Setembro 2017 - R-br

Packge Systemfit method SUR - ajuste simultâneo
by Thiago Wendling gonçalves de oliveira 27 Set '17

27 Set '17

Prezados, boa tarde!!! Meu nome é Thiago e sou doutorando em Eng. Florestal da UFPR Estou fazendo um trabalho de regressão linear para estimativa de biomassa de folhas, galhos e fuste de uma espécie florestal.A ideia é utilizar o ajuste simultâneo das equações, pois a biomassa aérea é a soma das biomassas mencionadas anteriormente.As variáveis independentes seriam a Biomassa da folha, galhos, fuste e aerea.As variáveis independentes seriam DAP (diâmetro à altura do peito) e Ht (altura total). O meu código do R foi o seguinte: install.packages("systemfit")library(systemfit) #Equações:#Biomassa_folhas =b0 + b1*DAP+b2*(DAP^2)+b3*((DAP^2)*Ht)#Biomassa_galhos=b0+b1*DAP+b2*(DAP^2)#Biomassa_fuste=b0+b1*DAP+b2*((DAP^2)*Ht)#Biomassa_aerea = Biomassa_folhas+Biomassa_galhos+Biomassa_fuste# Ou -> Biomassa_aerea = (b0 + b1*DAP+b2*(DAP^2)+b3*((DAP^2)*Ht)) + (b0+b1*DAP+b2*(DAP^2)) + (b0+b1*DAP+b2*((DAP^2)*Ht)) #Biomassa folhas eq1 <- tbl_araucaria$Bfolhas_kg~tbl_araucaria$DAP_cm+tbl_araucaria$dap2+tbl_araucaria$dap2Ht#Biomassa de galhoseq2 <- tbl_araucaria$Bgalhos_kg~tbl_araucaria$DAP_cm+tbl_araucaria$dap2#Biomassa do fusteeq3 <- tbl_araucaria$Bfuste_kg~tbl_araucaria$DAP_cm+tbl_araucaria$dap2Htsystem_eq <- list(eq1 = eq1, eq2 = eq2, eq3 = eq3) #SUR sur <- systemfit(system_eq, method = "SUR", data = tbl_araucaria)summary(sur) Para eu relacionar o ajuste simultâneo com a biomassa aérea (que seria a soma das biomassas) acredito que meu código ainda esteja incompleto e não sei se falta algum procedimento no código systemfit. Eu escrevi a equação para a biomassa aérea, considerando as minhas variáveis independentes dos três modelos (eq1,eq2,eq3):eq4 <- tbl_araucaria$Barea_kg~tbl_araucaria$DAP_cm+tbl_araucaria$dap2Ht+tbl_araucaria$dap2 Contudo a estimativa ainda não deu certo, pois acredito que as variáveis dependentes para a Biomassa_area ainda não estejam corretas. Alguém que trabalha com ajuste simultâneo e fez algum procedimento parecido no R poderia me auxiliar? Desde já agradeço. Thiago Wendling Gonçalves de Oliveira(41) 98858-2031 Universidade Federal do Paraná

1 0

Combinar linhas cuja interseção seja zero...
by andrebvs＠bol.com.br 27 Set '17

27 Set '17

2 1

[WordCloud] Problema com encoding
by Max 27 Set '17

27 Set '17

Pessoal, boa noite! Com base em um exemplo pego na internet, tentei fazer uma nuvem de palavras de um arquivo notepad (peguei uma pequena matéria na internet e salvei, em .txt, com o nome SaoBento). O código tem funcionado quase corretamente. O problema é que eu não tenho tido sucesso em corrigir o *encoding* do texto. - Tentei usar encoding = "UTF-8" na linha do readlines, mas sem sucesso. - Também tentei usar enc2native() na última linha do passo 7, mas ocorre erro argument is not a character vector. - Salvei o arquivo SaoBento.txt no Notepad++, usando UTF-8 e, também, a nuvem final acusou problemas de *encoding*. - Deixei o arquivo SaoBento.txt salvo sozinho em uma pasta e, também, não tive sucesso em acertar o *encoding*. Sendo assim, gostaria de saber se alguém poderia fornecer uma dica de como posso driblar esse problema. Agradeço pela atenção. Saudações, -Max Lara #============================================== # WORDCLOUD #============================================== *#1) INSTALL REQUIRED PACKAGES* install.packages("tm") #for text mining install.packages("SnowballC") #for text stemming install.packages("wordcloud") #wordcloud generator install.packages("RColorBrewer") #color palletes *#2) LOAD REQUIRED PACKAGES* library("tm") library("SnowballC") library("wordcloud") library("RColorBrewer") *#3) TEXT MINING* #LOAD THE TEXT (SAVED LOCALLY) text <- readLines(file.choose()) *#4) LOAD THE DATA AS A CORPUS* docs <- Corpus(VectorSource(text)) #VectorSource() function creates a corpus of character vectors docs <- tm_map(docs, PlainTextDocument) *#5) TEXT TRANSFORMATION* #tm_map() function (to replace, for instance, special characters from the text). #Replacing "/", "@" and "|" with space: toSpace <- content_transformer(function (x , pattern ) gsub(pattern, " ", x)) docs <- tm_map(docs, toSpace, "/") docs <- tm_map(docs, toSpace, "@") docs <- tm_map(docs, toSpace, "\\|") *#6) TEXT CLEANING* #tm_map() (remove unnecessary white space, to convert the text to lower case) #Removing common stopwords docs <- tm_map(docs, content_transformer(tolower)) #Convert the text to lower case docs <- tm_map(docs, removeNumbers) #Remove numbers docs <- tm_map(docs, removeWords, stopwords("portuguese")) #Remove Portuguese common stopwords docs <- tm_map(docs, removePunctuation) #Remove punctuations docs <- tm_map(docs, stripWhitespace) #Eliminate extra white spaces docs <- tm_map(docs, stemDocument) #Text stemming *#7) BUILD A TERM-DOCUMENT MATRIX (TDM)* #TDM is a table containing the frequency of the words. #Column names are words #Rown names are documents dtm <- TermDocumentMatrix(docs) terms(dtm) m <- as.matrix(dtm) v <- sort(rowSums(m),decreasing=TRUE) d <- data.frame(word = names(v),freq=v) head(d, 10) AQUI_ERRO <- d$word *#8) GENERATE THE WORD CLOUD* wordcloud( words = AQUI_ERRO, freq = d$freq, min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2")) *saída:* [image: Inline image 1]

1 0

Modelo GLM e ZIP
by Anna Karoline R. da Cruz 26 Set '17

26 Set '17

Boa tarde! Prezados, estou tentando ajustar o número de filhos/mulheres para 4 municípios a partir do modelo de poisson e ZIP. 1) Tentei colocar no modelo o número de filhos/mulheres (Y), mas o resultado foi: Error: não é possível encontrar valores iniciais válidos: especifique alguns Não sei se isso está relacionado com o fato desse vetor ter como respostas NaN e Inf. ou pq o Y seja mesmo o C (número de filhos). 2) E para plotar o gráfico de linhas com o número de filhos/mulheres já ajustado pelo modelo o comando (inicialmente) seria plot(F$Idade, (modelo.z$fitted.values)) ? 3) e qual comando posso usar para ter um gráfico para cada município? C= número de filhos segundo a idade simples da mulher M = número de mulheres por idade simples Idade = as idades simples das mulheres. ###saída GLM Call: glm(formula = C ~ M + Idade, family = poisson(), data = F) Deviance Residuals: Min 1Q Median 3Q Max -7.3875 -2.6026 -2.3316 0.9828 9.5008 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.2374245 0.1272825 9.722 <2e-16 *** M 0.0051281 0.0002939 17.451 <2e-16 *** Idade -0.0073104 0.0035070 -2.084 0.0371 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 2080.1 on 163 degrees of freedom Residual deviance: 1675.9 on 161 degrees of freedom AIC: 1941.2 Number of Fisher Scoring iterations: 6 ##Saída ZIP Call: zeroinfl(formula = C ~ M + Idade, data = F, dist = "poisson") Pearson residuals: Min 1Q Median 3Q Max -2.1522 -0.6622 -0.5179 0.4001 3.8106 Count model coefficients (poisson with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 2.1878890 0.1359308 16.096 <2e-16 *** M 0.0035164 0.0003052 11.521 <2e-16 *** Idade -0.0026143 0.0040649 -0.643 0.52 Zero-inflation model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) 0.277153 0.538864 0.514 0.60702 M -0.005364 0.001954 -2.745 0.00605 ** Idade 0.022874 0.015151 1.510 0.13111 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of iterations in BFGS optimization: 14 Log-likelihood: -389.5 on 6 Df Desde já agradeço! Anna Cruz.

