Dezembro 2017 - R-br

[OFF -Tópic] Cotas em concursos para negros e índios!
by Andre Oliveira 12 Jun '49

12 Jun '49

Pessoal, tem um colegar meu que vou fazer um concurso ele é descendente de negro ele é pardo. Ele veio com a pergunta como provar que sou negro, pois, tem vagas reservada para negros e índios? Eu sinceramente não sei e não tenho ideia pois nunca tive esta curiosidade. O que de fato comprova sua raça? Algum documento? O que são usados em concursos para provar? obrigado André Oliveira Souza

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Legenda eixo X
by Luciano F. Sgarbi 10 Jun '49

10 Jun '49

Prezados, Estou tentando centralizar a legenda do eixo X em uma figura com vários gráficos. Estou conseguindo centralizar a legenda da linha inferior como no exemplo, mas as linhas do meio não. par(mfrow=c(2,2)) plot(rnorm(100),xlab="",ylab="y1") plot(rnorm(100),xlab="",ylab="y2") mtext(side=1,cex=2,line=-1.8,text="Variável Explanatória 1",outer=TRUE) ### o problema está aqui, pois a legenda desce para a parte inferior da figura. plot(rnorm(100),xlab="",ylab="y1") plot(rnorm(100),xlab="",ylab="y2") mtext(side=1,cex=2,line=-1.8,text="Variável Explanatória 2",outer=TRUE)## Esse dá certo Alguém podia dar uma luz de como eu posso fazer isso? Abraços -- *Luciano F. Sgarbi* Mestrando em Ecologia e Evolução Laboratório de Ecologia de Insetos (sl. 222) Departamento de Ecologia Instituto de Ciências Biológicas - ICB 1 Universidade Federal de Goiás, campus II Goiânia-GO 74001-970 Brazil

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Diferença resultados agricolae::HSD.test e lsmeas
by Fernando Antonio de souza 04 Nov '18

04 Nov '18

Pessoal, poque o resultado dos testes HSD.test e lsmeans diferem neste exemplo? O resultado fornecido pelo lsmeas está em acordo com o material de onde tirei esse exemplo e que havia sido rodado no SAS. Já o HSD.test difere tanto no valor das médias estimadas como na significancia. install.packages(c("agricolae",lsmeans")) library(agricolae) library(lsmeans) COVARIANCIA<-read.csv(" https://www.dropbox.com/s/0uvwqnnvsdsvphc/Covariancia.csv?raw=1",head=TRUE) modeloCOVAR<-lm(GANHO~PESOINIC+DIETA,data=COVARIANCIA) anova(modeloCOVAR) HSD.test(modeloCOVAR,"DIETA",console=TRUE) lsmeans(modeloCOVAR,pairwise~DIETA,adjust="tukey") -- ========================================= 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/ ==========================================

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Salvar saídas de um loop
by Yury Duarte 09 Fev '18

09 Fev '18

Boa tarde colegas listeiros! Estou tendo um pequeno problema para salvar as saídas de um loop que fiz para preencher uma matriz com dados de diferentes arquivos. Meus arquivos base possuem informações anuais de produtividade de 15 locais para um determinado mês. Construí o loop com intenção de gerar um arquivo por local, contemplando as produtividades de cada mês (nas linhas) e de cada ano (nas colunas). Conferi o output do único arquivo salvo pelo script e os valores correspondem ao desejado, então acredito que a lógica para o preenchimento da matriz em branco esteja correta. Os arquivos para executar a simulação estão em anexo e o script está no corpo do email. Desde já, agradeço pela colaboração de todos! rm(list = ls()) raiz = 'C:\\Users\\Yury\\Desktop\\Mestrado\\1_TESE\\Model_Data\\Maize\\PREVISAO\\PA\\' estrategia = c('E1', 'E2', 'E3', 'E4', 'E5') meses = c('setembro', 'outubro', 'novembro', 'dezembro', 'janeiro', 'fevereiro', 'marco', 'abril') cidade = c("abelardoluz", "altamira", "brasilia", "caarapo", "catalao", "lagarto", "machado", "mateiros", "muitoscapoes", "piracicaba", "pontagrossa", "primaveradoleste", "srmangab", "sdesiderio", "urucui") anos = c(1980:2012) previsao = matrix(NA, nrow = length(meses), ncol = length(1980:2012)) colnames(previsao) = 1980:2012 #for(e in 1:length(estrategias)){ for(m in 1:length(meses)){ a = read.table(paste0(raiz, 'E1\\', meses[m],'\\', 'final.txt'), header = T, sep = '\t') for(city in 1:length(cidade)){ for(i in 1:length(anos)){ previsao[m,i] = a[city,i] } } write.table(previsao, paste0(raiz, 'E1\\', cidade[city],'.txt'), row.names = F, col.names = T, sep = '\t') } #} Yury Duarte Engenheiro Agrônomo - ESALQ/USP

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Especialização em Data Science & Big Data em Curitiba - PR
by Elias T. Krainski 28 Dez '17

28 Dez '17

Caro(a)s, Alguns professores dos Departamentos de Estatística e de Ciências da Computação da Universidade Federal do Paraná estão unindo forças para abrir uma turma de Especialização em *Data Science & Big Data.* Maiores detalhes estão disponíveis em http://dsbd.leg.ufpr.br/ Att. Elias PS.: Justifico este post nesta lista por estar indiretamente associado ao R, uma vez que será uma das linguagens lecionadas :)

