, >>>> 71.0485302772, 177.743449233, 187.3998893, 179.464856475, 177.962224878, >>>> 72.9460880655, 52.2011730864, 109.173183978, 129.8967523, 121.836259287, >>>> 147.025253713, 152.031441102, 124.646779658, 100.076387829, >>>> 133.011240036, >>>> 120.939828964, 64.3940362035, 114.996266127, 89.786038347, >>>> 118.008543994, >>>> 84.0282040216, 119.041263465, 44.0234944515, 41.8771712674, >>>> 48.2651512851, >>>> 73.4117997844, 87.9741710827, 142.802105252, 186.596945575, >>>> 228.955878351, >>>> 39.727571142, 59.5842097784, 237.196336949, 244.661132149, >>>> 111.885460631, >>>> 172.978503244, 31.9606458285, 262.185785391, 142.37968626, >>>> 71.9893685188, >>>> 261.41293353, 32.6187049615, 61.3911365603, 33.9234837497, >>>> 194.293576816, >>>> 59.9894516509, 40.4781868063, 136.131434893, 43.8955202545, >>>> 166.161274956, >>>> 101.623813429, 160.809302111, 39.3742142225, 134.084319986, >>>> 65.9772777256, >>>> 119.705366465, 27.1765413035, 90.2079815554, 41.3695883805, >>>> 32.9317985059, >>>> 61.9912390925, 53.1711001574, 53.8325000656, 52.2218112743, >>>> 49.1524441926, >>>> 62.6024730954, 67.1897383436, 66.0508521024, 69.425604381, 69.874832943, >>>> 73.9822629935, 75.6320803161, 98.9442750104, 108.632920447, >>>> 111.310932929, >>>> 117.076441302, 119.455819615, 125.059523339, 117.758307932, >>>> 117.561279774, >>>> 90.3341173828, 113.921889667, 67.6379513723, 99.9119129685, >>>> 58.2801014975, >>>> 63.4483498456, 66.4356707327, 57.6791519282, 62.3488331665), >>>> borders = structure(list(coords.x1 = c(-52.0676766304347, >>>> -52.0039483695653, -51.956152173913, -51.7968315217391, >>>> -51.6853070652174, >>>> -51.6853070652174, -51.5419184782609, -51.4144619565217, >>>> -51.2710733695652, -51.1914130434782, -51.0002282608695, >>>> -50.8090434782609, -50.7134510869565, -50.6178586956521, >>>> -50.5541304347826, -50.5541304347826, -50.5700624999999, >>>> -50.4744701086956, -50.4585380434783, -50.410741847826, >>>> -50.4426059782608, >>>> -50.4426059782608, -50.4585380434783, -50.3310815217391, >>>> -50.2195570652173, -50.0283722826087, -49.8531195652174, >>>> -49.6619347826086, -49.47075, -49.3751576086956, -49.2636331521738, >>>> -49.1202445652174, -48.9768559782608, -48.8971956521739, >>>> -48.8334673913043, -48.7060108695651, -48.6422826086957, >>>> -48.658214673913, -48.4670298913043, -48.3077092391304, >>>> -48.1643206521739, >>>> -48.0368641304347, -48.0687282608695, -48.0209320652174, >>>> -48.0368641304347, -47.9890679347826, -47.9253396739131, >>>> -47.8297472826087, -47.7341548913043, -47.7341548913043, >>>> -47.6066983695652, -47.4951739130435, -47.4633097826087, >>>> -47.4473777173913, -47.3039891304348, -47.2083967391304, >>>> -47.0331440217391, -46.9216195652173, -46.7304347826087, >>>> -46.5073858695652, -46.2524728260869, -45.9816277173912, >>>> -45.7426467391305, -45.4558695652173, -45.2965489130434, >>>> -45.1212961956522, -44.9619755434783, -44.8504510869565, >>>> 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>>>> -27.7560190217391, -27.915339673913, -28.0905923913044, >>>> -28.2499130434783, -28.3933016304348, -28.5844864130434, >>>> -28.7119429347826, -28.7119429347826, -28.807535326087, >>>> -28.9031277173913, -29.0943124999999, -29.3014293478261, >>>> -29.4766820652174, -29.7475271739131, -29.9865081521739, >>>> -30.2254891304348, -30.3688777173912, -30.5122663043478, >>>> -30.5122663043478, -30.6078586956521, -30.6875190217391, >>>> -30.7353152173913, -30.7353152173913, -30.7831114130435, >>>> -30.9105679347826, -31.0380244565217, -31.2292092391304, >>>> -31.4044619565217, -31.6115788043478, -31.8346277173912, >>>> -32.1054728260869, -32.3125896739131, -32.4878423913044, >>>> -32.6949592391304, -32.8861440217391, -33.125125, >>>> -33.3003777173913, >>>> -33.4915624999999, -33.6030869565217, -33.6827472826087, >>>> -33.6986793478261, -33.7942717391304, -33.7942717391304, >>>> -33.9057961956522, -33.9057961956522, -34.0173206521739, >>>> -34.0969809782609, -34.1607092391304, -34.2881657608695, >>>> -34.4474864130434, -34.4793505434782, -34.511214673913 >>>> )), .Names = c("coords.x1", "coords.x2"), class = >>>> "data.frame", row.names = c(NA, >>>> -181L))), .Names = c("coords", "data", "borders"), class = >>>> "geodata") >>>> >>>> >>>> ## Matrix com os chutes iniciais de Sigma e Phi >>>> ini.fit <- expand.grid((2000:5000), (0:12)/12) >>>> >>>> ## Modelo >>>> mod.gau <- likfit(dados.geo, ini.cov.pars = ini.fit, cov.model = >>>> "gau", trend = "cte", fix.nugget = FALSE, >>>> fix.lambda = FALSE, lik.method = "ML") >>>> >>>> ### Identificação do problema resultante! >>>> >>>> kappa not used for the gaussian correlation function >>>> likfit: searching for best initial value ... selected values: >>>> >>>> sigmasq phi tausq kappa lambda psiR >>>> initial.value "2952.00" " 0.83" " 0.00" " 0.50" " 1.00" " >>>> 1.00" >>>> status "est" "est" "est" "fix" "est" "fix" >>>> psiA >>>> initial.value " 0.00" >>>> status "fix" >>>> likelihood value: -319.704123974414 >>>> --------------------------------------------------------------- >>>> likfit: likelihood maximisation using the function optim. >>>> likfit: Use control() to pass additional >>>> arguments for the maximisation function. >>>> For further details see documentation for optim. >>>> likfit: It is highly advisable to run this function several >>>> times with different initial values for the parameters. >>>> likfit: WARNING: This step can be time demanding! >>>> --------------------------------------------------------------- >>>> trying another decomposition (svd) >>>> Erro em solve.default(v$varcov, xmat) : >>>> sistema é computacionalmente singular: condição recíproca número = >>>> 6.36899e-20 >>>> >>>> >>>> _______________ >>>> Rodrigo Sant'Ana >>>> Oceanógrafo >>>> >>>> >>>> Em 16 de setembro de 2012 18:05, Rodrigo Sant'Ana >>>> <[email protected]> escreveu: >>>>> >>>>> >>>>> Caros, >>>>> >>>>> estou tentando ajustar um modelo gaussiano aos de biomassa de peixe >>>>> (kg), e estou caindo em um problema que não consigo identificar o >>>>> motivo! >>>>> Abaixo segue o CMR, bem como o Erro resultante do procedimento. >>>>> >>>>> Obrigado. >>>>> >>>>> dados <- structure(list(coords = structure(c(-42.2064355778, >>>>> -42.3032008959, >>>>> -41.5452059035, -42.1580529187, -41.1420170777, -42.9724943467, >>>>> -41.7306727634, -43.2224714187, -42.601560627, -43.0773234414, >>>>> -43.0692596649, -42.61768818, -42.6499432861, -42.7708999338, >>>>> -42.6338157331, -43.0128132293, -42.2951371194, -42.319328449, >>>>> -41.7387365399, -43.0692596649, -42.3032008959, -42.3596473316, >>>>> -42.4402850967, -43.4643847142, -43.6579153505, -43.7304893392, >>>>> -43.8272546573, -42.7547723808, -44.2143159301, -43.7224255627, >>>>> -43.117642324, -43.4724484907, -44.3272088013, -43.7466168922, >>>>> -43.4160020551, -44.149805718, -43.8595097634, -44.6175047559, >>>>> -44.1981883771, -44.2143159301, -44.6497598619, -45.0690762407, >>>>> -44.9884384756, -44.5368669907, -44.8836093809, 