Caros, estou tentando ajustar um modelo geoestatístico aos meus dados com auxilio da função likfit, presente no pacote geoR, porém a mesma esta ajustando os parâmetros do modelo muito longe (fora) da distribuição observada no variograma dos dados. Achando que poderia ser alguma influência dos valores iniciais, rodei um ajuste com base na minimização dos quadrados, que ajustou bem os parâmetros a distribuição dos dados. Utilizei os valores do ajuste por minimos quadrados como valores iniciais da função de verossimilhança, e ainda sim não ajustou bem! Gostaria da ajuda de vocês para tentar entender o que esta acontecendo. Outro possível resposta que pensei, seria a transformação dos meus dados, pois os mesmos possuem uma certa assimetria. Mas a dúvida que me resultaria, se eu utiliza-se uma transformação de box-cox, como retornaria os valores ao seu normal após a modelagem e predição! []'s CMR temp <- structure(list(x = c(-42.2225631308, -41.4000579262, -41.8113105285, -42.7386448278, -41.2145910664, -43.101514771, -41.2629737255, -43.2869816308, -41.803246752, -42.569305521, -41.9322671762, -42.9966856763, -43.0128132293, -42.7708999338, -41.7790554224, -44.2949536952, -43.4563209376, -43.4805122672, -42.8757290285, -44.1901246005, -43.5692138089, -44.2143159301, -44.5449307672, -44.7465251801, -43.5530862558, -44.4884843316, -43.9885301877, -43.6014689149, -44.1901246005, -44.7949078392, -45.3996910778, -44.8029716157, -45.0932675703, -45.5287115021, -46.0044743165, -45.762561021, -46.6576402142, -46.7060228733, -45.6980508089, -46.3108978241, -46.6011937786, -45.5125839491, -46.6657039908, -46.9882550514, -45.8512625627, -46.1254309642, -46.1496222938, -46.1576860703, -47.1979132408, -46.7624693089, -47.1495305817, -47.0043826044, -47.1575943582, -47.6414209491, -47.9397806802, -47.7623775969, -47.7543138203, -47.0043826044, -47.1172754756, -47.7946327029, -47.076956593, -47.0850203696, -47.7139949378, -47.1092116991, -47.5365918544, -47.1817856877, -48.0446097749, -47.1656581347, -47.9720357863, -48.1494388696, -48.1494388696, -47.9397806802, -48.1575026461, -48.907433862, -49.0364542863, -48.3832883885, -47.9478444567, -49.0041991802, -48.9800078507, -48.9558165211, -47.9881633393, -48.1575026461, -48.9880716272, -48.4558623772, -49.7380028431, -49.7057477371, -49.9315334795, -50.0605539037, -50.0928090098, -50.3831049643, -50.1250641159, -50.1089365628, -50.3185947522, -50.2944034227, -50.4798702825, -50.9959519795, -50.8346764492, -51.6652454303, -51.7055643129, -51.7297556424), y = c(-23.2774714209, -23.3025323734, -23.4194834851, -23.4278371359, -23.4445444376, -23.4445444376, -23.4696053901, -23.4863126917, -23.5030199934, -23.5030199934, -23.5531418984, -23.6951539626, -23.7035076134, -23.7703368201, -23.8037514234, -23.8455196776, -23.8872879318, -23.8956415826, -23.9039952335, -23.9123488843, -23.9457634876, -24.1044828535, 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-31.3805127304, -31.4222809846, -31.4389882862, -31.7647806688, -31.8733781296, -31.9986828921, -32.0905730513, -32.2743533697, -32.3495362272, -32.8925235314, -33.519047344, -33.6527057574, -34.0119127433, -34.1455711566, -34.3042905225), biomass = c(83.0961332806, 50.7211614516, 87.2964904063, 94.9368757858, 43.9927789353, 77.2411555034, 48.3943067415, 70.9457447384, 93.3226415451, 111.558449875, 105.120137803, 121.774317746, 121.984738595, 134.194524295, 