Re: [R-br] Modelos Mistos - Duvida sobre transformação/distribuição logistica
Olá Mauro, É porque esqueci de colocar o comando que gera a variável controle. Agora deve funcionar controle=lmeControl(maxIter=200,msMaxIter=200,opt="optim") 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")) On Mar 4 2019, at 10:51 pm, Mauro Sznelwar por (R-br) <r-br@listas.c3sl.ufpr.br> wrote:
Por que não estou conseguindo rodar?
# 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)) Error in lme.formula(fixed = CT ~ GS + Tratamento * Semana + Pbserica + : objeto 'controle' não encontrado anova(modeloCT_F1.4,modeloCT_F1.5) Error in anova.lme(modeloCT_F1.4, modeloCT_F1.5) : objeto 'modeloCT_F1.5' não encontrado
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, 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, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 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, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 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, 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, 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, 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, 79, 79, 83, 81, 83, 86, 90, 75, 75, 76,
77, 79, 81, 86, 87, 92, 71, 72, 73, 75, 78, 85, 86, 91, 75,
77, 77, 80, 81, 86, 89, 91, 97, 77, 78, 78, 79, 84, 86, 90,
96, 99, 78, 80, 80, 80, 84, 84, 87, 92, 97, 74, 77, 78, 79,
81, 83, 84, 92, 72, 74, 75, 79, 79, 80, 86, 85, 91, 75, 76,
76, 78, 78, 80, 84, 86, 90, 75, 77, 77, 80, 81, 83, 88, 90,
94, 71, 72, 70, 74, 78, 91, 85, 86, 97, 71, 72, 75, 76, 77,
80, 84, 86, 91, 70.5, 75, 73, 77, 80, 84, 86, 87, 92, 75,
71, 77, 76, 80, 80, 83, 84, 90, 71, 77, 75, 76, 77, 78, 82,
84, 89, 74, 77, 78, 79, 80, 82, 90, 89, 94, 77, 79, 79, 81,
83, 83, 86, 90, 95, 76, 78, 78, 80, 82, 82, 85, 86, 70, 73,
72, 71, 77, 74, 75, 77, 80, 77, 81, 81, 82, 85, 86, 89, 93,
98, 73, 73, 70, 73, 75, 77, 81, 85, 89, 71, 74, 74, 78, 77,
82, 84, 88, 90, 82, 80, 82, 85, 89, 89, 93, 95, 98, 76, 80,
79, 80, 81, 84, 88, 89, 93, 77, 77, 77, 80, 82, 82, 85, 86,
91, 68, 74, 75, 75, 76, 78, 80, 81, 86, 68, 69, 69, 74, 77,
79, 80, 82, 85, 68, 72, 73, 75, 77, 78, 80, 84, 88, 71, 72,
72, 76, 78, 80, 81, 85, 90, 77, 78, 80, 80, 83, 85, 88, 90,
94, 76, 77, 77, 79, 82, 83, 84, 89, 93, 88, 76, 78, 79, 81,
83, 85, 89, 94)), class = "data.frame", row.names = c(1L,
2L, 4L, 6L, 8L, 10L, 12L, 14L, 17L, 18L, 20L, 23L, 25L, 27L,
29L, 31L, 34L, 35L, 37L, 39L, 41L, 43L, 45L, 47L, 49L, 52L, 53L,
55L, 57L, 61L, 63L, 65L, 67L, 70L, 71L, 73L, 75L, 77L, 79L, 81L,
83L, 85L, 88L, 89L, 91L, 93L, 95L, 97L, 99L, 101L, 103L, 106L,
107L, 109L, 111L, 113L, 115L, 117L, 119L, 121L, 124L, 125L, 127L,
133L, 135L, 137L, 139L, 142L, 143L, 145L, 147L, 149L, 151L, 153L,
155L, 157L, 160L, 161L, 163L, 165L, 167L, 169L, 171L, 173L, 178L,
179L, 181L, 183L, 185L, 187L, 189L, 191L, 193L, 196L, 197L, 199L,
201L, 203L, 205L, 207L, 209L, 211L, 214L, 215L, 217L, 219L, 221L,
223L, 225L, 227L, 229L, 233L, 235L, 237L, 239L, 241L, 243L, 245L,
247L, 250L, 251L, 253L, 255L, 257L, 259L, 261L, 263L, 265L, 268L,
269L, 271L, 273L, 275L, 277L, 279L, 283L, 286L, 287L, 289L, 291L,
293L, 295L, 297L, 299L, 301L, 304L, 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)
_______________________________________________ R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br (https://link.getmailspring.com/link/E18142D3-39E0-410F-977B-A9A1B7F293E6@get...) Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia (https://link.getmailspring.com/link/E18142D3-39E0-410F-977B-A9A1B7F293E6@get...)) e forne�a c�digo m�nimo reproduz�vel.
_______________________________________________ R-br mailing list R-br@listas.c3sl.ufpr.br https://listas.inf.ufpr.br/cgi-bin/mailman/listinfo/r-br Leia o guia de postagem (http://www.leg.ufpr.br/r-br-guia) e forne�a c�digo m�nimo reproduz�vel.
participantes (2)
-
Fernando Souza -
sznelwar＠uol.com.br