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,
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74.9284080818, 137.771352549, 124.913721538, 160.875499767, 138.782910412,
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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,
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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,
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-51.7808994565218, -51.9083559782608, -52.0676766304347), coords.x2 =
c(-34.511214673913,
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)), .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