1 0

Saída Html do pacote Gmisc
by Abel Brasil Ramos da Silva 21 Set '17

21 Set '17

Olá à todos, Boa Noite! Estou tentando reproduzir o seguinte código abaixo para gerar uma tabela Html na aba do Viewer: data(mtcars) library(Hmisc) label(mtcars$mpg) <- "Gas" units(mtcars$mpg) <- "Miles/(US) gallon" label(mtcars$wt) <- "Weight" units(mtcars$wt) <- "10<sup>3</sup> kg" # not sure the unit is correct mtcars$am <- factor(mtcars$am, levels=0:1, labels=c("Automatic", "Manual")) label(mtcars$am) <- "Transmission" mtcars$gear <- factor(mtcars$gear) label(mtcars$gear) <- "Gears" mtcars$col <- factor(sample(c("red", "black", "silver"), size=NROW(mtcars), replace=TRUE)) label(mtcars$col) <- "Car color" library(Gmisc) getTable1Stats <- function(x, digits = 0, ...){ getDescriptionStatsBy(x = x, by = mtcars$am, digits = digits, continuous_fn = describeMedian, header_count = TRUE, ...)} t1 <- list() t1[["Gas"]] <- getTable1Stats(mtcars$mpg) t1[["Weight&dagger;"]] <- getTable1Stats(mtcars$wt) t1[["Color"]] <- getTable1Stats(mtcars$col) # If we want to use the labels set in the beginning # we add an element without a name t1 <- c(t1, list(getTable1Stats(mtcars$gear))) mergeDesc(t1, htmlTable_args = list(css.rgroup = "", caption = "Basic descriptive statistics from the mtcars dataset", tfoot = "&dagger; The weight is in 10<sup>3</sup> kg")) *PORÉM, AO INVÉS DO OUTPUT SAIR NO VIEWER EM FORMA DE TABELA O MESMO PRODUZ A SAÍDA ABAIXO:* <table class='gmisc_table' style='border-collapse: collapse; margin-top: 1em; margin-bottom: 1em;' > <thead> <tr><td colspan='3' style='text-align: left;'> Basic descriptive statistics from the mtcars dataset</td></tr> <tr> <th style='border-bottom: 1px solid grey; border-top: 2px solid grey;'> </th> <th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>Automatic<br /> No. 19</th> <th style='border-bottom: 1px solid grey; border-top: 2px solid grey; text-align: center;'>Manual<br /> No. 13</th> </tr> </thead> <tbody> <tr> <td style='text-align: left;'>Gas</td> <td style='text-align: center;'>17 (15 - 19)</td> <td style='text-align: center;'>23 (21 - 30)</td> </tr> <tr> <td style='text-align: left;'>Weight&dagger;</td> <td style='text-align: center;'>4 (3 - 4)</td> <td style='text-align: center;'>2 (2 - 3)</td> </tr> <tr><td colspan='3' style=''>Color</td></tr> <tr> <td style='text-align: left;'>  black</td> <td style='text-align: center;'>11 (58%)</td> <td style='text-align: center;'>5 (38%)</td> </tr> <tr> <td style='text-align: left;'>  red</td> <td style='text-align: center;'>6 (32%)</td> <td style='text-align: center;'>6 (46%)</td> </tr> <tr> <td style='text-align: left;'>  silver</td> <td style='text-align: center;'>2 (11%)</td> <td style='text-align: center;'>2 (15%)</td> </tr> <tr><td colspan='3' style=''>Gears</td></tr> <tr> <td style='text-align: left;'>  3</td> <td style='text-align: center;'>15 (79%)</td> <td style='text-align: center;'>0 (0%)</td> </tr> <tr> <td style='text-align: left;'>  4</td> <td style='text-align: center;'>4 (21%)</td> <td style='text-align: center;'>8 (62%)</td> </tr> <tr> <td style='border-bottom: 2px solid grey; text-align: left;'>  5</td> <td style='border-bottom: 2px solid grey; text-align: center;'>0 (0%)</td> <td style='border-bottom: 2px solid grey; text-align: center;'>5 (38%)</td> </tr> </tbody> <tfoot><tr><td colspan='3'> &dagger; The weight is in 10<sup>3</sup> kg</td></tr></tfoot> </table> Gostaria de saber como faço para que o output seja reproduzido no Viewer? Agradeço à ajuda!

1 0

Uso do iRamuteq - interface com R
by Ronaldo Câmara de Araújo 20 Set '17

20 Set '17

Olá, Alguém do grupo já utilizou o iRamuteq? Quem pode ajudar em saber os pacotes que ele utiliza e como faz? Grato.

2 1

ajustar escala e interpolação
by Heloíse Pavanato 19 Set '17

19 Set '17

Colegas do R-br, Tenho a seguinte questão: Estou trabalhando com um grid de 0.1 x 0.1 grau de latitude e longitude onde quero inserir a variável vento. No entanto a resolução do vento é de 0.25 x 0.25 graus. Os dados estão armazenados aqui: http://www.datafilehost.com/d/693635d4 http://www.datafilehost.com/d/ee24730c vento <- read.csv('ascat.data.csv', header = TRUE) str(vento) grid <- read.csv('grid.data.csv', header = TRUE) str(grid) #Quando faço um merge() dos dois dataframes, obviamente meus valores de #vento$media aparecem como NA pois não há nenhuma correspondência entre as #coordenadas do grid e de vento. dmerge <- merge(grid, vento, by = c('lon', 'lat'), all.x = TRUE) Porém, eu gostaria de obter valores de vento, por exemplo, entre latitudes de -22.625 e -22.375 na mesma escala do grid: -22.6, -22.5, -22.4, etc. E o mesmo para longitude. Tentei utilizar a scales::rescale(), mas consegui mudar apenas os mínimos e máximos das coordenadas: #install.packages('scales', dependencies = TRUE) library(scales) vento$lon.r <- rescale(vento$lon, to = range(grid$lon)) vento$lon.r vento$lat.r <- rescale(vento$lat, to = range(grid$lat)) vento$lat.r Então, creio que eu tenho dois problemas: o primeiro é obter as coordenadas de vento na mesma escala que as do grid; segundo é interpolar vento$media para substituir os NAs, que correspondem a quase metade do total de valores. Agradeço se algum colega tiver alguma dica. Obrigada, Heloise.

6 20

Regressão Exponencial
by Fabiana Goncalves Barbosa 18 Set '17

18 Set '17

Boa Tarde! Tenho os seguintes dados: x = c(1,1,2,2,1,3,10,15,9,17,13,18,21,27) tempo=c(2001,2003,2004,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016) Ao plotar (x,y), verifico um crescimento exponencial do x ao longo do tempo. Alguém teria o script de regressão exponencial para me ajudar? Grata Fabiana

