Prezados Estou analisando um dado experimental utilizando a abordagem dos modelos mistos, através da função lme do pacote nlme. O dado no entanto não apresentou distribuição normal e tentativas como remoção de outliers, transformação foram infrutíferas. Tentei olhar se outra distribuição se ajusta melhor aos dados e encontrei que a distribuição logistica, se ajusta bem (Veja imagens abaixo), porém não sei como ajustar esse modelo utilizando a abordagem de modelos mistos. Visto que meus dados não são dados de proporção, como posso fazer para analisar tal dado? Desculpe a quantidade de informação fornecida abaixo, mas este é o mínimo que consegui organizar para ilustrar minha questão BiometriaAleit<-structure(list(Animal = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 44L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L), .Label = c("1", "9", "24", "25", "41", "49", "8016", "8019", "8020", "8024", "8025", "8027", "8028", "8029", "8030", "8031", "8032", "8034", "8035", "8037", "8040", "8042", "8058", "8067", "8068", "8109", "8111", "8112", "8114", "8116", "8124", "8128", "8130", "8137", "8405", "8407", "8415", "8420", "8422", "8424", "8425", "8426", "8429", "8434", "8436"), class = "factor"), Sexo = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Femea", "Macho"), class = "factor"), GS = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("3/4hz", "5/8hz", "HOL"), class = "factor"), Tratamento = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 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, 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("apex", "controle" ), class = "factor"), Pbserica = c(11, 11, 11, 11, 11, 11, 11, 11, 11, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11.9, 11.9, 11.9, 11.9, 11.9, 11.9, 11.9, 11.9, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10, 10, 10, 10, 10, 10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, 12, 10, 10, 10, 10, 10, 10, 10, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 12.4, 12.4, 12.4, 12.4, 12.4, 12.4, 12.4, 12.4, 12.4, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 10.9, 10.9, 10.9, 10.9, 10.9, 10.9, 10.9, 10.9, 10.9, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 9.7, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 12, 12, 12, 12, 12, 12, 12, 12, 12, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, 10.8, 10.8, 10.8, 10.8, 10.8, 10.8, 10.8, 10.8, 10.8, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4), Pnasc = c(27.06, 27.06, 27.06, 27.06, 27.06, 27.06, 27.06, 27.06, 27.06, 38.34, 38.34, 38.34, 38.34, 38.34, 38.34, 38.34, 38.34, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, 32.65, 32.65, 32.65, 32.65, 32.65, 32.65, 32.65, 32.65, 39.7, 39.7, 39.7, 39.7, 39.7, 39.7, 39.7, 39.7, 39.7, 35.9, 35.9, 35.9, 35.9, 35.9, 35.9, 35.9, 35.9, 35.9, 34, 34, 34, 34, 34, 34, 34, 34, 34, 28.58, 28.58, 28.58, 28.58, 28.58, 28.58, 28.58, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 32.314, 32.314, 32.314, 32.314, 32.314, 32.314, 32.314, 32.314, 32.314, 27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 35, 35, 35, 35, 35, 35, 35, 35, 26.8, 26.8, 26.8, 26.8, 26.8, 26.8, 26.8, 26.8, 26.8, 36.6, 36.6, 36.6, 36.6, 36.6, 36.6, 36.6, 36.6, 36.6, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 29.45, 29.45, 29.45, 29.45, 29.45, 29.45, 29.45, 29.45, 29.45, 34.48, 34.48, 34.48, 34.48, 34.48, 34.48, 34.48, 34.48, 34.48, 31.76, 31.76, 31.76, 31.76, 31.76, 31.76, 31.76, 31.76, 31.76, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 25.9, 25.9, 25.9, 25.9, 25.9, 25.9, 25.9, 25.9, 25.9, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 35.2, 35.2, 35.2, 35.2, 35.2, 35.2, 35.2, 35.2, 35.2, 31, 31, 31, 31, 31, 31, 31, 31, 31, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 29.49, 29.49, 29.49, 29.49, 29.49, 29.49, 29.49, 29.49, 29.49, 30, 30, 30, 30, 30, 30, 30, 30, 30, 33.12, 33.12, 33.12, 33.12, 33.12, 33.12, 33.12, 33.12, 33.12, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35.7, 35.7, 35.7, 35.7, 35.7, 35.7, 35.7, 35.7, 30.7, 30.7, 30.7, 30.7, 30.7, 30.7, 30.7, 30.7, 30.7, 39.3, 39.3, 39.3, 39.3, 39.3, 39.3, 39.3, 39.3, 39.