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-25.796375, >>>>> -25.8760353260869, >>>>> -25.8441711956521, -25.9716277173912, -26.0353559782608, >>>>> -26.1309483695651, -26.1787445652174, -26.5133179347825, >>>>> -26.8000951086956, -27.0072119565218, -27.1346684782609, >>>>> -27.182464673913, -27.2461929347825, -27.3417853260869, >>>>> -27.4214456521739, -27.4851739130435, -27.5807663043478, >>>>> -27.7560190217391, -27.915339673913, -28.0905923913044, >>>>> -28.2499130434783, -28.3933016304348, -28.5844864130434, >>>>> -28.7119429347826, -28.7119429347826, -28.807535326087, >>>>> -28.9031277173913, -29.0943124999999, -29.3014293478261, >>>>> -29.4766820652174, -29.7475271739131, -29.9865081521739, >>>>> -30.2254891304348, -30.3688777173912, -30.5122663043478, >>>>> -30.5122663043478, -30.6078586956521, -30.6875190217391, >>>>> -30.7353152173913, -30.7353152173913, -30.7831114130435, >>>>> -30.9105679347826, -31.0380244565217, -31.2292092391304, >>>>> -31.4044619565217, -31.6115788043478, -31.8346277173912, >>>>> -32.1054728260869, -32.3125896739131, -32.4878423913044, >>>>> -32.6949592391304, -32.8861440217391, -33.125125, >>>>> -33.3003777173913, >>>>> -33.4915624999999, -33.6030869565217, -33.6827472826087, >>>>> -33.6986793478261, -33.7942717391304, -33.7942717391304, >>>>> -33.9057961956522, -33.9057961956522, -34.0173206521739, >>>>> -34.0969809782609, -34.1607092391304, -34.2881657608695, >>>>> -34.4474864130434, -34.4793505434782, -34.511214673913 >>>>> )), .Names = c("coords.x1", "coords.x2"), class = >>>>> "data.frame", row.names = c(NA, >>>>> -181L))), .Names = c("coords", "data", "borders"), class = >>>>> "geodata") >>>>> >>>>> >>>>> ini.fit <- ini.fit <- expand.grid((2000:5000), (0:12)/12) >>>>> >>>>> mod.gau <- likfit(dados.geo, ini.cov.pars = ini.fit, cov.model = >>>>> "gau", trend = "cte", fix.nugget = FALSE, >>>>> fix.lambda = FALSE, lik.method = "ML") >>>>> >>>>> >>>>> kappa not used for the gaussian correlation function >>>>> likfit: searching for best initial value ... selected values: >>>>> sigmasq phi tausq kappa lambda psiR >>>>> initial.value "2952.00" " 0.83" " 0.00" " 0.50" " 1.00" " >>>>> 1.00" >>>>> status "est" "est" "est" "fix" "est" "fix" >>>>> psiA >>>>> initial.value " 0.00" >>>>> status "fix" >>>>> likelihood value: -319.704123974414 >>>>> --------------------------------------------------------------- >>>>> likfit: likelihood maximisation using the function optim. >>>>> likfit: Use control() to pass additional >>>>> arguments for the maximisation function. >>>>> For further details see documentation for optim. >>>>> likfit: It is highly advisable to run this function several >>>>> times with different initial values for the parameters. >>>>> likfit: WARNING: This step can be time demanding! >>>>> --------------------------------------------------------------- >>>>> trying another decomposition (svd) >>>>> Erro em solve.default(v$varcov, xmat) : >>>>> sistema é computacionalmente singular: condição recíproca número = >>>>> 6.36899e-20 >>>>> >>>>> >>>>> _______________ >>>>> Rodrigo Sant'Ana >>>>> Oceanógrafo >>>> >>>> >>>> _______________________________________________ >>>> R-br mailing list >>>> [email protected] >>>> https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br >>>> Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça >>>> código mínimo reproduzível. >>> >>> >>> >>> _______________________________________________ >>> R-br mailing list >>> [email protected] >>> https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br >>> Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça >>> código >>> mínimo reproduzível. >> >> _______________________________________________ >> R-br mailing list >> [email protected] >> https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br >> Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça >> código mínimo reproduzível. > > > _______________________________________________ > R-br mailing list > [email protected] > https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br > Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código > mínimo reproduzível. _______________________________________________ R-br mailing list [email protected] https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forneça código mínimo reproduzível.
