Erro em solve.default(v$varcov, xmat) GeoR likfit
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, 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-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
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, 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-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, -28.8393994565217, -28.648214673913, -28.4092336956522, -28.281777173913, -28.1224565217391, -28.0268641304347, -27.8834755434783, -27.7082228260869, -27.5966983695652, -27.3895815217391, -27.2302608695651, -27.0709402173912, 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-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 <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$varcov, xmat) : sistema é computacionalmente singular: condição recíproca número = 6.36899e-20
_______________ Rodrigo Sant'Ana Oceanógrafo
Não tentei reproduzir seu exemplo mas suas informações dão uma pista do problema O modelo Gaussiano de função de correlação é muito suave na origem. A consequyencioa disto é q de que facilmente pode-se gerar inderteminações numéricas com este modelo. Isto ocorre quando o parametro de nugget está em zero aliado a coordenadas muito proximas de pontos. A suavidade da função acaba gerando linhas/colunas na matrix de correlação não distinguiveis nomericamente e daí a singularidade (numérica) Este fato é bem conhecido e por isto não se recomenda o uso do Gaussiano, ou se recomenda combinado com algum nugget (mas ai somente para predicao) Se quiser obter algo semelhante ao gaussiano (por que usar este modelo tb é uma pergunta relevante - o fato de que "todo mundo usa" nao é resposta válida) sugiro usar o Matern com um valor mais elevado de kappa (4.5 ou superior. On Mon, 17 Sep 2012, Rodrigo Sant'Ana wrote:
Pessoal,
no email enviando anteriormente existiam alguns erros no CMR, bem como a identificação do problema estava muito vaga. Seguem as correções no mesmo para que seja possível reproduzi-lo. Desculpem-me pelos erros anteriores.
Identificação do problema: Erro em solve.default(v$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, 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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.
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)
## 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, 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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.
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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.
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)
## 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, 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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.
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 <paulojus@leg.ufpr.br> 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 <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)
## 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, 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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.
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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.
_______________________________________________ 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.
participantes (2)
-
Paulo Justiniano -
Rodrigo Sant'Ana