[R-br] Modelos Mistos - Duvida sobre =?utf-8?Q?transforma=C3=A7=C3=A3o/distribui=C3=A7=C3=A3o_?=logistica

Seg Mar 4 22:59:32 -03 2019

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 em 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)
>
>
>
> _______________________________________________
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