Exam3.3: Example 3.3 from Generalized Linear Mixed Models: Modern...

In myaseen208/StroupGLMM: R Codes and Datasets for Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup

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)

myaseen208/StroupGLMM documentation built on May 10, 2019, 8:28 a.m.