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 <paulojus@leg.ufpr.br> 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$varcov, xmat) : sistema é computacionalmente singular: condição recíproca número = 6.36899e-20
Função utilizada: geoR::likfit
Dúvida: Busquei como solução, passar pelo argumento control da função geoR::likfit algumas mundanças na optimização do modelo, tais como, control = list(method = "CG"); Mas não obtive exito. Rodando outros modelos que não o Gaussiano para a mesma base de dados (ex: Exponencial, Matern, Esférico) o problema não ocorre. Teria como passar uma modificação para matrix svd?? Acho que é ai onde esta ocorrendo o Erro, não?
Código Minimo Reproduzível
## Carrega o pacote geoR library(geoR)
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## Matrix com os chutes iniciais de Sigma e Phi ini.fit <- expand.grid((2000:5000), (0:12)/12)
## Modelo mod.gau <- likfit(dados.geo, ini.cov.pars = ini.fit, cov.model = "gau", trend = "cte", fix.nugget = FALSE, fix.lambda = FALSE, lik.method = "ML")
### Identificação do problema resultante!
kappa not used for the gaussian correlation function likfit: searching for best initial value ... selected values:
sigmasq phi tausq kappa lambda psiR initial.value "2952.00" " 0.83" " 0.00" " 0.50" " 1.00" " 1.00" status "est" "est" "est" "fix" "est" "fix" psiA initial.value " 0.00" status "fix" likelihood value: -319.704123974414 --------------------------------------------------------------- likfit: likelihood maximisation using the function optim. likfit: Use control() to pass additional arguments for the maximisation function. For further details see documentation for optim. likfit: It is highly advisable to run this function several times with different initial values for the parameters. likfit: WARNING: This step can be time demanding! --------------------------------------------------------------- trying another decomposition (svd) Erro em solve.default(v$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 <rodrigo.gringo@gmail.com> 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, -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, 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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 R-br@listas.c3sl.ufpr.br 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 R-br@listas.c3sl.ufpr.br 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 R-br@listas.c3sl.ufpr.br 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.