Julho 2015 - R-br - DINF UFPR

Re: [R-br] Uso da função melt() RESOLVIDO
by Michelle Bau Graczyk 15 Jul '15

15 Jul '15

Oi Diego, Isso. Era isso e o fato de eu fazer meu file virar uma matriz. Muito obrigada! 2015-07-15 15:02 GMT-03:00 Diego Miro <d.miro1089(a)gmail.com>: > Michelle, tente renomear as colunas do objeto melted_MatrizCor. > colnames(melted_MatrizCor) <- c('Var1', 'Var2', 'value') > > _______________________________________________ > R-br mailing list > R-br(a)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. >

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Explicação de um modelo GLM
by André Lucas de Oliveira Moreira 15 Jul '15

15 Jul '15

Olá pessoal, boa tarde. Tenho uma dúvida que acredito ser facilmente respondida. Como posso falar da explicabilidade de um modelo GLM ou de uma variável? Alguém tem poderia me ajudar? Pergunto isso no geral, mas agora pretendo aplicar isso em um log-linear que analisei com a função glm() distribuição poisson e log-link. Na verdade trata-se de uma análise log-linear, com todas as variáveis categóricas. O output com a função anova(modelo, "chisq") é esse: Analysis of Deviance Table Model: poisson, link: log Response: Freq Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev Pr(>Chi) NULL 11 97.545 Parasitado 1 73.126 10 24.419 < 2.2e-16 *** Idade 2 9.239 8 15.180 0.009859 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Obrigado, André -- *MSc. André Lucas de O. Moreira* http://lattes.cnpq.br/7258065668864153 <http://www.wikiaves.com.br/perfil_andrelukinhas> http://www.wikiaves.com.br/perfil_andrelukinhas 79 8837-3562 79 9132-9093

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Re: [R-br] OR e IC usando comandos clm e clm2
by Luciane Maria Pilotto 15 Jul '15