3 2

sparse matrix para matriz
by Edimeire Alexandra Pinto 15 Set '17

15 Set '17

Bom dia, pessoal. Alguém poderia me tirar uma dúvida sobre Sparse matrix? Tenho um objeto em que os identificadores de linhas e colunas são os mesmos, códigos de municipios (por exemplo,3509106 ,3511003, .... ) e estão em dimnames. O conteúdo da matriz é composto por pontos e proporções, como vcs vêem abaixo, no exemplo resumido (pois são mais de 5000 municipios, ou seja, matriz bem grande). > matw5018 x 5018 sparse Matrix of class "dgCMatrix" [[ suppressing 48 column names ‘3509106’, ‘3511003’, ‘3514403’ ... ]] [[ suppressing 48 column names ‘3509106’, ‘3511003’, ‘3514403’ ... ]] 3509106 . . . . 0.25 . . . . . 0.25 . . 0.2500000 0.2500000 . . 3511003 . . . . . . . . . . . 0.1666667 . . . . . 3514403 . . . . . . . . . 0.2 . . 0.2000000 . 0.2000000 . . 3515301 . . . . . . . . . . . . . . . . 0.33333333528700 0.1428571 . . . . 0.1428571 . . . . . . 0.1428571 0.1428571 0.1428571 . . 3530201 . . . . 0.20 . . . . . . . . . . . 0.20000003531605 . . . . . . . . 0.2 . . . . . . . . 3532207 . . . . . . . . . . . . . . . . . 3533106 . . . . . . 0.25 . . . . . . . . . . 3534807 . . 0.2 . . . . . . . 0.20 . . . 0.2000000 . . Por que toda vez que uso o comando as.matrix(matw) ele simplesmente retira as proporções e coloca como conteúdo da matriz os codigos dos municipios? Veja abaixo. matw_matriz 3509106 3511003 3514403 3515301 3528700 3530201 3531605 3532207 3533106 3534807 3535408 3536406 3538303 3541307 3541505 3544251 3545506 3547106 3547700 3549300 3553906 3554300 3555109 4200101 4200507 4200556 4200754 4201273 4201653 4202081 4202578 4203105 4203501 4203907 4204103 4204202 4204301 4204350 4204400 4204459 4204707 4204756 4204905 4205001 4205175 4205308 4205357 4205431 4205605 4206603 4206652 4207601 4207650 4207684 4207700 4207759 4207809 4208005 4208401 4208609 4209003 4209177 4209201 4209458 4209854 4210035 4210506 4210555 4210902 4211009 4211405 4211454 4211801 4211876 4212007 4212106 4212270 4212601 4212908 4213104 4213153 4213401 4213906 4214151 4214201 4215075 4215208 4215356 4215687 Preciso que onde há pontos virem zero e as proporções permaneçam na matriz e os codigos dos municipios continuem como dimnanes da matriz. Eu faço essa pergunta para vcs, pois ao rodar painel espacial usando a função spml, o resultado é de erro e diz que Error in checklw(listw) : 'listw' has to be either a 'listw' or a 'matrix' object Obrigada

