MODELO 1: E(y) = (β0 + b0) + B1*Tempo Reta única. Intercepto aleatório - PDF

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MODELO 1: E(y) = (β0 + b0) + B1*Tempo Reta única. Intercepto aleatório 1 mod1.lme summary(mod1.lme) Formula: ~1 indiv (Intercept)

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MODELO 1: E(y) = (β0 + b0) + B1*Tempo Reta única. Intercepto aleatório 1 mod1.lme -lme(fixed=peso~tempo, random=~1, data=frango) summary(mod1.lme) Formula: ~1 indiv (Intercept) Residual StdDev: Fixed effects: peso ~ tempo (Intercept) tempo (Intr) tempo class indiv; model peso = tempo / solution; random intercept / subject=indiv; Cov Parm Subject Estimate Intercept indiv a Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) Effect Estimate Error DF t Value Pr t Intercept .0001 tempo .0001 tempo .0001 MODELO 2: E(y) = (β0 + b0) + (β1 + b1)*tempo Reta única. Intercepto e inclinação aleatórios 2 mod2.lme -lme(fixed=peso~tempo, random=~tempo indiv, data=frango,control=lmecontrol( opt= optim )) summary(mod2.lme) Formula: ~tempo indiv (Intercept) (Intr) tempo Residual Fixed effects: peso ~ tempo (Intercept) tempo (Intr) tempo model peso = tempo / solution; random intercept tempo / subject=indiv; Z Cov Parm Subject Estimate Error Value Pr Z Intercept indiv 0... tempo indiv Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) Effect Estimate Error DF t Value Pr t Intercept .0001 tempo .0001 tempo .0001 MODELO 3: E(y) = (β0 + b0i) + (β1 + b1i)*tempo, i = 1, 2. Reta única. Intercepto e inclinação aleatórios por sexo 3 mod3.lme -lme(fixed=peso~tempo, random=~sex+sex:tempo-1 indiv, data=frango,control=lmecontrol( opt= optim )) summary(mod3.lme) Formula: ~sex + sex:tempo - 1 indiv sexfemea sexfem sexmch sxfm:t sexmacho sexfemea:tempo sexmacho:tempo Residual Fixed effects: peso ~ tempo (Intercept) tempo (Intr) tempo model peso = tempo / solution; random intercept tempo / subject=indiv group=sex; Columns in Z Per Subject 4 Z Cov Parm Subject Group Estimate Error Value Pr Z Intercept indiv sex Femea 0... Intercept indiv sex Macho 0... tempo indiv sex Femea tempo indiv sex Macho Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) Effect Estimate Error DF t Value Pr t Intercept .0001 tempo .0001 tempo .0001 MODELO 4: E(y) = (β0i + b0) + β1*tempo (i = 1, 2) Retas paralelas. Intercepto aleatório 4 mod4.lme -lme(fixed = peso~sex-1 + tempo, random=~1 indiv, data=frango) summary(mod4.lme) Formula: ~1 indiv (Intercept) Residual StdDev: Fixed effects: peso ~ sex tempo sexfemea sexmacho tempo sexfem sexmch sexmacho tempo model peso = sex tempo / solution noint; random intercept / subject=indiv; Columns in Z Per Subject 1 Z Cov Parm Subject Estimate Error Value Pr Z Intercept indiv Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) sex Femea .0001 sex Macho .0001 tempo .0001 sex .0001 tempo .0001 MODELO 5: E(y) = (β0i + b0) + (β1 + b1)*tempo (i = 1, 2) Retas paralelas. Intercepto aleatório por sexo. 5 mod5.lme -lme(fixed=peso~sex-1 + tempo, random=~sex-1 indiv, data=frango) summary(mod5.lme) Formula: ~sex - 1 indiv sexfemea sexfem sexmacho Residual Fixed effects: peso ~ sex tempo sexfemea sexmacho tempo sexfem sexmch sexmacho tempo model peso = sex tempo / solution noint; random intercept / subject=indiv group=sex; Columns in Z Per Subject 2 Z Cov Parm Subject Group Estimate Error Value Pr Z Intercept indiv sex Femea 0... Intercept indiv sex Macho Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) sex Femea .0001 sex Macho .0001 tempo .0001 sex .0001 tempo .0001 MODELO 6: E(y) = (β0i + b0) + (β1 + b1)*tempo (i = 1, 2) Retas paralelas. Intercepto e inclinação aleatórios. 6 mod6.lme -lme(fixed=peso~sex-1 + tempo, random=~tempo indiv, data=frango, + control=lmecontrol( opt= optim )) summary(mod6.lme) Formula: ~tempo indiv (Intercept) (Intr) tempo Residual Fixed effects: peso ~ sex tempo sexfemea sexmacho tempo sexfem sexmch sexmacho tempo model peso = sex tempo / solution noint; random intercept tempo / subject=indiv; Columns in Z Per Subject 2 Z Cov Parm Subject Estimate Error Value Pr Z Intercept indiv 0... tempo indiv Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) sex Femea .0001 sex Macho .0001 tempo .0001 sex .0001 tempo .0001 MODELO 7: E(y) = (β0i + b0) + β1i*tempo (i = 1, 2) Retas distintas. Intercepto aleatório. 7 mod7.lme -lme(fixed=peso~sex-1 + sex:tempo, random=~1 indiv, + data=frango,control=lmecontrol( opt= optim )) summary(mod7.lme) Formula: ~1 indiv (Intercept) Residual StdDev: Fixed effects: peso ~ sex sex:tempo sexfemea sexmacho sexfemea:tempo sexmacho:tempo sexfem sexmch sxfm:t sexmacho sexfemea:tempo sexmacho:tempo model peso = sex sex*tempo / solution noint; random intercept / subject=indiv; Columns in Z Per Subject 1 Cov Parm Subject Estimate Intercept indiv Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) sex Femea .0001 sex Macho .0001 tempo*sex Femea .0001 tempo*sex Macho .0001 sex .0001 tempo*sex .0001 MODELO 8: E(y) = (β0i + b0) + (β1i + b1)*tempo (i = 1, 2) Retas distintas. Intercepto e inclinação aleatórios. 8 mod8.lme -lme(fixed=peso~sex-1 + sex:tempo, random=~tempo indiv, + data=frango,control=lmecontrol( opt= optim )) summary(mod8.lme) Formula: ~tempo indiv (Intercept) (Intr) tempo Residual Fixed effects: peso ~ sex sex:tempo sexfemea sexmacho sexfemea:tempo sexmacho:tempo sexfem sexmch sxfm:t sexmacho sexfemea:tempo sexmacho:tempo proc mixed data=frango.frango covtest; model peso = sex sex*tempo / solution noint; random intercept tempo / subject=indiv; Z Cov Parm Subject Estimate Error Value Pr Z Intercept indiv 0... tempo indiv Residual Res Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) sex Femea .0001 sex Macho .0001 tempo*sex Femea .0001 tempo*sex Macho .0001 sex .0001 tempo*sex .0001
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