15 Jul '15

Olá, usei os comandos abaixo! ########################################## load(file.choose())#dataframe:"id3.rda" attach(id3) dput(head(id3,10)) structure(list(regiao = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Norte", "Nordeste", "Sudeste", "Sul", "Centro-Oeste"), class = "factor"), estado = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Rondonia", "Acre", "Amazonas", "Roraima", "Para", "Amapa", "Tocantins", "Maranhao", "Piaui", "Ceara", "Rio Grande Do Norte", "Paraiba", "Pernambuco", "Alagoas", "Sergipe", "Bahia", "Minas Gerais", "Espirito Santo", "Rio De Janeiro", "Sao Paulo", "Parana", "Santa Catarina", "Rio Grande Do Sul", "Mato Grosso Do Sul", "Mato Grosso", "Goias", "Distrito Federal" ), class = "factor"), cod_mun = c(1200401L, 1200401L, 1200401L, 1200401L, 1200401L, 1200401L, 1200401L, 1200401L, 1200401L, 1200401L ), setor = c("05000056", "05000056", "05000056", "05000056", "05000056", "05000056", "05000056", "05000056", "05000062", "05000062" ), cap_int = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Interior", "Capital"), class = "factor"), idade = c(15L, 15L, 17L, 17L, 18L, 18L, 18L, 19L, 19L, 15L), getario = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("5 anos", "12 anos", "15 a 19 anos", "35 a 44 anos", "65 a 74 anos"), class = "factor"), sexo = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L ), .Label = c("Male", "Female"), class = "factor"), grp_etni = structure(c(1L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L), .Label = c("Branca", "Preta", "Amarela", "Parda", "Indigena", "Sem Informacao" ), class = "factor"), quest_01 = c(8L, 2L, 4L, 4L, 3L, 4L, 3L, 3L, 3L, 4L), quest_02 = c(4L, 4L, 2L, 2L, 4L, 1L, 3L, 1L, 3L, 2L), density = c(2, 0.5, 2, 2, 0.75, 4, 1, 3, 1, 2), quest_03 = c(3L, 4L, 6L, 5L, 5L, 3L, 3L, 3L, 6L, 5L), quest_04 = structure(c(2L, 3L, 3L, 4L, NA, 2L, 1L, NA, 3L, 3L), .Label = c("Ate 250", "251 a 500", "501 a 1.500", "1.501 a 2.500", "2.501 a 4.500", "4.501 a 9.500", "Mais de 9.500", "Nao sabe/Nao respondeu" ), class = "factor"), inc_percapita1 = c(46.875, 500, 250, 500, NA, 93.75, 41.6666679382324, NA, 333.333343505859, 250 ), inc_percapita2 = c(46.875, 500, 250, 500, NA, 93.75, 41.6666679382324, NA, 333.333343505859, 250), inc_sqrt1 = c(132.58251953125, 707.106811523438, 500, 1000, NA, 187.5, 72.1687850952148, NA, 577.350280761719, 500), inc_sqrt2 = c(132.58251953125, 707.106811523438, 500, 1000, NA, 187.5, 72.1687850952148, NA, 577.350280761719, 500), q05 = c(11L, 4L, 12L, 6L, 11L, 10L, 7L, 12L, 8L, 7L), quest_06 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Nao", "Sim", "Nao se aplica", "Nao sabe"), class = "factor"), quest_07 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("Nao", "Sim", "Nao se aplica", "Nao sabe"), class = "factor"), quest_08 = c(9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 3L, 9L), quest_09 = structure(c(1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Nao", "Sim", "Nao se aplica", "Nao sabe"), class = "factor"), quest_10 = structure(c(5L, 5L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L), .Label = c("Menos de 1 ano", "1 a 2 anos", ">3 anos", "Outros", "Nïa se aplica", "Nao sabe" ), class = "factor"), quest_11 = structure(c(5L, 5L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L), .Label = c("Publico", "Particular", "Plano de Saude/Convenios", "Outros", "Nao se aplica", "Nao sabe" ), class = "factor"), quest_12 = structure(c(6L, 6L, 2L, 4L, 1L, 3L, 1L, 4L, 2L, 1L), .Label = c("Revisao/Prevencao", "Dor", "Extracao", "Tratamento", "Outros", "Nao se aplica", "Nao sabe"), class = "factor"), quest_13 = structure(c(NA, NA, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L), .Label = c("Muito bom", "Bom", "Regular", "Ruim", "Muito Ruim", "Nao se aplica", "Nao sabe"), class = "factor"), quest_14 = structure(c(3L, 2L, 3L, 4L, 2L, 2L, 4L, 3L, 4L, 2L), .Label = c("Muito satisfeito", "Satisfeito", "Nem satisfeito/insatisfeito", "Insatisfeito", "Muito Insatisfeito", "Nao sabe"), class = "factor"), quest_15 = structure(c(1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Nao", "Sim", "Nao se aplica", "Nao sabe"), class = "factor"), exame = c("1", "1", "1", "1", "1", "1", "1", "1", "1", "1"), cpod = c(3L, 0L, 8L, 3L, 3L, 12L, 1L, 6L, 6L, 3L), p_sang = c(0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L), p_calc = c(0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L), cpi_max = structure(c(1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 2L), .Label = c("Higido", "Sangramento", "Calculo", "Bolsa 4-5 mm", "Bolsa 6 mm ou +", "4", "A", "X" ), class = "factor"), dai = c(21L, 32L, 23L, 21L, 19L, 18L, 17L, 23L, 34L, 25L), trauma = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_), n_higido = c(26L, 28L, 24L, 24L, 25L, 17L, 27L, 25L, 24L, 25L), n_cariado = c(3L, 0L, 6L, 2L, 0L, 2L, 1L, 2L, 4L, 3L), n_restcar = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 1L, 0L), n_restaur = c(0L, 0L, 2L, 0L, 3L, 6L, 0L, 2L, 0L, 0L), n_perdcar = c(0L, 0L, 0L, 1L, 0L, 4L, 0L, 0L, 1L, 0L), n_perdout = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), perdidos = c(0, 0, 0, 1, 0, 4, 0, 0, 1, 0), usaprots = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), usaproti = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Não Usa", "Uma Ponte Fixa", "Mais de 1 PF", "Prótese Parcial Removível", "Prótese Fixa + Removível", "Prótese Total", "Sem Informação" ), class = "factor"), necprots = structure(c(1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Não necessita", "Prótese 1 elemento", "Mais de 1 elemento", "Combinação de próteses", "Prótese Total", "Sem Informação"), class = "factor"), necproti = structure(c(1L, 1L, 4L, 2L, 1L, 3L, 1L, 1L, 2L, 1L), .Label = c("Não necessita", "Prótese 1 elemento", "Mais de 1 elemento", "Combinação de próteses", "Prótese Total", "Sem Informação"), class = "factor"), necprot = structure(c(1L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L, 1L), .Label = c("Não necessita", "Parcial 1 maxilar", "Parcial 2 maxilar", "Total 1 maxilar", "Parcial + Total", "Total 2 maxilar", "Sem Informação"), class = "factor"), oidp = c(0L, 0L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 1L), f1 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1), f2 = c(0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 0.0518900007009506, 0.106059998273849, 0.106059998273849), f3 = c(0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 0.0662299990653992, 0.138889998197556, 0.138889998197556), f = c(0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 0.00343999988399446, 0.0147299999371171, 0.0147299999371171), bwgr_et = c(291.019989013672, 291.019989013672, 291.019989013672, 291.019989013672, 291.019989013672, 291.019989013672, 291.019989013672, 291.019989013672, 67.879997253418, 67.879997253418 ), cluster = c(120040105000056, 120040105000056, 120040105000056, 120040105000056, 120040105000056, 120040105000056, 120040105000056, 120040105000056, 120040105000062, 120040105000062), cluster2 = c("01120308", "01120308", "01120308", "01120308", "01120308", "01120308", "01120308", "01120308", "01120309", "01120309"), getni = c(1L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L), q04 = c(2L, 3L, 3L, 4L, NA, 2L, 1L, NA, 3L, 3L), q06 = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), q07 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L), q08 = c(0, 0, 0, 0, 0, 0, 0, 0, 3, 0), q10 = c(NA, NA, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 3L), q11 = c(NA, NA, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L), q12 = c(NA, NA, 2L, 4L, 1L, 3L, 1L, 4L, 2L, 1L), q13 = structure(c(NA, NA, 2L, 3L, 2L, 3L, 2L, 2L, 2L, 2L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), q14 = c(3L, 2L, 3L, 4L, 2L, 2L, 4L, 3L, 4L, 2L), q15 = c(1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), edcat = c(3, 1, 4, 2, 3, 3, 2, 4, 2, 2), peso = c(291, 291, 291, 291, 291, 291, 291, 291, 68, 68), cariado = c(3L, 0L, 6L, 2L, 0L, 2L, 1L, 4L, 5L, 3L), getar = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), .Names = c("regiao", "estado", "cod_mun", "setor", "cap_int", "idade", "getario", "sexo", "grp_etni", "quest_01", "quest_02", "density", "quest_03", "quest_04", "inc_percapita1", "inc_percapita2", "inc_sqrt1", "inc_sqrt2", "q05", "quest_06", "quest_07", "quest_08", "quest_09", "quest_10", "quest_11", "quest_12", "quest_13", "quest_14", "quest_15", "exame", "cpod", "p_sang", "p_calc", "cpi_max", "dai", "trauma", "n_higido", "n_cariado", "n_restcar", "n_restaur", "n_perdcar", "n_perdout", "perdidos", "usaprots", "usaproti", "necprots", "necproti", "necprot", "oidp", "f1", "f2", "f3", "f", "bwgr_et", "cluster", "cluster2", "getni", "q04", "q06", "q07", "q08", "q10", "q11", "q12", "q13", "q14", "q15", "edcat", "peso", "cariado", "getar"), row.names = c("1241", "1242", "1243", "1244", "1245", "1246", "1247", "1248", "1256", "1268"), class = "data.frame") ################################################################################ id3$q13<-as.factor(id3$q13) m1 <- polr(q13 ~ q11 + q10 + q12 + edcat + q08 + q06 + q14, data=id3, Hess=TRUE) summary(m1) ordinal.or.display(m1) #################################################################################### #Verificando os ajustes do modelo: fm1 <- clm(q13 ~ q11 + q10 + q12 + edcat + q08 + q06 + q14, data=id3) summary(fm1) exp(coef(fm1)) (ci <- confint(fm1)) exp(cbind(OR = coef(fm1), ci)) Obs: o IC não está correto, está usando os coeficientes tb!!! ############################################################################################ nominal_test(fm1)# test partial proportional odds assumption #A variável edcat violou o pressuposto de ordinalidade ##clm2 - partial proprotional odds fm.nom <- clm2(q13 ~ q11 + q10 + q12 + q08 + q06 + q14, data=id3, nominal=~ edcat) summary(fm.nom) exp(coef(fm.nom)) exp(cbind(OR = coef(fm.nom), ci)) Obs: o IC não está correto, está usando os coeficientes tb!!! Obrigada, Luciane __________________________________________________ Luciane Maria Pilotto Mestre e Doutoranda em Saúde Bucal Coletiva - FO/UFRGS NDE Odontologia - UNIVATES Telefone: (51) 84512344 -------------------------------------------- Em qua, 8/7/15, Daniel Marcelino <dmsilva.br(a)gmail.com> escreveu: Assunto: Re: [R-br] OR e IC usando comandos clm e clm2 Para: "R-br Lista" <r-br(a)listas.c3sl.ufpr.br> Data: Quarta-feira, 8 de Julho de 2015, 21:05 Sem exemplo não dá para falar muito. Mas deve haver uma função `confint` para cada função de ajuste, não? Eu não conheço esse pacote, quando preciso ajustar um modelo ordinal, eu uso o a função `` do pacote MASS. modelo <- polr(y ~ x1 + x2, data = dados, Hess=TRUE) summary(modelo); (ci <- confint(modelo)); confint.default(modelo); # ci assumindo normalidade dos dados # Odds Ratios exp(coef(modelo)) # or e ci exp(cbind(or = coef(modelo), ci)) # Tabela (ctable <- coef(summary(modelo))); # calcular e salvar os p values p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2 (ctable <- cbind(ctable, "p value" = p)) Daniel 2015-07-08 20:27 GMT-03:00 Luciane Maria Pilotto <lutipilotto(a)yahoo.com.br>: > Olá pessoal, > > Estou trabalhando com regressão logística ordinal e realizando os ajustes com a função "clm" do pacote ordinal e gostaria de obter o intervalo de confiaça. De acordo com o Tutorial on fitting Cumulative Link Models with the ordinal Package, de Rune Haubo B Christensen (January 21, 2015) é possível rodar o OR, mas não o intervalo de confiaça. O mesmo ocorre com a função "clm2" para partial proportional odds. > > Agradeço qualquer ajuda!! > > > Luciane > > _______________________________________________ > R-br mailing list > R-br(a)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(a)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.