2 2

xyplot
by Fernando Souza 14 Set '17

14 Set '17

Amigos como faço para colocar a linha dos valores preditos e intervalo de confiança da predição sobre esses gráfico? Fiz uma tentativa, o gráfico não ficou agradável GALINHA<-structure(list(ID = structure(c(128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 279L, 352L, 360L, 6L, 9L, 340L, 31L, 18L, 274L, 322L, 335L, 300L, 370L, 225L, 364L, 311L, 312L, 337L, 338L, 356L, 354L, 213L, 21L, 220L, 294L, 224L, 319L, 276L, 347L, 263L, 175L, 172L, 138L, 187L, 7L, 4L, 32L, 12L, 128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 279L, 352L, 360L, 6L, 9L, 340L, 31L, 18L, 274L, 322L, 335L, 300L, 370L, 225L, 364L, 311L, 312L, 337L, 338L, 356L, 354L, 213L, 21L, 220L, 294L, 224L, 319L, 276L, 347L, 263L, 175L, 172L, 138L, 187L, 7L, 4L, 32L, 12L, 128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 279L, 352L, 360L, 6L, 9L, 340L, 31L, 18L, 274L, 322L, 335L, 300L, 370L, 225L, 364L, 311L, 312L, 337L, 338L, 356L, 354L, 213L, 21L, 220L, 294L, 224L, 319L, 276L, 347L, 263L, 175L, 172L, 138L, 187L, 7L, 4L, 32L, 12L, 128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 279L, 352L, 360L, 6L, 9L, 340L, 31L, 18L, 274L, 322L, 335L, 300L, 370L, 225L, 364L, 311L, 312L, 337L, 338L, 356L, 354L, 213L, 21L, 220L, 294L, 224L, 319L, 276L, 347L, 263L, 175L, 172L, 138L, 187L, 7L, 4L, 32L, 12L, 128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 279L, 352L, 360L, 6L, 9L, 340L, 31L, 18L, 274L, 322L, 335L, 300L, 370L, 225L, 364L, 311L, 312L, 337L, 338L, 356L, 354L, 213L, 21L, 220L, 294L, 224L, 319L, 276L, 347L, 263L, 175L, 172L, 138L, 187L, 7L, 4L, 32L, 12L, 128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 279L, 352L, 360L, 6L, 9L, 340L, 31L, 18L, 274L, 322L, 335L, 300L, 370L, 225L, 364L, 311L, 312L, 337L, 338L, 356L, 354L, 213L, 21L, 220L, 294L, 224L, 319L, 276L, 347L, 263L, 175L, 172L, 138L, 187L, 7L, 4L, 32L, 12L, 128L, 48L, 109L, 52L, 42L, 16L, 210L, 37L, 281L, 96L, 255L, 1L, 189L, 323L, 77L, 184L, 66L, 293L, 268L, 205L, 41L, 43L, 2L, 34L, 85L, 193L, 63L, 55L, 131L, 54L, 67L, 178L, 103L, 93L, 56L, 79L, 133L, 80L, 264L, 161L, 114L, 288L, 28L, 113L, 88L, 97L, 160L, 68L, 58L, 246L, 25L, 141L, 94L, 150L, 284L, 60L, 278L, 26L, 29L, 72L, 61L, 49L, 40L, 287L, 98L, 75L, 136L, 45L, 144L, 5L, 11L, 124L, 233L, 273L, 3L, 125L, 152L, 137L, 71L, 46L, 27L, 107L, 59L, 92L, 44L, 51L, 157L, 89L, 134L, 126L, 84L, 82L, 145L, 53L, 111L, 214L, 153L, 74L, 99L, 257L, 306L, 105L, 108L, 116L, 277L, 143L, 230L, 64L, 171L, 122L, 91L, 282L, 135L, 203L, 30L, 147L, 156L, 155L, 35L, 76L, 23L, 185L, 262L, 247L, 123L, 227L, 258L, 238L, 223L, 73L, 130L, 81L, 154L, 104L, 291L, 216L, 112L, 333L, 165L, 212L, 308L, 201L, 313L, 324L, 148L, 321L, 170L, 211L, 102L, 198L, 286L, 229L, 249L, 167L, 159L, 191L, 283L, 320L, 305L, 317L, 355L, 330L, 182L, 221L, 231L, 241L, 166L, 234L, 142L, 199L, 217L, 117L, 121L, 132L, 174L, 266L, 215L, 325L, 65L, 301L, 106L, 179L, 50L, 33L, 115L, 8L, 304L, 345L, 363L, 169L, 36L, 292L, 110L, 47L, 192L, 95L, 101L, 69L, 202L, 146L, 200L, 173L, 100L, 39L, 302L, 336L, 290L, 332L, 86L, 127L, 197L, 310L, 242L, 118L, 90L, 120L, 13L, 285L, 183L, 188L, 158L, 252L, 164L, 177L, 190L, 186L, 237L, 62L, 78L, 240L, 250L, 196L, 228L, 149L, 87L, 243L, 24L, 14L, 204L, 140L, 15L, 244L, 339L, 180L, 303L, 195L, 265L, 289L, 269L, 272L, 350L, 162L, 254L, 344L, 139L, 151L, 226L, 270L, 280L, 327L, 57L, 361L, 176L, 251L, 235L, 351L, 119L, 163L, 206L, 271L, 365L, 314L, 329L, 181L, 295L, 239L, 316L, 70L, 207L, 362L, 10L, 208L, 343L, 342L, 219L, 253L, 298L, 349L, 299L, 222L, 307L, 245L, 334L, 248L, 315L, 366L, 17L, 296L, 369L, 341L, 260L, 309L, 297L, 328L, 261L, 83L, 367L, 38L, 318L, 259L, 358L, 346L, 20L, 331L, 348L, 256L, 275L, 168L, 218L, 194L, 232L, 22L, 368L, 209L, 357L, 267L, 19L, 129L, 326L, 236L, 353L, 359L, 279L, 352L, 360L, 6L, 9L, 340L, 31L, 18L, 274L, 322L, 335L, 300L, 370L, 225L, 364L, 311L, 312L, 337L, 338L, 356L, 354L, 213L, 21L, 220L, 294L, 224L, 319L, 276L, 347L, 263L, 175L, 172L, 138L, 187L, 7L, 4L, 32L, 12L), .Label = c("12", "23", "75", "368", "70", "336", "367", "186", "337", "281", "71", "370", "217", "238", "241", "6", "297", "340", "327", "313", "355", "322", "121", "237", "51", "58", "81", "43", "59", "115", "339", "369", "184", "24", "119", "191", "8", "308", "204", "63", "21", "5", "22", "85", "68", "80", "194", "2", "62", "183", "86", "4", "94", "30", "28", "35", "261", "49", "83", "56", "61", "228", "27", "108", "179", "17", "31", "48", "198", "278", "79", "60", "130", "98", "66", "120", "15", "229", "36", "38", "132", "92", "306", "91", "25", "209", "235", "45", "88", "215", "111", "84", "34", "53", "196", "10", "46", "65", "99", "203", "197", "149", "33", "134", "102", "181", "82", "103", "3", "193", "95", "137", "44", "41", "185", "104", "172", "214", "267", "216", "173", "110", "125", "72", "76", "90", "210", "1", "328", "131", "29", "174", "37", "89", "113", "67", "78", "365", "255", "240", "52", "169", "106", "69", "93", "200", "116", "145", "234", "54", "256", "77", "97", "133", "118", "117", "87", "221", "155", "47", "40", "252", "268", "223", "139", "167", "154", "318", "190", "147", "109", "364", "202", "175", "363", "263", "224", "32", "182", "244", "274", "163", "219", "16", "122", "226", "366", "220", "13", "225", "156", "195", "26", "320", "246", "232", "211", "150", "170", "201", "142", "199", "114", "239", "20", "269", "279", "282", "324", "7", "148", "140", "354", "96", "177", "136", "171", "319", "285", "356", "164", "290", "129", "358", "346", "257", "126", "233", "152", "107", "165", "321", "73", "168", "265", "330", "227