Examp3.3: Examp3.3 from Duchateau, L. and Janssen, P. and Rowlands, G....

In MYaseen208/VetResearchLMM: Linear Mixed Models - An Introduction with Applications in Veterinary Research

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)

MYaseen208/VetResearchLMM documentation built on May 8, 2019, 3:36 p.m.