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Bases B-splines em R^2
by David Matta 11 Jul '15

11 Jul '15

Olá pessoal, Alguém sabe me informar se existe no R algum pacote que gera bases B-splines em R^2. Poderia ser também qualquer outra base em R^2, sendo ela ortogonal ou não. Obrigado David

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Variáveis para utilizar no modelo de trend, geoR
by Wagner Wolff 10 Jul '15

10 Jul '15

Olá pessoal da lista! Eu tenho mais de 10 covariáveis que quero testar para a remoção de tendência espacial de um modelo geoestatístico, no qual utilizarei o AIC como índice de escolha do melhor modelo. Gostaria de saber como eu poderia proceder para fazer um filtro nessas covariáveis, eliminando as que não terão peso significativo no modelo, antes de testar o AIC. Sendo assim minha pergunta é: É garantido que as covariáveis que terão maior correlação com a variável a ser interpolada, serão as que representarão no modelo de retirada de tendência o menor AIC? É válido fazer um gráfico Biplot para analisar isso ou a matriz de correlação já seria suficiente? Obrigado pela atenção! Wagner Wolff

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OR e IC usando comandos clm e clm2
by Luciane Maria Pilotto 09 Jul '15

09 Jul '15

Olá pessoal, Estou trabalhando com regressão logística ordinal e realizando os ajustes com a função "clm" do pacote ordinal e gostaria de obter o intervalo de confiaça. De acordo com o Tutorial on fitting Cumulative Link Models with the ordinal Package, de Rune Haubo B Christensen (January 21, 2015) é possível rodar o OR, mas não o intervalo de confiaça. O mesmo ocorre com a função "clm2" para partial proportional odds. Agradeço qualquer ajuda!! Luciane

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ProgressBar não aparece ao instalar pacotes via script bat (Windows)!
by Ivan Bezerra Allaman 09 Jul '15

09 Jul '15

Boa noite senhores! Quando eu executo um arquivo .bat no qual invoco um arquivo .R para instalar um determinado pacote, a caixa do Progressbar não aparece. Alguém sabe como contornar isso? No CMR abaixo é preciso criar dois arquivos: RUN.bat e teste.R. Em seguida, execute o arquivo RUN.bat. É apenas para a plataforma Windows! RUN.bat-------------------- @ECHO OFF IF EXIST "%ProgramFiles(x86)%\R" set Path="%programfiles(x86)%\R\R-3.2.1\bin" IF EXIST "%ProgramFiles%\R" set Path="%programfiles%\R\R-3.2.1\bin" SET ROPTS=--no-save --no-environ --no-init-file --no-restore --no-Rconsole %Path%"\Rscript.exe" %ROPTS% teste.R 1> ShinyApp.log 2>&1 teste.R----------------------------- install.packages('lba', repos = "http://nbcgib.uesc.br/mirrors/cran/") Desde já, grato! (s,f,p) Allaman

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Problema om o heatmap
by Michelle Bau Graczyk 07 Jul '15