", "128", "276", "230", "166", "213", "236", "242", "292", "50", "124", "294", "153", "231", "264", "222", "286", "253", "11", "316", "100", "127", "310", "301", "305", "123", "362", "39", "247", "176", "326", "19", "249", "258", "270", "250", "74", "341", "317", "360", "105", "57", "333", "259", "9", "112", "157", "55", "218", "151", "64", "42", "248", "207", "135", "192", "18", "357", "275", "298", "303", "287", "289", "344", "180", "205", "245", "187", "159", "101", "291", "141", "302", "212", "348", "349", "143", "272", "295", "277", "160", "309", "359", "158", "146", "342", "14", "144", "178", "329", "260", "304", "273", "162", "314", "208", "138", "293", "343", "206", "350", "351", "243", "338", "300", "284", "283", "254", "188", "312", "361", "315", "288", "251", "266", "334", "331", "353", "161", "352", "325", "311", "332", "335", "262", "280", "189", "347", "271", "296", "307", "323", "299", "345"), class = c("ordered", "factor")), TRAT = structure(c(3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("DSM", "NUT", "POL"), class = "factor"), SEMANA = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6), PESO = c(0.0395, 0.0431, 0.046, 0.043, 0.043, 0.0436, 0.042, 0.0405, 0.0417, 0.045, 0.043, 0.0416, 0.041, 0.042, 0.0445, 0.044, 0.047, 0.043, 0.049, 0.0435, 0.044, 0.0421, 0.0444, 0.041, 0.042, 0.0415, 0.0411, 0.0422, 0.042, 0.042, 0.045, 0.045, 0.0416, 0.044, 0.044, 0.041, 0.04, 0.039, 0.0408, 0.037, 0.044, 0.042, 0.041, 0.04, 0.0435, 0.039, 0.0404, 0.0466, 0.0461, 0.0435, 0.043, 0.043, 0.041, 0.0432, 0.0393, 0.0415, 0.041, 0.041, 0.042, 0.0404, 0.0385, 0.0435, 0.034, 0.0409, 0.044, 0.045, 0.045, 0.046, 0.045, 0.0396, 0.0406, 0.039, 0.038, 0.039, 0.045, 0.042, 0.042, 0.0413, 0.043, 0.042, 0.0385, 0.0415, 0.0391, 0.048, 0.043, 0.039, 0.048, 0.0388, 0.039, 0.043, 0.0436, 0.043, 0.0435, 0.0395, 0.0424, 0.0414, 0.0422, 0.0394, 0.0405, 0.041, 0.0373, 0.0466, 0.045, 0.0431, 0.0435, 0.047, 0.044, 0.042, 0.041, 0.0409, 0.042, 0.042, 0.043, 0.0375, 0.043, 0.038, 0.0402, 0.0375, 0.043, 0.044, 0.043, 0.0415, 0.0445, 0.044, 0.0445, 0.0413, 0.041, 0.043, 0.032, 0.044, 0.0392, 0.043, 0.0437, 0.034, 0.038, 0.0386, 0.037, 0.044, 0.046, 0.0445, 0.0425, 0.045, 0.0388, 0.0387, 0.039, 0.0386, 0.0421, 0.0405, 0.0412, 0.042, 0.0471, 0.042, 0.047, 0.039, 0.039, 0.043, 0.041, 0.044, 0.044, 0.042, 0.042, 0.042, 0.0422, 0.047, 0.047, 0.0415, 0.045, 0.045, 0.044, 0.0426, 0.0405, 0.044, 0.0446, 0.04, 0.0445, 0.0395, 0.0444, 0.046, 0.0465, 0.0415, 0.042, 0.042, 0.0481, 0.047, 0.042, 0.041, 0.041, 0.04, 0.0451, 0.043, 0.0415, 0.0432, 0.0414, 0.0445, 0.0427, 0.041, 0.04, 0.047, 0.043, 0.043, 0.0404, 0.0394, 0.043, 0.043, 0.04, 0.042, 0.0424, 0.042, 0.046, 0.042, 0.04, 0.0404, 0.0417, 0.0404, 0.0442, 0.041, 0.0405, 0.0426, 0.0433, 0.044, 0.0465, 0.0405, 0.0451, 0.0446, 0.043, 0.04, 0.041, 0.0454, 0.0449, 0.0433, 0.043, 0.0424, 0.0439, 0.044, 0.0452, 0.04, 0.04, 0.0416, 0.0405, 0.04, 0.044, 0.043, 0.045, 0.043, 0.043, 0.0424, 0.041, 0.0464, 0.0447, 0.0426, 0.0426, 0.042, 0.0421, 0.045, 0.0379, 0.044, 0.042, 0.042, 0.042, 0.0433, 0.034, 0.037, 0.037, 0.0445, 0.042, 0.042, 0.044, 0.045, 0.042, 0.0416, 0.0436, 0.0405, 0.0426, 0.044, 0.044, 0.0404, 0.039, 0.0435, 0.0469, 0.0435, 0.045, 0.043, 0.043, 0.038, 0.0438, 0.0433, 0.0404, 0.0435, 0.043, 0.043, 0.038, 0.041, 0.042, 0.04, 0.037, 0.038, 0.0417, 0.043, 0.047, 0.0449, 0.043, 0.04, 0.04, 0.04, 0.0421, 0.0442, 0.043, 0.041, 0.041, 0.0414, 0.0435, 0.045, 0.044, 0.044, 0.0425, 0.0465, 0.043, 0.0446, 0.045, 0.043, 0.046, 0.0423, 0.045, 0.047, 0.0464, 0.0444, 0.0446, 0.044, 0.0438, 0.044, 0.0484, 0.043, 0.0434, 0.0395, 0.0445, 0.037, 0.0395, 0.039, 0.039, 0.043, 0.0409, 0.0441, 0.0452, 0.044, 0.044, 0.0458, 0.043, 0.044, 0.04, 0.0408, 0.044, 0.044, 0.041, 0.0439, 0.045, 0.0416, 0.0447, 0.0443, 0.044, 0.042, 0.0447, 0.043, 0.044, 0.0408, 0.044, 0.044, 0.046, 0.0435, 0.047, 0.0417, 0.177, 0.1757, 0.189, 0.185, 0.1836, 0.1969, 0.1746, 0.1561, 0.1757, 0.176, 0.1957, 0.1555, 0.1684, 0.171, 0.1771, 0.1929, 0.1927, 0.185, 0.1935, 0.1883, 0.1837, 0.1763, 0.1669, 0.172, 0.172, 0.1699, 0.1722, 0.1868, 0.1836, 0.137, 0.1565, 0.1765, 0.1655, 0.18, 0.1682, 0.17, 0.1897, 0.1746, 0.1869, 0.17, 0.195, 0.207, 0.1388, 0.1815, 0.1868, 0.154, 0.1514, 0.1862, 0.178, 0.182, 0.166, 0.181, 0.146, 0.1704, 0.1541, 0.1749, 0.1976, 0.1671, 0.1706, 0.1708, 0.1597, 0.1731, 0.1465, 0.1764, 0.1647, 0.1647, 0.2056, 0.1812, 0.1746, 0.1651, 0.1705, 0.18, 0.163, 0.18, 0.183, 0.163, 0.1859, 0.1697, 0.1895, 0.1737, 0.1577, 0.1716, 0.1605, 0.1879, 0.1814, 0.1578, 0.1684, 0.157, 0.1619, 0.1793, 0.1828, 0.1705, 0.184, 0.1516, 0.1754, 0.1818, 0.1984, 0.1711, 0.1765, 0.2006, 0.1728, 0.1843, 0.178, 0.1707, 0.1761, 0.175, 0.1882, 0.1915, 0.1719, 0.1618, 0.165, 0.153, 0.167, 0.184, 0.165, 0.17, 0.1745, 0.1825, 0.1461, 0.2103, 0.1687, 0.17, 0.183, 0.18, 0.1683, 0.1898, 0.1731, 0.1827, 0.1693, 0.1859, 0.1723, 0.2012, 0.1988, 0.1447, 0.1898, 0.1522, 0.1659, 0.178, 0.18, 0.1824, 0.1778, 0.1824, 0.1854, 0.181, 0.1684, 0.1825, 0.1898, 0.186, 0.1813, 0.1852, 0.189, 0.1812, 0.1847, 0.167, 0.1523, 0.1881, 0.1753, 0.2212, 0.204, 0.205, 0.203, 0.203, 0.1786, 0.1906, 0.1907, 0.1894, 0.186, 0.18, 0.197, 0.1349, 0.1805, 0.1968, 0.1885, 0.1794, 0.1953, 0.1835, 0.2037, 0.21, 0.1988, 0.1806, 0.1847, 0.1941, 0.1857, 0.182, 0.1814, 0.178, 0.19, 0.179, 0.1911, 0.1925, 0.1763, 0.1927, 0.207, 0.183, 0.1831, 0.188, 0.1753, 0.2071, 0.1976, 0.1941, 0.1688, 0.1778, 0.1941, 0.1653, 0.19, 0.194, 0.1889, 0.1835, 0.186, 0.1812, 0.1779, 0.1744, 0.1843, 0.1808, 0.1828, 0.1701, 0.193, 0.1987, 0.1915, 0.192, 0.1966, 0.1907, 