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R Day - Encontro nacional de usuários do R
by Fernando Mayer 20 Dez '17

20 Dez '17

Car@s redistas, é com grande satisfação que anunciamos o R Day - Encontro nacional de usuários do R, um evento para congregar usuários e desenvolvedores de R em um ambiente para troca de experiências, aprendizado e contatos. O R Day acontecerá na UFPR, Curitiba (PR), no dia 22 de maio de 2018 como um evento satélite da 63ª RBras. Sendo este o primeiro evento de R no Brasil endossado oficialmente pela The R Foundation, o objetivo do R Day é reunir a ativa e grande, embora esparsa, comunidade de usuários de R no Brasil. Para atender a todos os públicos, estão programadas 3 conferências, 9 apresentações orais (short-talks) e 8 tutoriais. As apresentações orais serão voltadas para demonstração de pacotes (vinheta falada) ou estudos de caso "out of the box" relatando desafios superados com uso do R, tanto no escopo acadêmico quanto profissional. As apresentações terão duração de 15 min + 5 min para discussão. Os tutoriais terão 1 hora de duração e foram separados em duas linhas que correrão simultaneamente: uma para usuários iniciantes e outra para usuários experientes ou desenvolvedores. Alguns temas de tutorias dentro de cada linha foram sugeridos pela comissão organizadora apenas para orientação dos interessados. No entanto, serão aceitas propostas em temas diferentes. As conferências enfatizarão temas de ampla abrangência sobre R a sua comunidade de usuários. Diferentemente das demais modalidades, os conferencistas serão definidos pela comissão organizadora. Sugestões são bem vindas. Sintam-se todos convidados e estimulados a submeter propostas para apresentações orais e tutoriais. Almejamos a colaboração de todos para que o evento reuna o maior número possível de usuários do R com a intenção de promover a troca de conhecimento, recursos e contatos, além de ampliar e fortalecer nossa comunidade. O respaudo da R Foundation para realização desse evento demonstra o prestígio e reconhecimento da comunidade brasileira de usuários. Esse evento configura uma abertura de campanha para a realização de uma conferência userR! no Brasil nos próximos anos. Por isso, seu apoio e participação são fundamentais. A submissão de propostas está aberta até o dia 08/04/2018 via preenchimento de formulário online. Como R Day é um evento sem fins lucrativos e de inscrição gratuita, sua realização depende de recursos providos por patrocinadores. Aqueles que podem prestar auxílio, seja financeiro, logístico, publicitário, etc, são convidados a entrar em contato com a comissão organizadora. Todas as informações necessárias estão disponíveis no site: http://rday.leg.ufpr.br Para atualizações, acompanhe o Twitter do LEG: @LEG_UFPR e siga a #rdayCWB. Mensagens de apoio e sugestões para o evento podem ser feitos pelo Twitter ou email. Atenciosamente, Fernando Mayer P/ Comissão organizadora do R Day --- Fernando de Pol Mayer Laboratório de Estatística e Geoinformação - LEG Departamento de Estatística - DEST Universidade Federal do Paraná - UFPR URL: http://leg.ufpr.br/~fernandomayer e-mail: fernando.mayer [@] {gmail.com, ufpr.br}

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leitura de arquivo de foto em formato dng (raw)
by Cleber N.Borges 19 Dez '17

19 Dez '17

Olá a todos... Bom dia... Gostaria de ler arquivos de fotos em DNG pelo R. Verifiquei que usando a função readTiff ( pacote tiff ) é possível carregar a matriz com os dados... Porém pela documentação da função, eu fiquei em dúvida se realmente não há nenhum processamento das intensidades capturados pelo sensor de luz da câmera... Alguém tem alguma de dica de como acessar essa informação contida nos arquivos DNG com o R? Pode ser alguma dica que use algum outro software para passos intermediários... mas no final gostaria de produzir matrizes com o R... Obrigado Cleber Borges ps.: imagens serão produzidas smartphone samsung s7 edge --- Este email foi escaneado pelo Avast antivírus. https://www.avast.com/antivirus

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Sigularidade backsolve, o que significa?
by Fernando Souza 15 Dez '17

15 Dez '17

Caros Amigos o que pode significar essa mensagem em modelos não linear mixto (nlme)? Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 -- ========================================= 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/ ==========================================

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FlexParamCurve
by Marcelo Laia 14 Dez '17

14 Dez '17

Alguém usa ou já usou? https://cran.r-project.org/web/packages/FlexParamCurve/index.html -- Marcelo

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Dúvida gráfico lattice da função plot.nlme
by Fernando Souza 13 Dez '17

13 Dez '17

Caros colegas, Devido ao elevado número de individuos avaliados há uma sobreposição dos identificados no eixo y do gráfico, o que o torna incompreensível. Como posso organizar esses indicadores? Ou como posso removê-los. library(nlme) library(lattice) 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, 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"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", 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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) #-------------------------------ESTIMATIVA DOS PARAMETROS----------- 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) #--------------------------GRÁFICO------------------------------------ plot(intervals(modelo1.list),layout = c(3,1)) -- ========================================= 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/ ==========================================

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