Exam3.3 {StroupGLMM}

Example 3.3 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-77)

Description

Exam3.3 use RCBD data with fixed location effect and different forms of estimable functions are shown in this example.

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

DataSet3.2

Examples

#-----------------------------------------------------------------------------------
## linear model for Gaussian data
#-----------------------------------------------------------------------------------
data(DataSet3.2)
DataSet3.2$trt <- factor(x = DataSet3.2$trt, level = c(3,0,1,2))
DataSet3.2$loc <- factor(x = DataSet3.2$loc, level = c(8, 1, 2, 3, 4, 5, 6, 7))
Exam3.3.lm1 <-
  lm(
         formula     = Y~ trt+loc
       , data        = DataSet3.2
    #  , subset
    #  , weights
    #  , na.action
       , method      = "qr"
       , model       = TRUE
    #  , x           = FALSE
    #  , y           = FALSE
       , qr          = TRUE
       , singular.ok = TRUE
       , contrasts   = NULL
    #  , offset
    #  , ...
  )
summary( Exam3.3.lm1 )
#-------------------------------------------------------------
## Individula least squares treatment means
#-------------------------------------------------------------
library(lsmeans)
(Lsm3.3    <-
  lsmeans::lsmeans(
      object  = Exam3.3.lm1
    , specs   = "trt"
    # , ...
  )
)
#---------------------------------------------------
## Pairwise treatment means estimate
#---------------------------------------------------
contrast( object = Lsm3.3 , method = "pairwise")
#---------------------------------------------------
## Repairwise treatment means estimate
#---------------------------------------------------
## contrast( object = Lsm3.3 , method = "repairwise")
#-------------------------------------------------------
## LSM Trt0 (This term is used in Walter Stroups' book)
#-------------------------------------------------------
library(phia)
list3.3.1 <- list(trt=c("0" = 1 ) )
Test3.3.1 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels =  list3.3.1)
  )
#-------------------------------------------------------
## LSM Trt0 alt(This term is used in Walter Stroups' book)
#-------------------------------------------------------
list3.3.2 <-
  list(trt=c("0" = 1 )
       , loc=c("1" = 0,"2" = 0,"3" = 0,"4" = 0,"5" = 0,"6" = 0,"7" = 0)
  )
Test3.3.2 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels =  list3.3.2)
  )
#-------------------------------------------------------
##  Trt0 Vs Trt1
#-------------------------------------------------------
list3.3.3 <- list(trt=c("0" = 1,"1" = -1))
Test3.3.3 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels =  list3.3.3)
  )
#-------------------------------------------------------
##  average Trt0+1
#-------------------------------------------------------
list3.3.4 <- list(trt=c("0" = 0.5 , "1" = 0.5))
Test3.3.4 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels =  list3.3.4)
  )
#-------------------------------------------------------
##  average Trt0+2+3
#-------------------------------------------------------
list3.3.5 <- list(trt=c("0" = 0.33333,"2" = 0.33333,"3" = 0.33333))
Test3.3.5 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels = list3.3.5)
  )
#-------------------------------------------------------
##  Trt 2 Vs 3 difference
#-------------------------------------------------------
list3.3.6 <- list(trt=c("2" = 1,"3" = -1))
Test3.3.6 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels = list3.3.6)
  )
#-------------------------------------------------------
##  Trt 1 Vs 2 difference
#-------------------------------------------------------
list3.3.7 <- list(trt=c("1" = 1,"2" = -1))
Test3.3.7 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels = list3.3.7)
  )
#-------------------------------------------------------
##  Trt 1 Vs 3 difference
#-------------------------------------------------------
list3.3.8 <- list(trt=c("1" = 1,"3" = -1))
Test3.3.8 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels = list3.3.8)
  )
#-------------------------------------------------------
##  Average trt0+1  vs Average Trt2+3
#-------------------------------------------------------
list3.3.9 <-  list(trt=c("0" = 0.5,"1" = 0.5,"2" = -0.5,"3" = -0.5))
Test3.3.9 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels = list3.3.9)
  )
#-------------------------------------------------------
##  Trt1  vs Average Trt0+1+2
#-------------------------------------------------------
list3.3.10 <- list(trt=c("0" = 0.33333,"1" = -1,"2" = 0.33333,"3" = 0.33333))
Test3.3.10 <-
summary(testFactors(
    model  =  Exam3.3.lm1
  , levels = list3.3.10)
  )
#-------------------------------------------------------
## Sidak Multiplicity adjustment for p-values
#-------------------------------------------------------
library(mutoss)
PValues3.3 <-
  c(
    Test3.3.3[[7]][1, 4]
  , Test3.3.6[[7]][1, 4]
  , Test3.3.7[[7]][1, 4]
  , Test3.3.8[[7]][1, 4]
  , Test3.3.9[[7]][1, 4]
  , Test3.3.10[[7]][1, 4]
   )
 AdjPValues3.3 <- sidak(PValues3.3)