92.3472981727, 74.0563409989, 118.991685107, 118.913371773, 137.446764012, 95.1474633741, 121.714698897, 134.59315967, 101.797584806, 74.9284080818, 137.771352549, 124.913721538, 160.875499767, 138.782910412, 168.972311629, 120.529672382, 43.8330352522, 111.603138446, 82.6283167931, 102.710993825, 129.466779012, 137.200989394, 71.9554182061, 70.8317598018, 142.071147662, 151.190668965, 99.8896044376, 115.837487832, 118.971176291, 51.6652301103, 193.282510478, 250.005205316, 253.408049368, 250.625852379, 67.7379862984, 216.506386439, 124.554150793, 163.227533825, 137.090380572, 50.1620052836, 28.8243753442, 58.1501629663, 67.1120039497, 177.207650243, 189.334832544, 65.2785306408, 182.688259577, 177.288638618, 90.8691963808, 145.065202159, 127.324867544, 142.685381792, 64.4063001299, 131.548465882, 156.065568878, 169.403052205, 205.245173124, 228.321987613, 242.44647784, 40.1581317266, 43.3583613984, 197.084041126, 214.893149963, 54.8643340973, 66.3804698828, 70.0325520737, 163.330903327, 179.736007179, 70.2135583494, 123.133087815, 53.2497546853, 63.4470098487, 65.9584005227, 62.0232097497, 77.2506169312, 61.9183653079, 82.7548476928, 83.9715064102, 87.0964042733, 91.4666549661, 119.213889345, 86.1920719644, 89.9531669855, 55.7705625113, 52.9182431297, 47.4292707838)), .Names = c("x", "y", "biomass" ), row.names = c(NA, -100L), class = "data.frame") bor <- structure(list(coords.x1 = c(-52.0676766304347, -52.0039483695653, -51.956152173913, -51.7968315217391, -51.6853070652174, -51.6853070652174, -51.5419184782609, 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-50.5541304347826, -50.6815869565217, -50.8727717391304, -51.0002282608695, -51.2073451086957, -51.3029375, -51.3348016304348, -51.4463260869564, -51.3507336956521, -51.4941222826087, -51.5737826086956, -51.6215788043478, -51.7808994565218, -51.9083559782608, -52.0676766304347), coords.x2 = c(-34.511214673913, -34.3200298913043, -34.2244375, -34.1607092391304, -34.0651168478261, -33.9217282608695, -33.7464755434783, -33.6349510869565, -33.5074945652174, -33.3800380434783, -33.2685135869565, -33.0932608695652, -33.1410570652174, -33.0613967391304, -32.9020760869565, -32.7427554347826, -32.5675027173913, -32.471910326087, -32.2647934782608, -32.0736086956522, -31.8824239130434, -31.7390353260869, -31.5956467391304, -31.372597826087, -31.1495489130434, -30.9583641304347, -30.7193831521739, -30.5600625, -30.4644701086957, -30.2732853260869, -30.0980326086956, -29.9865081521739, -29.7953233695652, -29.6041385869565, -29.4288858695652, -29.3332934782608, -29.1739728260869, -29.0305842391305, 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-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)) dados.geo <- list() dados.geo$coords <- cbind(temp$long, temp$lat) dados.geo$data <- temp$biomass dados.geo$borders <- bor class(dados.geo) <- "geodata" plot(dados.geo, lowess = TRUE) vario.exp <- variog(dados.geo, uvec = seq(0, 15, l = 100), estimator.type = "classical", max.dist = 15) ## Máxima verossimilhança - Não está convergindo corretamente!!! mod.exp <- likfit(dados.geo, ini.cov.pars = c(2354, 1.5), cov.model = "exp", trend = "cte", fix.nugget = FALSE, nugget = 0.1, lik.method = "ML") ## Minimos quadrados - OK! mod.exp <- variofit(vario.exp, ini = c(3000, 0.3), fix.nugget = FALSE, nugget = 0.1, cov.model = "exp", max.dist = 10) _______________ Rodrigo Sant'Ana Oceanógrafo