0.1963, 0.1907, 0.1855, 0.1682, 0.165, 0.1871, 0.1908, 0.183, 0.182, 0.1809, 0.1806, 0.1765, 0.1814, 0.1913, 0.1877, 0.1902, 0.1867, 0.1755, 0.196, 0.1882, 0.1984, 0.1874, 0.1835, 0.1774, 0.181, 0.188, 0.19, 0.176, 0.1825, 0.1844, 0.1873, 0.1843, 0.1694, 0.1871, 0.1706, 0.1729, 0.1794, 0.1836, 0.1459, 0.1565, 0.1565, 0.178, 0.176, 0.17, 0.1682, 0.179, 0.173, 0.1812, 0.1915, 0.172, 0.195, 0.2, 0.19, 0.1972, 0.17, 0.1772, 0.2012, 0.1857, 0.1959, 0.1834, 0.197, 0.1647, 0.1796, 0.1798, 0.1818, 0.1986, 0.2089, 0.142, 0.1882, 0.2059, 0.1882, 0.1871, 0.1706, 0.182, 0.2036, 0.196, 0.21, 0.2142, 0.1871, 0.1781, 0.1835, 0.174, 0.1831, 0.17, 0.174, 0.198, 0.186, 0.1808, 0.1983, 0.192, 0.1894, 0.1859, 0.184, 0.1847, 0.1694, 0.1734, 0.184, 0.1847, 0.1682, 0.1843, 0.1848, 0.181, 0.1915, 0.1823, 0.1796, 0.1953, 0.2031, 0.1824, 0.1882, 0.18, 0.1837, 0.1747, 0.1928, 0.1765, 0.1714, 0.1666, 0.1724, 0.1918, 0.1894, 0.1864, 0.1936, 0.1813, 0.199, 0.1896, 0.1812, 0.186, 0.1714, 0.167, 0.1824, 0.194, 0.185, 0.189, 0.2, 0.1997, 0.186, 0.1961, 0.1827, 0.1806, 0.1825, 0.18, 0.1789, 0.173, 0.1616, 0.1776, 0.191, 0.1788, 0.1976, 0.1767, 0.4056, 0.4401, 0.4615, 0.44, 0.4698, 0.4991, 0.441, 0.3404, 0.4144, 0.3682, 0.5132, 0.4332, 0.4246, 0.4535, 0.4288, 0.4961, 0.4296, 0.4883, 0.4836, 0.4675, 0.4524, 0.4419, 0.4568, 0.451, 0.473, 0.4515, 0.4702, 0.4663, 0.4743, 0.3246, 0.3918, 0.4387, 0.3816, 0.4083, 0.4273, 0.444, 0.4702, 0.441, 0.4843, 0.414, 0.4659, 0.5444, 0.31, 0.4602, 0.428, 0.4058, 0.4367, 0.4797, 0.4287, 0.4827, 0.332, 0.45, 0.334, 0.3776, 0.3914, 0.3915, 0.477, 0.348, 0.3536, 0.4312, 0.4031, 0.4253, 0.3677, 0.4695, 0.3738, 0.4018, 0.5217, 0.44, 0.4714, 0.3999, 0.4478, 0.4517, 0.404, 0.424, 0.3885, 0.4125, 0.5118, 0.3975, 0.4465, 0.4021, 0.3555, 0.3804, 0.3547, 0.4518, 0.391, 0.3592, 0.4066, 0.3966, 0.3888, 0.4716, 0.419, 0.4565, 0.4658, 0.403, 0.4524, 0.509, 0.516, 0.434, 0.433, 0.5141, 0.4718, 0.46, 0.491, 0.4407, 0.4504, 0.437, 0.4587, 0.4484, 0.4457, 0.4063, 0.461, 0.395, 0.391, 0.4757, 0.394, 0.4572, 0.4405, 0.4794, 0.3574, 0.5332, 0.3565, 0.465, 0.4525, 0.473, 0.4234, 0.5006, 0.4651, 0.4479, 0.431, 0.4939, 0.4354, 0.4916, 0.499, 0.4018, 0.4931, 0.4217, 0.4523, 0.4505, 0.44, 0.4748, 0.5106, 0.4714, 0.4307, 0.4692, 0.42, 0.5005, 0.4712, 0.4823, 0.4393, 0.4855, 0.4759, 0.4692, 0.4613, 0.4336, 0.4354, 0.4852, 0.4164, 0.5534, 0.5085, 0.5197, 0.5264, 0.5085, 0.4542, 0.4702, 0.47, 0.4701, 0.4565, 0.4972, 0.456, 0.3752, 0.449, 0.4517, 0.4309, 0.4299, 0.4839, 0.5006, 0.4957, 0.5399, 0.4951, 0.4752, 0.483, 0.496, 0.42, 0.445, 0.4378, 0.44, 0.534, 0.483, 0.5379, 0.5157, 0.4049, 0.534, 0.5041, 0.3965, 0.4214, 0.4895, 0.3962, 0.4961, 0.4786, 0.5021, 0.4374, 0.4956, 0.4731, 0.3408, 0.537, 0.4961, 0.4773, 0.5309, 0.4827, 0.416, 0.47, 0.4505, 0.4698, 0.46, 0.4632, 0.4726, 0.467, 0.5012, 0.4719, 0.445, 0.4853, 0.4754, 0.5109, 0.469, 0.4735, 0.416, 0.408, 0.4676, 0.4745, 0.4644, 0.4546, 0.5077, 0.4571, 0.4419, 0.425, 0.4886, 0.4662, 0.4973, 0.4842, 0.447, 0.522, 0.4971, 0.5032, 0.4757, 0.461, 0.467, 0.458, 0.5102, 0.4778, 0.4804, 0.5032, 0.4724, 0.4802, 0.5519, 0.4094, 0.4445, 0.449, 0.4647, 0.4809, 0.4684, 0.3682, 0.4456, 0.3842, 0.48, 0.4378, 0.4489, 0.4164, 0.461, 0.4201, 0.5157, 0.4727, 0.4375, 0.5038, 0.515, 0.499, 0.4936, 0.42, 0.4748, 0.5001, 0.4787, 0.4331, 0.4426, 0.482, 0.431, 0.4612, 0.4678, 0.4659, 0.48, 0.5247, 0.3512, 0.4568, 0.4905, 0.4669, 0.4714, 0.452, 0.4653, 0.4992, 0.492, 0.489, 0.5304, 0.501, 0.4964, 0.4658, 0.4897, 0.4783, 0.3947, 0.481, 0.501, 0.471, 0.4449, 0.4865, 0.4872, 0.4961, 0.4624, 0.4775, 0.4414, 0.44, 0.4343, 0.449, 0.4601, 0.44, 0.4997, 0.4974, 0.486, 0.509, 0.4702, 0.4749, 0.4434, 0.5167, 0.4438, 0.4847, 0.4539, 0.4285, 0.4773, 0.4846, 0.4197, 0.458, 0.453, 0.438, 0.4939, 0.4839, 0.4589, 0.473, 0.4731, 0.506, 0.4954, 0.4714, 0.4536, 0.4439, 0.4431, 0.4818, 0.496, 0.5108, 0.4134, 0.5051, 0.5247, 0.4821, 0.4583, 0.475, 0.4485, 0.4692, 0.4501, 0.4639, 0.44, 0.4004, 0.4501, 0.4843, 0.4606, 0.5253, 0.4429, 0.8378, 0.8471, 0.8886, 0.81, 0.9366, 0.9909, 0.8597, 0.6937, 0.8395, 0.832, 1.009, 0.8833, 0.7511, 0.8965, 0.8922, 0.96, 0.7807, 1.002, 0.963, 0.962, 0.8641, 0.8847, 0.8782, 0.81, 0.902, 0.7963, 0.8896, 0.8852, 0.9848, 0.737, 0.8543, 0.9315, 0.8005, 0.8761, 0.9185, 0.85, 0.9096, 0.815, 0.9353, 0.913, 0.8832, 1.0239, 0.634, 0.8885, 0.734, 0.8875, 0.8878, 0.9387, 0.8309, 0.9772, 0.718, 0.8574, 0.727, 0.843, 0.8819, 0.8765, 0.9533, 0.6587, 0.6609, 0.8953, 0.8873, 0.7675, 0.7517, 0.9561, 0.8424, 0.7728, 1.0431, 0.805, 0.9166, 0.8258, 0.9308, 0.8518, 0.8546, 0.8185, 0.7579, 0.8505, 0.9554, 0.8116, 0.8611, 0.7879, 0.775, 0.771, 0.7706, 0.8675, 0.7561, 0.7585, 0.8196, 0.7993, 0.842, 0.9372, 0.8446, 0.9328, 0.9495, 0.7644, 0.9246, 0.9592, 0.9594, 0.842, 0.9085, 0.986, 0.9085, 0.8866, 0.7695, 0.9027, 0.9104, 0.9304, 0.8761, 0.8988, 0.9277, 0.8332, 0.861, 0.776, 0.844, 0.97, 0.8026, 0.895, 0.8928, 0.918, 0.7535, 0.9975, 0.7307, 0.9997, 0.9169, 0.9444, 0.8713, 0.9523, 0.8918, 0.9314, 0.9233, 0.9, 0.8646, 0.9181, 0.9478, 0.7761, 1.0358, 0.8917, 0.8, 0.888, 0.8283, 0.9174, 0.9131, 0.927, 0.8629, 0.9495, 0.7656, 1.0016, 0.9123, 0.915, 0.8805, 0.9568, 0.9769, 0.9674, 0.9783, 0.901, 0.8097, 0.9665, 0.7767, 1.0761, 0.998, 0.988, 1.004, 0.987, 0.9206, 0.945, 0.91, 0.9553, 0.961, 0.975, 0.95, 0.8542, 0.9019, 0.9331, 0.8521, 0.8897, 0.9847, 1.0218, 0.9577, 1.0783, 0.9665, 0.9721, 0.937, 0.91, 0.8823, 0.86, 0.8604, 0.838, 1, 1, 1.0924, 0.991, 0.816, 1.0692, 0.9754, 0.847, 0.8324, 0.9739, 0.8891, 0.9609, 0.9596, 1.013, 0.8791, 0.9591, 1.0097, 0.7858, 0.994, 0.9978, 1.0076, 1.0848, 0.9174, 0.8936, 