Examp3.3 {VetResearchLMM}

Examp3.3 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

Description

Examp3.3 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.

Author(s)

1. Muhammad Yaseen ([e-mail])

References

1. Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

See Also

ex124

Examples

#-------------------------------------------------------------
## Example 3.3 Model 1 p-88
#-------------------------------------------------------------
# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# RANDOM animal_id(breed)/SOLUTION;
# RUN;

 library(lme4)
 options(contrasts = c(factor = "contr.SAS", ordered = "contr.poly"))
 str(ex33)

 fm3.5 <-
  lme4::lmer(
         formula    = PCV ~ breed + breed:time + (1|animal_id:breed)
       , data       = ex33
       , REML       = TRUE
       , control    = lmerControl()
       , start      = NULL
       , verbose    = 0L
    #  , subset
    #  , weights
    #  , na.action
    #  , offset
       , contrasts  = list(breed = "contr.SAS")
       , devFunOnly = FALSE
    #  , ...
       )
 summary(fm3.5)
 anova(fm3.5)

 library(lmerTest)
 fm3.6 <-
  lmerTest::lmer(
         formula    = PCV ~ breed + breed:time + (1|animal_id:breed)
       , data       = ex33
       , REML       = TRUE
       , control    = lmerControl()
       , start      = NULL
       , verbose    = 0L
    #  , subset
    #  , weights
    #  , na.action
    #  , offset
       , contrasts  = list(breed = "contr.SAS")
       , devFunOnly = FALSE
    #  , ...
       )
 summary(fm3.6)
 anova(object = fm3.6, ddf = "Satterthwaite")


# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# REPEATED/TYPE=CS SUB = animal_id(breed) R;
# RUN;


 library(nlme)
 fm3.7 <-
      nlme::gls(
            model       = PCV ~ breed + breed:time
          , data        = ex33
          , correlation = corCompSymm(, form = ~ 1|animal_id/breed)
          , weights     = NULL
        # , subset      =
          , method      = "REML" # c("REML", "ML")
          , na.action   = na.fail
          , control     = list()
          )
 summary(fm3.7)
 anova(fm3.7)



# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = time breed breed*time/SOLUTION;
# RANDOM animal_id(breed)/SOLUTION;
# RUN;

 fm3.8 <-
  lme4::lmer(
         formula    = PCV ~ time + breed + breed:time + (1|animal_id:breed)
       , data       = ex33
       , REML       = TRUE
       , control    = lmerControl()
       , start      = NULL
       , verbose    = 0L
    #  , subset
    #  , weights
    #  , na.action
    #  , offset
       , contrasts  = list(breed = "contr.SAS")
       , devFunOnly = FALSE
    #  , ...
       )
 summary(fm3.8)
 anova(fm3.8)


 fm3.9 <-
  lmerTest::lmer(
         formula    = PCV ~ time + breed + breed:time + (1|animal_id:breed)
       , data       = ex33
       , REML       = TRUE
       , control    = lmerControl()
       , start      = NULL
       , verbose    = 0L
    #  , subset
    #  , weights
    #  , na.action
    #  , offset
       , contrasts  = list(breed = "contr.SAS")
       , devFunOnly = FALSE
    #  , ...
       )
 summary(fm3.9)
 anova(object = fm3.9, ddf = "Satterthwaite", type = 3)


# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# REPEATED/TYPE=AR(1) SUBJET = animal_id(breed) R;
# RUN;


 library(nlme)
 fm3.10 <-
      nlme::gls(
            model       = PCV ~ breed + breed:time
          , data        = ex33
          , correlation = corAR1(, form = ~ 1|animal_id/breed)
          , weights     = NULL
        # , subset      =
          , method      = "REML" # c("REML", "ML")
          , na.action   = na.fail
          , control     = list()
          )
 summary(fm3.10)
 anova(fm3.10)

# PROC MIXED DATA=ex33;
# CLASS breed animal_id;
# MODEL pcv = breed breed*time/SOLUTION;
# RANDOM INTERCEPT time/TYPE=UN SUBJET = animal_id(breed) SOLUTION;
# RUN;


 library(nlme)
# fm3.11 <-
#      nlme::gls(
#            model       = PCV ~ breed + breed:time
#          , data        = ex33
#          , random      = ~1|animal_id/breed
#          , correlation = corAR1(, form = ~ 1|animal_id/breed)
#          , weights     = NULL
#        # , subset      =
#          , method      = "REML" # c("REML", "ML")
#          , na.action   = na.fail
#          , control     = list()
#          )
# summary(fm3.11)
# anova(fm3.11)