07 Jul '15

Boa tarde, Estou usando a função heatmap para fazer um grafico da correlação entre 30 empresas. Porém a diagonal toda da matriz é 1, e a cor que aparece não é branco para todas elas. Alguém saberia me dizer o por quê disso? Abaixo segue o código. Muito obrigada, Michelle > if (!require("gplots")) { + install.packages("gplots", dependencies = TRUE) + library(gplots) + } > if (!require("RColorBrewer")) { + install.packages("RColorBrewer", dependencies = TRUE) + library(RColorBrewer) + } > library(gplots) > library(RColorBrewer) > > > file<-read.table("/Users/bau/Análise20%/MatrizCorrelaçãoHoraFixaAn20%2Verificação.txt", head=TRUE) > > matriz<-as.matrix(file) > matriz V1 V2 V3 V4 V5 V6 V7 V8 1 1.000000 -0.1488000 -0.120220 -0.278740 0.812920 -0.153150 -0.246640 -0.0706680 2 -0.148800 1.0000000 -0.094092 -0.291490 -0.026490 -0.084461 -0.274460 -0.0053051 3 -0.120220 -0.0940920 1.000000 -0.038117 -0.156290 0.443590 0.555750 -0.0185260 4 -0.278740 -0.2914900 -0.038117 1.000000 -0.186330 -0.316690 -0.233550 -0.1683500 5 0.812920 -0.0264900 -0.156290 -0.186330 1.000000 -0.179180 -0.240810 -0.0821580 6 -0.153150 -0.0844610 0.443590 -0.316690 -0.179180 1.000000 0.314870 -0.0805790 7 -0.246640 -0.2744600 0.555750 -0.233550 -0.240810 0.314870 1.000000 -0.1081200 8 -0.070668 -0.0053051 -0.018526 -0.168350 -0.082158 -0.080579 -0.108120 1.0000000 9 -0.103610 -0.2866200 -0.138530 -0.415740 -0.240890 -0.147270 -0.217480 0.1628300 10 -0.049682 -0.0473140 -0.051863 -0.214470 -0.060698 -0.118840 -0.111480 0.9682600 11 -0.263810 -0.2668200 -0.218920 -0.481630 -0.244870 -0.240020 -0.111070 -0.1727900 12 -0.188570 -0.0335670 0.602170 -0.189760 -0.223400 0.375510 0.474240 -0.1309300 13 -0.241810 -0.1810300 0.313570 -0.352580 -0.243430 0.235260 0.197230 -0.1601100 14 -0.236110 -0.1327000 0.221720 -0.417150 -0.283360 -0.026711 0.061281 0.2914000 15 -0.196730 -0.2440000 -0.180820 -0.472170 -0.120510 -0.266970 -0.263940 0.4074900 16 -0.242250 -0.2702000 -0.176820 -0.406400 -0.234990 -0.210460 0.025799 -0.1415800 17 -0.210830 -0.2452300 -0.194290 -0.340790 -0.188320 -0.318860 -0.244580 -0.0325100 18 -0.205970 -0.2793200 -0.158450 -0.395590 -0.103040 -0.040617 -0.165250 -0.0957780 19 -0.174340 -0.1529100 0.581590 -0.197270 -0.170600 0.362750 0.411190 -0.0440840 20 -0.236400 -0.3040200 -0.259570 -0.432960 -0.277750 -0.257810 -0.498010 -0.2060400 21 0.627290 -0.1816700 -0.148760 -0.334340 0.625360 -0.200010 -0.211250 -0.0982320 22 0.646400 -0.2604900 -0.107130 -0.347920 0.598150 -0.246790 -0.177750 -0.1006800 23 -0.166960 -0.3303900 -0.170460 -0.480110 -0.134900 -0.297750 -0.256080 0.0181360 24 -0.175720 -0.2457900 -0.334160 -0.475940 -0.279300 -0.476930 -0.122630 -0.1589900 25 -0.239010 -0.2516100 -0.261780 -0.358940 -0.243110 -0.330530 -0.418030 -0.0720400 26 -0.072637 -0.1991100 -0.108510 -0.306400 -0.122220 -0.110840 -0.035140 -0.0188130 27 -0.239730 0.0208830 -0.317810 -0.562200 -0.224260 -0.350740 -0.357450 -0.2385000 28 -0.081665 -0.0172600 -0.057269 -0.187130 -0.089078 -0.092451 -0.107760 0.9782200 29 -0.287560 -0.4044900 -0.222730 -0.402910 -0.246270 -0.374840 -0.170290 -0.2050000 30 -0.240640 -0.2494600 -0.161820 -0.363650 -0.301390 -0.335640 -0.221850 -0.0917310 V9 V10 V11 V12 V13 V14 V15 V16 1 -0.103610 -0.049682 -0.2638100 -0.188570 -0.241810 -0.236110 -0.19673 -0.242250 2 -0.286620 -0.047314 -0.2668200 -0.033567 -0.181030 -0.132700 -0.24400 -0.270200 3 -0.138530 -0.051863 -0.2189200 0.602170 0.313570 0.221720 -0.18082 -0.176820 4 -0.415740 -0.214470 -0.4816300 -0.189760 -0.352580 -0.417150 -0.47217 -0.406400 5 -0.240890 -0.060698 -0.2448700 -0.223400 -0.243430 -0.283360 -0.12051 -0.234990 6 -0.147270 -0.118840 -0.2400200 0.375510 0.235260 -0.026711 -0.26697 -0.210460 7 -0.217480 -0.111480 -0.1110700 0.474240 0.197230 0.061281 -0.26394 0.025799 8 0.162830 0.968260 -0.1727900 -0.130930 -0.160110 0.291400 0.40749 -0.141580 9 1.000000 0.111420 -0.3292500 -0.117090 -0.183380 -0.138610 0.10657 -0.297930 10 0.111420 1.000000 -0.1616400 -0.158130 -0.193640 0.249040 0.38221 -0.167030 11 -0.329250 -0.161640 1.0000000 -0.365690 -0.437690 -0.460970 -0.19613 0.173440 12 -0.117090 -0.158130 -0.3656900 1.000000 0.262660 0.152760 -0.28939 -0.286680 13 -0.183380 -0.193640 -0.4376900 0.262660 1.000000 -0.036207 -0.27265 -0.217780 14 -0.138610 0.249040 -0.4609700 0.152760 -0.036207 1.000000 -0.17618 -0.350990 15 0.106570 0.382210 -0.1961300 -0.289390 -0.272650 -0.176180 1.00000 -0.329490 16 -0.297930 -0.167030 0.1734400 -0.286680 -0.217780 -0.350990 -0.32949 1.000000 17 -0.142130 -0.041754 -0.3445100 -0.241300 -0.370250 -0.241910 -0.12044 -0.399280 18 -0.240860 -0.132840 -0.2431700 -0.205090 -0.395380 -0.246830 -0.26935 -0.405670 19 -0.133520 -0.045900 -0.2048300 0.445090 0.349490 0.098674 -0.12891 -0.145070 20 -0.284450 -0.268370 -0.4866800 -0.490160 -0.413300 -0.534260 -0.39506 -0.440230 21 -0.215960 -0.084613 -0.2693500 -0.224280 -0.053839 -0.298130 -0.19650 -0.249920 22 -0.290980 -0.088546 -0.2989100 -0.280310 -0.335690 -0.178300 -0.27805 -0.286220 23 -0.431420 0.024286 -0.3099400 -0.297010 -0.458870 -0.393310 -0.18978 -0.149720 24 -0.461750 -0.174440 -0.2290000 -0.263640 -0.384700 -0.321700 -0.37221 -0.111130 25 -0.277500 -0.117370 -0.3217100 -0.435670 -0.297100 -0.336510 -0.30911 -0.126820 26 0.028362 -0.038184 -0.0044301 -0.051589 -0.238220 -0.292680 -0.20221 0.068210 27 -0.297810 -0.279020 -0.6358500 -0.315320 -0.461570 -0.565570 -0.50525 -0.525960 28 0.138330 0.964190 -0.1637700 -0.135670 -0.175530 0.290640 0.40377 -0.175950 29 -0.377810 -0.226910 -0.0314590 -0.325840 -0.322050 -0.389640 -0.42031 -0.045736 30 -0.274010 -0.103120 -0.4103000 -0.299110 -0.285560 -0.338200 -0.48234 -0.446340 V17 V18 V19 V20 V21 V22 V23 V24 1 -0.210830 -0.205970 -0.174340 -0.23640 0.627290 0.646400 -0.166960 -0.175720 2 -0.245230 -0.279320 -0.152910 -0.30402 -0.181670 -0.260490 -0.330390 -0.245790 3 -0.194290 -0.158450 0.581590 -0.25957 -0.148760 -0.107130 -0.170460 -0.334160 4 -0.340790 -0.395590 -0.197270 -0.43296 -0.334340 -0.347920 -0.480110 -0.475940 5 -0.188320 -0.103040 -0.170600 -0.27775 0.625360 0.598150 -0.134900 -0.279300 6 -0.318860 -0.040617 0.362750 -0.25781 -0.200010 -0.246790 -0.297750 -0.476930 7 -0.244580 -0.165250 0.411190 -0.49801 -0.211250 -0.177750 -0.256080 -0.122630 8 -0.032510 -0.095778 -0.044084 -0.20604 -0.098232 -0.100680 0.018136 -0.158990 9 -0.142130 -0.240860 -0.133520 -0.28445 -0.215960 -0.290980 -0.431420 -0.461750 10 -0.041754 -0.132840 -0.045900 -0.26837 -0.084613 -0.088546 0.024286 -0.174440 11 -0.344510 -0.243170 -0.204830 -0.48668 -0.269350 -0.298910 -0.309940 -0.229000 12 -0.241300 -0.205090 0.445090 -0.49016 -0.224280 -0.280310 -0.297010 -0.263640 13 -0.370250 -0.395380 0.349490 -0.41330 -0.053839 -0.335690 -0.458870 -0.384700 14 -0.241910 -0.246830 0.098674 -0.53426 -0.298130 -0.178300 -0.393310 -0.321700 15 -0.120440 -0.269350 -0.128910 -0.39506 -0.196500 -0.278050 -0.189780 -0.372210 16 -0.399280 -0.405670 -0.145070 -0.44023 -0.249920 -0.286220 -0.149720 -0.111130 17 1.000000 -0.338590 -0.225910 -0.45329 -0.212720 -0.258960 -0.327150 -0.220570 18 -0.338590 1.000000 -0.123500 -0.35347 -0.206230 -0.234200 -0.321910 -0.225490 19 -0.225910 -0.123500 1.000000 -0.30228 -0.180950 -0.237280 -0.269610 -0.332320 20 -0.453290 -0.353470 -0.302280 1.00000 -0.160310 -0.443100 -0.498960 -0.604740 21 -0.212720 -0.206230 -0.180950 -0.16031 1.000000 0.394940 -0.220940 -0.175960 22 -0.258960 -0.234200 -0.237280 -0.44310 0.394940 1.000000 -0.191920 -0.295690 23 -0.327150 -0.321910 -0.269610 -0.49896 -0.220940 -0.191920 1.000000 -0.140490 24 -0.220570 -0.225490 -0.332320 -0.60474 -0.175960 -0.295690 -0.140490 1.000000 25 -0.357960 -0.439870 -0.245740 -0.45062 -0.296380 -0.284950 -0.336660 -0.372750 26 -0.099695 -0.289810 -0.190800 -0.16041 -0.071261 -0.219200 -0.111650 -0.023106 27 -0.516980 -0.148320 -0.330300 -0.57538 -0.288440 -0.420560 -0.667640 -0.552860 28 -0.029953 -0.068158 -0.037025 -0.21447 -0.102620 -0.114390 -0.020426 -0.182240 29 -0.386590 -0.372130 -0.114870 -0.59123 -0.328560 -0.407050 -0.379740 -0.086991 30 -0.296050 -0.491150 -0.252440 -0.20588 -0.176020 -0.156980 -0.549450 -0.447000 V25 V26 V27 V28 V29 V30 1 -0.23901 -0.0726370 -0.239730 -0.081665 -0.287560 -0.240640 2 -0.25161 -0.1991100 0.020883 -0.017260 -0.404490 -0.249460 3 -0.26178 -0.1085100 -0.317810 -0.057269 -0.222730 -0.161820 4 -0.35894 -0.3064000 -0.562200 -0.187130 -0.402910 -0.363650 5 -0.24311 -0.1222200 -0.224260 -0.089078 -0.246270 -0.301390 6 -0.33053 -0.1108400 -0.350740 -0.092451 -0.374840 -0.335640 7 -0.41803 -0.0351400 -0.357450 -0.107760 -0.170290 -0.221850 8 -0.07204 -0.0188130 -0.238500 0.978220 -0.205000 -0.091731 9 -0.27750 0.0283620 -0.297810 0.138330 -0.377810 -0.274010 10 -0.11737 -0.0381840 -0.279020 0.964190 -0.226910 -0.103120 11 -0.32171 -0.0044301 -0.635850 -0.163770 -0.031459 -0.410300 12 -0.43567 -0.0515890 -0.315320 -0.135670 -0.325840 -0.299110 13 -0.29710 -0.2382200 -0.461570 -0.175530 -0.322050 -0.285560 14 -0.33651 -0.2926800 -0.565570 0.290640 -0.389640 -0.338200 15 -0.30911 -0.2022100 -0.505250 0.403770 -0.420310 -0.482340 16 -0.12682 0.0682100 -0.525960 -0.175950 -0.045736 -0.446340 17 -0.35796 -0.0996950 -0.516980 -0.029953 -0.386590 -0.296050 18 -0.43987 -0.2898100 -0.148320 -0.068158 -0.372130 -0.491150 19 -0.24574 -0.1908000 -0.330300 -0.037025 -0.114870 -0.252440 20 -0.45062 -0.1604100 -0.575380 -0.214470 -0.591230 -0.205880 21 -0.29638 -0.0712610 -0.288440 -0.102620 -0.328560 -0.176020 22 -0.28495 -0.2192000 -0.420560 -0.114390 -0.407050 -0.156980 23 -0.33666 -0.1116500 -0.667640 -0.020426 -0.379740 -0.549450 24 -0.37275 -0.0231060 -0.552860 -0.182240 -0.086991 -0.447000 25 1.00000 -0.3001800 -0.582360 -0.103130 -0.591400 -0.361260 26 -0.30018 1.0000000 -0.324240 -0.051578 -0.085934 -0.274650 27 -0.58236 -0.3242400 1.000000 -0.236760 -0.719200 -0.605090 28 -0.10313 -0.0515780 -0.236760 1.000000 -0.215050 -0.104520 29 -0.59140 -0.0859340 -0.719200 -0.215050 1.000000 -0.539620 30 -0.36126 -0.2746500 -0.605090 -0.104520 -0.539620 1.000000 > rownames(matriz)<-c("AA","AIG","AXP","BA","CAT","C","DD","DIS","GE","GM","HD","HON","HPQ","IBM","INTC","JNJ","JPM","KO","MCD","MMM","MO","MRK","MSFT","PFE","PG","SBC","UTX","VZ","WMT","XOM") > colnames(matriz)<-c("AA","AIG","AXP","BA","CAT","C","DD","DIS","GE","GM","HD","HON","HPQ","IBM","INTC","JNJ","JPM","KO","MCD","MMM","MO","MRK","MSFT","PFE","PG","SBC","UTX","VZ","WMT","XOM") > > > my_palette <- colorRampPalette(c( "dark blue", "violet","white"))(n = 299) > png("/Users/bau/Grafico de calor/HeatMapCorrViolet2.jpeg", + width = 5*300, + height = 5*300, + res = 300, + pointsize = 8) > > heatmap(matriz,Rowv=NA,Colv=NA,col=my_palette,scale="column",margins=c(5,10),main="Correlação_HoraFixa_2") > dev.off() [image: Imagem inline 1]

