Oi tenta usar esses dois pacote e o comando cov require(mvnormtest) require(mvsf) dados <- read.table("campanha.txt", header = T) ## lendo um conjunto de dados em txt ## dados attach(dados) S <- cov(dados,dados) #matriz de variancia e covariancia S Att. Edilza ________________________________ De: R-br <r-br-bounces@listas.c3sl.ufpr.br> em nome de Fernando Souza via R-br <r-br@listas.c3sl.ufpr.br> Enviado: quarta-feira, 13 de setembro de 2017 18:02 Para: r-br@listas.c3sl.ufpr.br Assunto: [R-br] Modelo Misto - Matrix variancia covariancia [Imagem inline 1] Caros amigos, Como posso obter uma tabela semelhante. Estou analizando um modelo não linear misto (Gompertz) da forma Peso~SEMANA 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, 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"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, 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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, 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6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 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, 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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<-update(modelo.nlme.0,fixed=list(a+b+c~TRAT),start=c(4.10,0,0,4.77,0,0,0.34,0,0),control=controle) ========================================= Fernando Souza Zootecnista, DSc. Produção e Alimentação Animal Celular: (31)99796-8781 (Vivo) E-mail:nandodesouza@gmail.com<mailto:e-mail%3Anandodesouza@gmail.com> Lattes: http://lattes.cnpq.br/6519538815038307 Blog: https://producaoanimalcomr.wordpress.com/ ==========================================