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, 25.72, 25.72, 25.72, 25.72, 25.72, 25.72, 25.72, 25.72, 25.72, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 38.9, 38.9, 38.9, 38.9, 38.9, 38.9, 38.9, 38.9, 38.9, 38.2, 38.2, 38.2, 38.2, 38.2, 38.2, 38.2, 38.2, 38.2, 31.1, 31.1, 31.1, 31.1, 31.1, 31.1, 31.1, 31.1, 31.1, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 22.3, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 27.6, 31.7, 31.7, 31.7, 31.7, 31.7, 31.7, 31.7, 31.7, 31.7, 34.7, 34.7, 34.7, 34.7, 34.7, 34.7, 34.7, 34.7, 34.7, 38, 38, 38, 38, 38, 38, 38, 38, 38, 40.7, 40.7, 40.7, 40.7, 40.7, 40.7, 40.7, 40.7, 40.7), Semana = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 5L, 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2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8", "9"), class = "factor"), CT = c(70, 72, 73, 77, 77, 80, 82, 86, 91, 77, 78, 82, 84, 85, 89, 94, 95, 78, 81, 82, 81, 84, 88, 89, 92, 96, 74, 75, 77, 84, 85, 86, 88, 92, 76, 78, 79, 79, 82, 82, 84, 86, 88, 75, 79, 80, 80, 81, 83, 86, 89, 92, 76, 73, 75, 79, 81, 82, 87, 87, 94, 71, 73, 77, 82, 86, 88, 94, 75, 76, 80, 80, 83, 85, 88, 91, 98, 78, 78, 81, 81, 85, 85, 89, 94, 73, 74, 78, 78, 78, 81, 84, 86, 90, 72, 74, 75, 76, 78, 81, 86, 86, 90, 75, 80, 82, 84, 80, 88, 91, 94, 75, 78, 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305L, 307L, 309L, 311L, 313L, 315L, 317L, 319L, 322L, 323L, 325L, 327L, 329L, 331L, 333L, 335L, 337L, 340L, 341L, 343L, 345L, 347L, 349L, 351L, 353L, 358L, 359L, 361L, 363L, 365L, 367L, 369L, 371L, 373L, 376L, 377L, 379L, 381L, 383L, 385L, 387L, 389L, 391L, 394L, 395L, 397L, 399L, 401L, 403L, 405L, 407L, 409L, 412L, 413L, 415L, 417L, 419L, 421L, 423L, 425L, 427L, 430L, 431L, 433L, 435L, 437L, 439L, 441L, 443L, 445L, 448L, 449L, 451L, 453L, 455L, 457L, 459L, 461L, 463L, 466L, 467L, 469L, 471L, 473L, 475L, 477L, 479L, 481L, 484L, 485L, 487L, 489L, 491L, 493L, 495L, 497L, 499L, 502L, 503L, 505L, 507L, 509L, 511L, 513L, 515L, 517L, 520L, 521L, 523L, 525L, 527L, 529L, 531L, 533L, 535L, 538L, 539L, 543L, 545L, 547L, 549L, 551L, 553L, 556L, 557L, 559L, 561L, 563L, 565L, 567L, 569L, 571L, 574L, 575L, 577L, 579L, 581L, 583L, 585L, 587L, 589L, 591L, 592L, 594L, 596L, 598L, 600L, 602L, 604L, 606L, 608L, 609L, 611L, 613L, 615L, 617L, 619L, 621L, 623L, 626L, 627L, 629L, 631L, 633L, 635L, 637L, 639L, 641L, 644L, 645L, 647L, 649L, 651L, 653L, 655L, 657L, 659L, 661L, 662L, 664L, 666L, 668L, 670L, 672L, 674L, 676L, 679L, 680L, 682L, 684L, 686L, 688L, 690L, 692L, 694L, 697L, 698L, 700L, 702L, 704L, 706L, 708L, 710L, 712L, 715L, 716L, 718L, 720L, 722L, 724L, 726L, 728L, 730L, 733L, 734L, 736L, 738L, 740L, 742L, 744L, 746L, 748L, 751L, 752L, 754L, 756L, 758L, 760L, 762L, 764L, 766L, 769L, 770L, 772L, 774L, 776L, 778L, 780L, 782L, 784L, 787L, 788L, 790L, 792L, 794L, 796L, 798L, 800L, 802L, 805L)) modeloCT_F<- lme(CT~GS+Tratamento*Semana+Pbserica+Pnasc,random=~1|Animal,data=subset(BiometriaAleit,Sexo=="Femea")) shapiro.test(resid(modeloCT_F,type="normalized")) bartlett.test(resid(modeloCT_F,type="normalized")~interaction(Tratamento,Semana),data=subset(BiometriaAleit,Sexo=="Femea")) # Modelando a variância e a covariancia dos erros modeloCT_F1.4 <- update(modeloCT_F,weights=varIdent(form=~1|Semana*Tratamento)) modeloCT_F1.5 <- update(modeloCT_F1.4,control=controle,correlation=corARMA(q=3,form=~1|Animal)) anova(modeloCT_F1.4,modeloCT_F1.5) shapiro.test(resid(modeloCT_F1.5,type="normalized")) bartlett.test(resid(modeloCT_F1.5,type="normalized")~interaction(Tratamento,Semana),data=subset(BiometriaAleit,Sexo=="Femea")) # Remoção de outliers modeloCT_F1.6 <- update(modeloCT_F1.5,data=subset(BiometriaAleit[-c(574,571,556,427,425,319,279,788,430,423),],Sexo=="Femea")) shapiro.test(resid(modeloCT_F1.6,type="normalized")) bartlett.test(resid(modeloCT_F1.6,type="normalized")~interaction(Tratamento,Semana),data=subset(BiometriaAleit[-c(574,571,556,427,425,319,279,788,430,423),],Sexo=="Femea")) # Selecionando a melhor distribuição install.packages("fitdistrplus") library(fitdistrplus) x1<-as.vector(resid(modeloCT_F1.6,type="normalized")) descdist(x1, discrete = FALSE) Graficos sobre a distribuição dos dados. library(car) qqp(resid(modeloCT_F1.6,type="normalized"),"logis") #install.packages("fitdistrplus") library(fitdistrplus) x1<-as.vector(resid(modeloCT_F1.6,type="normalized")) descdist(x1, discrete = FALSE)