0.9353, 0.94, 0.9573, 0.8629, 0.9516, 0.903, 0.93, 0.979, 0.9293, 0.812, 0.9387, 0.9252, 0.9476, 0.9151, 0.9239, 0.8873, 0.889, 0.9308, 0.9984, 0.8824, 0.936, 0.9941, 0.962, 0.8414, 0.854, 0.9367, 0.9601, 0.9998, 0.9538, 0.904, 1.016, 0.9036, 0.977, 0.9141, 0.8887, 0.9664, 0.9467, 1.0024, 0.9383, 1.0171, 0.9536, 0.9195, 0.9381, 0.9906, 0.8393, 0.8997, 0.95, 0.9793, 1.0044, 1.0127, 0.7932, 0.9554, 0.7272, 0.9685, 0.8804, 0.9098, 0.865, 0.896, 0.8813, 0.985, 0.9001, 0.8505, 0.973, 1.0228, 1.0326, 0.9605, 0.9089, 0.969, 0.9468, 0.9253, 0.8846, 0.9015, 0.9577, 0.8712, 0.9422, 0.8845, 0.8767, 0.9465, 1.0106, 0.7609, 0.8837, 0.963, 0.8652, 0.9033, 0.958, 0.9544, 0.9772, 1.007, 0.991, 1.0612, 0.967, 0.9619, 0.8641, 0.976, 0.897, 0.8328, 0.98, 1.024, 0.95, 0.9373, 0.9992, 1.0294, 1.0174, 1, 0.9663, 0.9136, 0.875, 0.8647, 0.8879, 0.925, 0.9098, 1.0355, 1.0423, 0.974, 1.0475, 0.9684, 0.9961, 0.8761, 1.043, 0.906, 1.0113, 0.9554, 0.8868, 0.9672, 0.9689, 0.7913, 0.9557, 0.95, 0.9241, 0.977, 0.9954, 0.9414, 0.9768, 0.9361, 1.012, 0.9797, 0.935, 0.9789, 0.9177, 0.9212, 0.9467, 1.0138, 0.996, 0.844, 0.9182, 0.988, 0.9479, 0.9296, 1.0091, 0.9145, 0.9084, 0.9838, 0.931, 0.8625, 0.8116, 0.975, 0.965, 0.8768, 0.996, 0.8978, 1.3968, 1.3513, 1.3796, 1.37, 1.511, 1.5374, 1.3402, 1.2161, 1.45, 1.3467, 1.6014, 1.4685, 1.4193, 1.5277, 1.404, 1.5426, 1.3524, 1.619, 1.5212, 1.4904, 1.3892, 1.3864, 1.4216, 1.298, 1.353, 1.3753, 1.4451, 1.411, 1.5489, 1.384, 1.4021, 1.3734, 1.3706, 1.2957, 1.3287, 1.3739, 1.4868, 1.3398, 1.4879, 1.376, 1.4362, 1.6883, 1.169, 1.438, 1.1338, 1.4425, 1.461, 1.4077, 1.4075, 1.5675, 1.2991, 1.361, 1.2582, 1.3556, 1.4687, 1.3709, 1.479, 1.2, 1.171, 1.4051, 1.3693, 1.3885, 1.3117, 1.4925, 1.382, 1.307, 1.5479, 1.3287, 1.463, 1.3607, 1.4868, 1.4895, 1.471, 1.478, 1.322, 1.3986, 1.5283, 1.3912, 1.3905, 1.2882, 1.2838, 1.3064, 1.2657, 1.4752, 1.33, 1.2807, 1.3757, 1.3521, 1.3615, 1.4647, 1.3665, 1.3926, 1.5306, 1.3466, 1.4485, 1.5256, 1.4598, 1.392, 1.4577, 1.569, 1.5389, 1.4609, 1.366, 1.4558, 1.4793, 1.483, 1.4778, 1.3583, 1.4369, 1.3851, 1.3002, 1.343, 1.396, 1.6054, 1.2581, 1.4681, 1.4902, 1.516, 1.2972, 1.5183, 1.1763, 1.5713, 1.5658, 1.5176, 1.3958, 1.5345, 1.4831, 1.5042, 1.475, 1.4894, 1.3109, 1.4947, 1.4008, 1.3924, 1.6255, 1.4301, 1.3511, 1.547, 1.417, 1.4545, 1.427, 1.575, 1.4619, 1.5714, 1.395, 1.6044, 1.5129, 1.4793, 1.4363, 1.5361, 1.5828, 1.4649, 1.5128, 1.4554, 1.4241, 1.5933, 1.487, 1.6123, 1.515, 1.661, 1.614, 1.604, 1.5179, 1.5463, 1.5053, 1.5174, 1.563, 1.5646, 1.529, 1.4507, 1.521, 1.3927, 1.4156, 1.4218, 1.4786, 1.4873, 1.528, 1.5404, 1.6069, 1.529, 1.524, 1.532, 1.3764, 1.365, 1.417, 1.42, 1.5018, 1.5171, 1.6861, 1.4826, 1.3477, 1.5042, 1.497, 1.3495, 1.5278, 1.5128, 1.4574, 1.5181, 1.5579, 1.55, 1.5066, 1.5624, 1.5097, 1.2975, 1.574, 1.6447, 1.5551, 1.6489, 1.575, 1.3699, 1.4815, 1.5128, 1.52, 1.4599, 1.5155, 1.5009, 1.525, 1.5682, 1.5374, 1.4216, 1.4754, 1.4921, 1.6045, 1.5672, 1.5415, 1.4147, 1.5007, 1.3903, 1.4811, 1.4684, 1.583, 1.5141, 1.5613, 1.4138, 1.3942, 1.5426, 1.5353, 1.5059, 1.5185, 1.4718, 1.557, 1.5503, 1.5809, 1.4571, 1.3876, 1.4999, 1.567, 1.6213, 1.4722, 1.6299, 1.5003, 1.5016, 1.3979, 1.5145, 1.368, 1.511, 1.419, 1.499, 1.4829, 1.5283, 1.3489, 1.5275, 1.2543, 1.5618, 1.4681, 1.475, 1.3351, 1.382, 1.5259, 1.5143, 1.5396, 1.4435, 1.6217, 1.5953, 1.45, 1.6084, 1.516, 1.4597, 1.3616, 1.595, 1.4594, 1.5034, 1.5266, 1.5228, 1.512, 1.5051, 1.4695, 1.5701, 1.6411, 1.3285, 1.4851, 1.483, 1.5085, 1.4766, 1.6053, 1.5442, 1.5624, 1.481, 1.56, 1.6188, 1.6365, 1.4982, 1.3872, 1.5787, 1.5001, 1.4118, 1.5, 1.664, 1.538, 1.5796, 1.6087, 1.6537, 1.5894, 1.5787, 1.5931, 1.5452, 1.4897, 1.4553, 1.5442, 1.5851, 1.5819, 1.5899, 1.6965, 1.6103, 1.6574, 1.5121, 1.5663, 1.474, 1.6411, 1.545, 1.68, 1.616, 1.5187, 1.5035, 1.5558, 1.453, 1.5854, 1.607, 1.6486, 1.6085, 1.5506, 1.5697, 1.5791, 1.5083, 1.6237, 1.5324, 1.6, 1.5703, 1.5518, 1.4557, 1.5142, 1.6592, 1.5913, 1.3739, 1.5366, 1.5575, 1.5205, 1.4785, 1.6111, 1.5861, 1.4798, 1.4806, 1.4898, 1.4108, 1.3855, 1.5362, 1.5827, 1.4556, 1.612, 1.4867, 2, 1.9875, 2.0456, 1.842, 2.1236, 2.1727, 2.0817, 1.7932, 2.101, 2.0199, 2.2014, 1.9665, 2.0714, 2.1465, 2.0089, 2.1927, 1.9458, 2.202, 2.2086, 2.0236, 1.9341, 1.9841, 1.9989, 1.9223, 1.9379, 2.0421, 2.0058, 1.9416, 2.137, 1.882, 1.898, 1.963, 1.9807, 1.834, 1.898, 2.0066, 2.1234, 1.915, 2.1505, 1.977, 2.0534, 2.228, 1.6904, 2.0385, 1.8062, 2.0204, 2.0615, 1.9617, 2.021, 2.165, 1.834, 2.049, 1.909, 2.0091, 2.1625, 2.0017, 2.242, 1.705, 1.8096, 1.9774, 1.9484, 1.9542, 1.9006, 2.1364, 1.9749, 1.9247, 2.1806, 1.8766, 2.0787, 2.0609, 2.1479, 2.072, 2.193, 2.141, 2, 2.0962, 2.2043, 2.0212, 2.0448, 1.7999, 1.7583, 1.9555, 1.9188, 2.1024, 1.9143, 1.9394, 2.0909, 1.9117, 1.9921, 2.0652, 1.9214, 1.9288, 2.1423, 1.9185, 2.061, 2.1143, 2.081, 1.9681, 2.0064, 2.1208, 2.1756, 2.0744, 2.0146, 2.0347, 2.0973, 2.1789, 2.0805, 2.016, 2.0443, 2.0145, 2.0485, 1.9404, 1.9174, 2.0835, 1.8177, 2.069, 2.0467, 2.1035, 1.9164, 2.132, 1.6446, 2.0955, 2.1768, 2.1103, 2.0296, 2.1719, 2.1433, 2.0995, 2.0743, 2.0314, 1.9498, 2.0753, 2.0269, 1.9466, 2.2078, 2.1043, 2.023, 2.1835, 2.1077, 2.0485, 2.0745, 2.107, 2.1725, 2.197, 2.073, 2.2474, 2.1259, 2.147, 2.0411, 2.179, 2.2463, 2.1625, 2.2131, 2.1112, 2.0768, 2.115, 2.1757, 2.1993, 2.215, 2.238, 2.302, 2.201, 2.0725, 2.1555, 2.1632, 2.1028, 2.109, 2.1559, 2, 2.0812, 2.0636, 1.9518, 2.0405, 1.9671, 2.0485, 2.1215, 2.1124, 2.3113, 2.1994, 2.2434, 2.091, 2.116, 2.0227, 1.8781, 1.8379, 2.122, 2.2, 2.167, 2.3945, 2.0258, 1.8949, 2.1335, 2.0854, 1.9336, 