Exam3.2 {StroupGLMM}

Example 3.2 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-73)

Description

Exam3.2 used binomial data, two treatment samples

Author(s)

1. Muhammad Yaseen ([e-mail])

2. Adeela Munawar ([e-mail])

References

1. Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.

See Also

DataSet3.1

Examples

#-------------------------------------------------------------
## Linear Model and results discussed in Article 1.2.1 after Table1.1
#-------------------------------------------------------------
data(DataSet3.1)
DataSet3.1$trt <- factor(x =  DataSet3.1$trt)
Exam3.2.glm <-
  glm(
         formula    =  F/N~trt
       , family     =  quasibinomial(link = "logit")
       , data       =  DataSet3.1
       , weights    =  NULL
    #  , subset
    #  , na.action
       , start      =  NULL
    #  , etastart
    #  , mustart
    #  , offset
    #  , control    =  list(...)
    #  , model      =  TRUE
       , method     =  "glm.fit"
    #  , x          =  FALSE
    #  , y          =  TRUE
       , contrasts  =  NULL
    #  , ...
  )
summary(Exam3.2.glm)

#-------------------------------------------------------------
## Individula least squares treatment means
#-------------------------------------------------------------
library(lsmeans)
(Lsm3.2    <-
  lsmeans::lsmeans(
    object  = Exam3.2.glm
    , specs   = "trt"
    # , ...
  )
)
OddsRatioMean3.2 <-  1/(1 + exp(-summary(Lsm3.2)[c("lsmean")] ) )
#---------------------------------------------------
## Over all mean
#---------------------------------------------------
library(phia)
list3.2<-   list(trt=c("0" = 0.5,"1" = 0.5 ))
(Test3.2 <-
  testFactors(
      model  =  Exam3.2.glm
    , levels =  list3.2 )
)
#---------------------------------------------------
## Pairwise treatment means estimate
#---------------------------------------------------
contrast(object = Lsm3.2 , method = "pairwise")
#---------------------------------------------------
## Repairwise treatment means estimate
#---------------------------------------------------
## contrast( object = Lsm3.2 , method = "repairwise")