2 1

legend.krige
by Hélio Gallo Rocha 07 Jul '15

07 Jul '15

Caros Listeiros, Estou tentando melhorar a visualização de um mapa de krigagem. Uma delas é ao plotar a legenda. Utilizei scale.vals para que os valores representados na legenda fossem os mesmos indicados pelo contorno ( contour). A dificuldade é fazer com que os valores fiquem justamente na divisa das cores, pois ficam sempre deslocados. Quem puder e tiver paciência para dar uma olhada https://drive.google.com/file/d/0ByCL48wb7T7Wek5Ud0g4bkgtWTQ/view?usp=shari… Agradeço desde já -- Hélio Gallo Rocha IFSULDEMINAS - Câmpus Muzambinho

1 0

Salvar grafico em jpeg
by Michelle Bau Graczyk 06 Jul '15

06 Jul '15

Bom dia, Estou com problemas em salvar gráficos em jpeg quando crio um laço. Ele não salva e não mostra mensagem alguma ou não salva e mostra uma mensagem quartz off screen. Quando uso o mesmo código para fazer apenas um gráfico e salvar ele não dá erro nenhum. Alguém, por favor, poderia me ajudar? Segue o código e os dados. library(gplots) library(RColorBrewer) require(lattice) for(i in 1:2){ file<-read.table(paste0("/Users/bau/Análise20%/MatrizCorrelaçãoHoraFixaAn20%",i,".txt"), head=TRUE) matriz<-as.matrix(file) rownames(matriz)<-c("AA","AIG","AXP","BA","CAT","C","DD","DIS","GE","GM","HD","HON","HPQ","IBM","INTC","JNJ","JPM","KO","MCD","MMM","MO","MRK","MSFT","PFE","PG","SBC","UTX","VZ","WMT","XOM") colnames(matriz)<-c("AA","AIG","AXP","BA","CAT","C","DD","DIS","GE","GM","HD","HON","HPQ","IBM","INTC","JNJ","JPM","KO","MCD","MMM","MO","MRK","MSFT","PFE","PG","SBC","UTX","VZ","WMT","XOM") matriz[1:5,] jpeg(filename = paste0("/Users/bau/Grafico de calor/HeatMapAZUL",i,".jpeg"), width = 480, height = 480, units = "px", pointsize = 12, quality = 75, bg = "white", res = NA) my_palette <- colorRampPalette(c( "dark blue", "violet","white"))(n = 299) png(paste0("/Users/bau/Grafico de calor/HeatMapAZUL",i,".png"), width = 5*300, height = 5*300, res = 300, pointsize = 8) levelplot(matriz, scale=list(x=list(rot=90)),at=seq( -1, 1, length=31), col.regions = my_palette,main= paste0("Correlação_HoraFixa_",i), xlab=NULL, ylab=NULL,pretty=FALSE) dev.off() } > file<-read.table("/Users/bau/Análise20%/MatrizCorrelaçãoHoraFixaAn20%1.txt", head=TRUE) > show(file) V1 V2 V3 V4 V5 V6 1 1.000000000 -0.013426078 -0.21285630 -0.09171000 -0.6231314 -0.20886277 2 -0.013426078 1.000000000 -0.19101532 -0.09269047 -0.3905452 -0.18698576 3 -0.212856304 -0.191015317 1.00000000 0.84406152 -0.3083666 0.97823817 4 -0.091709997 -0.092690470 0.84406152 1.00000000 -0.2213573 0.87796366 5 -0.623131438 -0.390545243 -0.30836662 -0.22135729 1.0000000 -0.31312560 6 -0.208862775 -0.186985764 0.97823817 0.87796366 -0.3131256 1.00000000 7 -0.504637041 -0.448676694 -0.25215668 -0.27538157 -0.5137430 -0.25444848 8 -0.467185960 -0.362268879 -0.30468949 -0.25907553 -0.5013547 -0.29230720 9 -0.497699689 -0.161087276 -0.37263674 -0.33835600 -0.6198943 -0.33038672 10 -0.524100951 -0.211592387 -0.30362088 -0.26835124 -0.5762579 -0.30552595 11 0.399221938 0.005904671 -0.24978163 -0.13225681 -0.5387313 -0.25498370 12 -0.197839618 -0.175675403 0.86442154 0.94829960 -0.2778112 0.87784476 13 -0.148276625 -0.293532154 0.24093457 -0.17666540 -0.4479914 0.21317377 14 -0.497814407 -0.111138707 -0.28168585 -0.20663433 -0.2801427 -0.23085704 15 -0.157977692 -0.149024426 0.86660398 0.95972443 -0.2446824 0.89157375 16 0.033288026 -0.356618173 -0.21621336 -0.19274479 -0.5635006 -0.20005142 17 -0.147716160 -0.207117938 0.97197757 0.85486085 -0.3239776 0.96789390 18 -0.045839759 -0.103767464 -0.18429365 -0.10376845 -0.4948730 -0.15166138 19 -0.212427688 -0.172610761 0.83109732 0.93752003 -0.2855651 0.85884265 20 0.495325219 0.220846605 -0.19408500 -0.05542756 -0.5081988 -0.18529317 21 -0.129377709 -0.189529548 0.83420407 0.93311687 -0.2571025 0.86614155 22 -0.279039235 -0.183975458 0.65615423 0.76146699 -0.2503563 0.68106200 23 0.009671092 -0.049383458 -0.04157648 0.06853473 -0.4408066 0.02434647 24 -0.114448903 -0.208502214 -0.25585583 -0.23975124 -0.5945710 -0.24042452 25 0.048300316 -0.050097188 0.82957327 0.95202128 -0.3010931 0.85659214 26 0.012219368 -0.255398346 -0.22028193 -0.20041563 -0.5335980 -0.20681233 27 0.089591597 0.064944658 -0.23856813 -0.08203396 -0.2550432 -0.21823074 28 -0.010193547 -0.260272495 -0.26536430 -0.19707519 -0.2196310 -0.23685789 29 0.257565480 0.155269068 -0.21697827 -0.07923628 -0.3889618 -0.19754306 30 0.122909786 -0.029950799 0.27500640 -0.07517171 -0.2770390 0.24016887 V7 V8 V9 V10 V11 V12 V13 1 -0.5046370 -0.4671860 -0.4976997 -0.5241010 3.992219e-01 -0.19783962 -0.1482766 2 -0.4486767 -0.3622689 -0.1610873 -0.2115924 5.904671e-03 -0.17567540 -0.2935322 3 -0.2521567 -0.3046895 -0.3726367 -0.3036209 -2.497816e-01 0.86442154 0.2409346 4 -0.2753816 -0.2590755 -0.3383560 -0.2683512 -1.322568e-01 0.94829960 -0.1766654 5 -0.5137430 -0.5013547 -0.6198943 -0.5762579 -5.387313e-01 -0.27781123 -0.4479914 6 -0.2544485 -0.2923072 -0.3303867 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0.15526907 -0.02995080 3 -0.23856813 -0.26536430 -0.21697827 0.27500640 4 -0.08203396 -0.19707519 -0.07923628 -0.07517171 5 -0.25504318 -0.21963101 -0.38896184 -0.27703901 6 -0.21823074 -0.23685789 -0.19754306 0.24016887 7 -0.40577431 -0.53559930 -0.34047029 -0.31228959 8 -0.42420522 -0.49514330 -0.51434521 -0.33663030 9 -0.55826844 -0.48949338 -0.38999084 -0.45053721 10 -0.41754979 -0.46481806 -0.45306342 -0.38931940 11 0.07514731 -0.06898829 0.14961950 0.06318255 12 -0.20531502 -0.23715318 -0.18721488 -0.13574861 13 -0.36469841 -0.13123625 -0.30971805 0.73592985 14 -0.24973099 -0.28371225 -0.44201145 -0.27645874 15 -0.17158152 -0.20221546 -0.13952450 -0.10875617 16 -0.41671510 0.16439014 -0.45351557 -0.04471142 17 -0.24691242 -0.19148725 -0.21848464 0.27019707 18 0.11090423 -0.43481448 0.10432716 -0.08632261 19 -0.23768820 -0.25152639 -0.20555802 -0.15581103 20 0.22033866 0.10439147 0.35752458 0.16307280 21 -0.22801837 -0.14745436 -0.19062429 -0.11619095 22 -0.27674657 -0.26775282 -0.34762731 -0.14332761 23 0.10694303 -0.02482201 0.07146488 0.06641018 24 -0.47297675 -0.03472561 -0.51940836 -0.17748601 25 -0.05642694 -0.09708505 -0.02788688 -0.02678411 26 -0.50517628 0.23412068 -0.46386362 -0.16582858 27 1.00000000 -0.24918184 0.37038916 0.04787473 28 -0.24918184 1.00000000 -0.36524071 -0.03518287 29 0.37038916 -0.36524071 1.00000000 0.05381014 30 0.04787473 -0.03518287 0.05381014 1.00000000 > file<-read.table("/Users/bau/Análise20%/MatrizCorrelaçãoHoraFixaAn20%2.txt", head=TRUE) > show(file) V1 V2 V3 V4 V5 V6 1 1.00000000 -0.148802897 -0.12022223 -0.27874441 0.81291519 -0.15314707 2 -0.14880290 1.000000000 -0.09409226 -0.29149169 -0.02648969 -0.08446126 3 -0.12022223 -0.094092262 1.00000000 -0.03811741 -0.15628940 0.44359269 4 -0.27874441 -0.291491692 -0.03811741 1.00000000 -0.18632625 -0.31669284 5 0.81291519 -0.026489690 -0.15628940 -0.18632625 1.00000000 -0.17918216 6 -0.15314707 -0.084461261 0.44359269 -0.31669284 -0.17918216 1.00000000 7 -0.24663544 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-0.22690994 -0.031458750 -0.32584427 30 -0.22184621 -0.091731035 -0.27400955 -0.10312034 -0.410299744 -0.29910598 V13 V14 V15 V16 V17 V18 V19 1 -0.24180551 -0.23610878 -0.1967282 -0.24225031 -0.21083189 -0.20596594 -0.17433828 2 -0.18102524 -0.13269887 -0.2439951 -0.27020364 -0.24523431 -0.27931752 -0.15291382 3 0.31357110 0.22171660 -0.1808179 -0.17681696 -0.19428859 -0.15845109 0.58159345 4 -0.35257546 -0.41715093 -0.4721732 -0.40640368 -0.34079396 -0.39558607 -0.19726939 5 -0.24343062 -0.28336114 -0.1205062 -0.23499450 -0.18832384 -0.10304093 -0.17059660 6 0.23525711 -0.02671104 -0.2669707 -0.21046189 -0.31886018 -0.04061684 0.36274947 7 0.19722655 0.06128069 -0.2639408 0.02579863 -0.24457624 -0.16524670 0.41119310 8 -0.16011424 0.29139685 0.4074907 -0.14158429 -0.03250955 -0.09577788 -0.04408438 9 -0.18338189 -0.13861471 0.1065695 -0.29792794 -0.14213354 -0.24086288 -0.13351615 10 -0.19364346 0.24903670 0.3822130 -0.16702746 -0.04175404 -0.13284008 -0.04590011 11 -0.43769154 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-0.23728301 23 -0.45887289 -0.39331122 -0.1897815 -0.14971863 -0.32715093 -0.32191285 -0.26960818 24 -0.38470376 -0.32169620 -0.3722094 -0.11113094 -0.22057358 -0.22548697 -0.33231501 25 -0.29710022 -0.33650812 -0.3091107 -0.12681911 -0.35795815 -0.43986760 -0.24573947 26 -0.23821696 -0.29267547 -0.2022070 0.06820963 -0.09969466 -0.28980539 -0.19080374 27 -0.46157443 -0.56557322 -0.5052536 -0.52595881 -0.51698055 -0.14831746 -0.33029722 28 -0.17553399 0.29063723 0.4037707 -0.17594595 -0.02995276 -0.06815830 -0.03702463 29 -0.32204504 -0.38963846 -0.4203058 -0.04573567 -0.38658959 -0.37212845 -0.11486707 30 -0.28556026 -0.33820289 -0.4823419 -0.44634143 -0.29604837 -0.49115267 -0.25244406 V20 V21 V22 V23 V24 V25 V26 1 -0.2364000 0.62729054 0.6464021 -0.16696302 -0.17572026 -0.23901217 -0.072636529 2 -0.3040231 -0.18167018 -0.2604907 -0.33038946 -0.24579003 -0.25161154 -0.199105473 3 -0.2595721 -0.14875756 -0.1071279 -0.17045972 -0.33416166 -0.26178381 -0.108510806 4 -0.4329635 -0.33433731 -0.3479210 -0.48010819 -0.47593906 -0.35894334 -0.306395637 5 -0.2777518 0.62535646 0.5981461 -0.13490094 -0.27930048 -0.24311055 -0.122215250 6 -0.2578149 -0.20001058 -0.2467946 -0.29774747 -0.47692840 -0.33052669 -0.110836651 7 -0.4980100 -0.21125269 -0.1777522 -0.25608478 -0.12263087 -0.41803360 -0.035140253 8 -0.2060441 -0.09823194 -0.1006802 0.01813585 -0.15899046 -0.07203959 -0.018813181 9 -0.2844513 -0.21596254 -0.2909822 -0.43142227 -0.46174646 -0.27749502 0.028361782 10 -0.2683692 -0.08461270 -0.0885459 0.02428580 -0.17444194 -0.11736600 -0.038183594 11 -0.4866839 -0.26934702 -0.2989095 -0.30994011 -0.22900337 -0.32170740 -0.004430114 12 -0.4901639 -0.22427553 -0.2803072 -0.29700599 -0.26364499 -0.43566677 -0.051588530 13 -0.4132969 -0.05383935 -0.3356937 -0.45887289 -0.38470376 -0.29710022 -0.238216957 14 -0.5342624 -0.29813229 -0.1783033 -0.39331122 -0.32169620 -0.33650812 -0.292675472 15 -0.3950604 -0.19650260 -0.2780501 -0.18978146 -0.37220940 -0.30911074 -0.202206974 16 -0.4402302 -0.24992044 -0.2862218 -0.14971863 -0.11113094 -0.12681911 0.068209633 17 -0.4532868 -0.21272499 -0.2589631 -0.32715093 -0.22057358 -0.35795815 -0.099694655 18 -0.3534670 -0.20622737 -0.2342024 -0.32191285 -0.22548697 -0.43986760 -0.289805392 19 -0.3022847 -0.18094793 -0.2372830 -0.26960818 -0.33231501 -0.24573947 -0.190803741 20 1.0000000 -0.16030914 -0.4431029 -0.49896363 -0.60474230 -0.45062087 -0.160412252 21 -0.1603091 1.00000000 0.3949372 -0.22093728 -0.17595649 -0.29637709 -0.071261480 22 -0.4431029 0.39493723 1.0000000 -0.19191603 -0.29568944 -0.28494691 -0.219197015 23 -0.4989636 -0.22093728 -0.1919160 1.00000000 -0.14049366 -0.33665607 -0.111651776 24 -0.6047423 -0.17595649 -0.2956894 -0.14049366 1.00000000 -0.37274908 -0.023105895 25 -0.4506209 -0.29637709 -0.2849469 -0.33665607 -0.37274908 1.00000000 -0.300176452 26 -0.1604123 -0.07126148 -0.2191970 -0.11165178 -0.02310590 -0.30017645 1.000000000 27 -0.5753831 -0.28844491 -0.4205560 -0.66764036 -0.55285753 -0.58236100 -0.324238501 28 -0.2144654 -0.10262187 -0.1143893 -0.02042606 -0.18223594 -0.10313373 -0.051578049 29 -0.5912338 -0.32856190 -0.4070485 -0.37974096 -0.08699138 -0.59139630 -0.085933586 30 -0.2058809 -0.17602279 -0.1569804 -0.54944644 -0.44700052 -0.36125601 -0.274653736 V27 V28 V29 V30 1 -0.23972558 -0.08166544 -0.28756105 -0.24063924 2 0.02088273 -0.01726043 -0.40449427 -0.24945999 3 -0.31781076 -0.05726889 -0.22273056 -0.16182331 4 -0.56220331 -0.18713444 -0.40290796 -0.36365438 5 -0.22425857 -0.08907762 -0.24626614 -0.30139490 6 -0.35074206 -0.09245104 -0.37484128 -0.33564494 7 -0.35744763 -0.10775754 -0.17028847 -0.22184621 8 -0.23849681 0.97822277 -0.20499765 -0.09173103 9 -0.29780862 0.13832863 -0.37780755 -0.27400955 10 -0.27901641 0.96418680 -0.22690994 -0.10312034 11 -0.63585107 -0.16376802 -0.03145875 -0.41029974 12 -0.31531707 -0.13566979 -0.32584427 -0.29910598 13 -0.46157443 -0.17553399 -0.32204504 -0.28556026 14 -0.56557322 0.29063723 -0.38963846 -0.33820289 15 -0.50525365 0.40377075 -0.42030579 -0.48234193 16 -0.52595881 -0.17594595 -0.04573567 -0.44634143 17 -0.51698055 -0.02995276 -0.38658959 -0.29604837 18 -0.14831746 -0.06815830 -0.37212845 -0.49115267 19 -0.33029722 -0.03702463 -0.11486707 -0.25244406 20 -0.57538312 -0.21446539 -0.59123378 -0.20588088 21 -0.28844491 -0.10262187 -0.32856190 -0.17602279 22 -0.42055596 -0.11438930 -0.40704848 -0.15698038 23 -0.66764036 -0.02042606 -0.37974096 -0.54944644 24 -0.55285753 -0.18223594 -0.08699138 -0.44700052 25 -0.58236100 -0.10313373 -0.59139630 -0.36125601 26 -0.32423850 -0.05157805 -0.08593359 -0.27465374 27 1.00000000 -0.23675539 -0.71920429 -0.60508621 28 -0.23675539 1.00000000 -0.21504839 -0.10451766 29 -0.71920429 -0.21504839 1.00000000 -0.53962323 30 -0.60508621 -0.10451766 -0.53962323 1.00000000

2 2