2.134, 2.0199, 2.0615, 2.0121, 2.2198, 2.1395, 2.2243, 2.1077, 2.1057, 1.7483, 2.18, 2.2803, 2.2239, 2.391, 2.0472, 2.0262, 2.1252, 2.1381, 2.1761, 2.0419, 2.0137, 2.118, 2.1554, 2.138, 2.1339, 2.0934, 2.043, 2.1349, 2.1876, 2.1754, 2.14, 2.1462, 2.134, 1.9915, 2.1273, 2.0927, 2.2647, 2.2311, 2.1182, 2.015, 1.9269, 2.1666, 2.2637, 2.1654, 2.1667, 2.0613, 2.166, 2.176, 2.1652, 2.1003, 2.06, 2.0848, 2.2029, 2.2304, 2.0783, 2.2003, 2.2126, 2.0313, 2.1385, 2.2015, 2.0062, 1.9707, 2.0471, 2.0847, 2.0912, 2.1259, 2.0132, 2.2313, 1.932, 2.1384, 2.0722, 2.1778, 1.9665, 2.0136, 2.2123, 2.1932, 2.3374, 2.159, 2.2233, 2.1087, 2.179, 2.216, 2.1935, 1.9536, 2.0363, 2.1864, 2.1038, 2.1941, 2.245, 2.16, 2.1348, 2.1572, 2.1454, 2.1742, 2.2023, 2.0934, 2.1621, 2.0701, 2.16, 2.1475, 2.2273, 2.2448, 2.1836, 2.175, 2.3232, 2.2565, 2.284, 2.1939, 2.0941, 2.2709, 2.1029, 1.9158, 2.19, 2.379, 2.173, 2.2437, 2.2624, 2.2093, 2.2531, 2.25, 2.248, 2.2174, 2.1297, 2.0173, 2.1263, 2.208, 2.0977, 2.2609, 2.3667, 2.201, 2.2743, 2.1338, 2.2251, 2.102, 2.2577, 2.1854, 2.3493, 2.159, 2.168, 2.2375, 2.2589, 2.0628, 2.132, 2.1699, 2.3285, 2.192, 2.2133, 2.2179, 2.3002, 2.208, 2.2856, 2.0855, 2.25, 2.1735, 2.1595, 2.0881, 2.1715, 2.2986, 2.2804, 2.0636, 2.2535, 2.0939, 2.208, 2.1907, 2.2991, 2.217, 2.1691, 2.11, 2.0292, 2.011, 2.0168, 2.1483, 2.111, 2.0586, 2.3473, 2.1486, 2.5865, 2.4405, 2.5577, 2.456, 2.41, NA, 2.6811, 2.3779, 2.7572, 2.5393, 2.7288, NA, 2.6591, 2.8193, 2.5132, 2.654, 2.4863, 2.7741, 2.7377, 2.6767, 2.4071, 2.4123, NA, 2.356, 2.52, 2.6631, 2.4839, 2.4638, 2.59, 2.4628, 2.49, 2.643, 2.5499, 2.533, 2.466, 2.5145, 2.5911, 2.515, 2.7347, 2.626, 2.5641, 2.7665, 2.298, 2.563, 2.5209, 2.5408, 2.6245, 2.4907, 2.4728, 2.7138, 2.272, 2.6, 2.537, 2.6142, 2.7606, 2.4786, 2.7479, 2.2775, 2.3125, 2.4968, 2.4808, 2.4446, 2.4036, 2.7649, 2.543, 2.508, 2.594, 2.4322, 2.6081, NA, NA, 2.5826, 2.7014, 2.7417, NA, 2.584, 2.6155, 2.5948, 2.4967, 2.4392, 2.2845, 2.553, 2.4747, 2.5312, 2.4126, 2.4536, 2.6231, 2.5226, 2.5916, 2.585, 2.519, 2.5179, 2.6081, 2.4572, 2.5614, 2.6855, 2.6177, 2.5052, 2.5431, 2.7309, 2.7998, 2.5526, 2.554, 2.5689, 2.7456, 2.6054, 2.6992, 2.4852, 2.6347, 2.582, 2.5281, 2.76, 2.5921, 2.6748, 2.317, 2.6102, 2.6204, 2.6192, 2.3701, 2.5123, 2.2612, 2.6544, 2.7331, 2.7146, 2.5822, 2.6972, 2.7309, 2.7028, 2.6928, 2.5, 2.5893, 2.5169, 2.6186, 2.55, 2.7712, 2.6865, 2.562, 2.8347, 2.6291, 2.6815, 2.8005, 2.6727, 2.8052, 2.8221, 2.6114, 2.8143, 2.6318, 2.6814, 2.5498, 2.6703, 2.7644, 2.6983, 2.7169, 2.6292, 2.6242, 2.6612, 2.7603, 2.814, 2.7986, 2.811, 2.9153, 2.8306, 2.6517, 2.6886, 2.7007, 2.706, 2.6291, 2.7014, 2.6002, 2.6714, 2.6875, 2.5713, 2.5788, 2.5904, 2.6389, 2.7355, 2.6855, 2.8236, 2.486, 2.7896, 2.5527, 2.6436, 2.4531, 2.3533, 2.5682, NA, 2.795, 2.8655, 2.9534, 2.6312, 2.3748, 2.7729, 2.5586, 2.44, 2.6624, 2.5382, 2.5475, 2.4938, 2.6734, 2.6095, 2.6717, 2.6366, 2.5472, 2.3864, 2.7903, 2.841, 2.7699, 2.8326, 2.52, 2.5861, 2.669, 2.8013, 2.7076, 2.5727, 2.5239, 2.5757, NA, 2.7622, 2.6532, 2.6589, 2.6235, 2.7259, 2.6283, 2.6429, 2.66, 2.6561, 2.702, 2.4816, 2.5144, 2.7054, 2.7225, 2.667, 2.6976, 2.6131, 2.5205, 2.7091, NA, NA, 2.6766, 2.5982, NA, 2.7091, 2.8495, 2.6441, 2.7945, 2.6637, 2.7352, 2.7684, 2.7396, 2.7413, 2.8885, 2.6273, 2.7283, 2.8645, 2.5978, 2.6144, 2.6942, 2.7407, 2.7534, 2.8245, 2.4704, 2.9521, 2.642, 2.724, 2.7014, 2.8919, 2.575, 2.6281, 2.677, 2.7407, 2.973, 2.8089, 2.83, 2.6453, 2.7818, 2.7043, 2.8093, 2.4955, 2.6775, 2.9532, 2.1434, 2.6778, 2.8586, 2.8543, 2.6881, 2.7275, 2.7866, 2.8864, 2.7892, 2.6896, 2.8, 2.7107, 2.836, 2.7149, 2.8089, 2.9778, NA, 2.7841, 3.0114, 2.8524, 2.732, 2.8007, 2.7841, 2.8254, 2.7324, 2.5185, 2.98, NA, 2.813, 2.7309, 2.9303, 2.8687, NA, 2.8316, 2.8766, 2.7308, 2.7438, 2.6303, 2.6875, 2.6631, 2.7013, NA, 3.0026, 2.681, 2.9199, 2.7356, NA, 2.5868, 2.8237, 2.7014, 2.9015, 2.9353, 2.7509, 2.8947, 2.9439, NA, NA, 2.852, NA, NA, 2.7421, 2.8189, 2.8385, 2.7892, 3.0434, 2.6936, 2.966, 2.8017, 2.8051, 2.8421, 2.8422, 2.9182, 2.9074, 2.6816, NA, 2.6885, 2.7776, 2.6932, 2.8139, 2.7443, 2.8721, 2.7336, 2.6389, 2.6348, 2.5962, 2.6568, NA, NA, NA, NA)), .Names = c("ID", "TRAT", "SEMANA", "PESO"), row.names = c(NA, -2590L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = PESO ~ SEMANA | ID, FUN = function (x) max(x, na.rm = TRUE), order.groups = TRUE) #------------------------------------------------------------- modelo1.list <- nlsList(PESO ~ a * exp(-b * exp( -c * SEMANA)),start = c(a = 4.10,b = 4.53,c = 0.37 ),na.action=na.omit,data =GALINHA) modelo.nlme.0<-nlme(modelo1.list) controle<-nlmeControl(maxIter=300,msMaxIter = 300,niterEM= 100) modelo.nlme.1.bc<-update(modelo.nlme.0,fixed=list(a~TRAT,b+c~1), correlation=corSymm(), weights=varIdent(form=~1|SEMANA), start=c(4.70,0,0,4.6,0.34), control=controle) #----------------------------------------------------------------------------------- panel.function <- function(x,y,subscript,...){ panel.xyplot(x,y,...) panel.lines(GALINHA$SEMANA[subscript],predict(modelo.gompertz.1.SYMM.IDENT,newdata = GALINHA[subscript,]),lwd = 4,col = "black") } xyplot(PESO~SEMANA |TRAT,groups = ID,type = "l", panel =panel.function,data = DADOS) ========================================= -- ========================================= Fernando Souza Zootecnista, DSc. Produção e Alimentação Animal Celular: (31)99796-8781 (Vivo) E-mail:nandodesouza@gmail.com <e-mail%3Anandodesouza(a)gmail.com> Lattes: http://lattes.cnpq.br/6519538815038307 Blog: https://producaoanimalcomr.wordpress.com/ ==========================================

2 1