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

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

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))

levels(DataSet3.2$trt)
levels(DataSet3.2$loc)

Exam3.3.lm1 <- lm(formula = Y~ trt + loc, data = DataSet3.2)
summary( Exam3.3.lm1 )

#-------------------------------------------------------------
## Individual least squares treatment means
#-------------------------------------------------------------
library(emmeans)
(Lsm3.3 <- emmeans(object  = Exam3.3.lm1, specs = ~trt))

#---------------------------------------------------
## Pairwise treatment means estimate
#---------------------------------------------------
contrast(object = Lsm3.3 , method = "pairwise")

#---------------------------------------------------
## Revpairwise treatment means estimate
#---------------------------------------------------
contrast(object = Lsm3.3, method = "revpairwise")
#-------------------------------------------------------
## LSM Trt0 (This term is used in Walter Stroups' book)
#-------------------------------------------------------
contrast(
       object = emmeans(object  = Exam3.3.lm1, specs   = ~ trt)
     , list(trt = c(0, 1, 0, 0))
     )

library(phia)
testFactors(model  =  Exam3.3.lm1, levels =  list(trt = c("0" = 1)))


#-------------------------------------------------------
## LSM Trt0 alt(This term is used in Walter Stroups' book)
#-------------------------------------------------------
# contrast(
#        object = emmeans(object  = Exam3.3.lm1, specs   = ~ trt + loc)
#      , list(
#         trt = c(0, 1, 0, 0)
#       , loc = c(1, 0, 0, 0, 0, 0, 0, 0)
#        )
#      )
#
#
# list3.3.2 <-
#   list(
#         trt = c("0" = 1 )
#       , loc = c("1" = 0, "2" = 0,"3" = 0,"4" = 0,"5" = 0,"6" = 0,"7" = 0)
#   )
# testFactors(model  =  Exam3.3.lm1, levels =  list3.3.2)

#-------------------------------------------------------
##  Trt0 Vs Trt1
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(0, 1, -1, 0))
  )

testFactors(model  =  Exam3.3.lm1, levels =  list(trt = c("0" = 1, "1" = -1)))

#-------------------------------------------------------
##  average Trt0 + Trt1
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(0, 1/2, 1/2, 0))
  )

testFactors(model  =  Exam3.3.lm1, levels =  list(trt = c("0" = 0.5 , "1" = 0.5)))

#-------------------------------------------------------
##  average Trt0+2+3
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(1/3, 1/3, 0, 1/3))
  )

testFactors(model  =  Exam3.3.lm1, levels = list(trt = c("0" = 1/3,"2" = 1/3,"3" = 1/3)))

#-------------------------------------------------------
##  Trt 2 Vs 3 difference
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(-1, 0, 0, 1))
  )

testFactors(model = Exam3.3.lm1, levels = list(trt = c("2" = 1,"3" = -1)))

#-------------------------------------------------------
##  Trt 1 Vs 2 difference
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(0, 0, 1, -1))
  )
testFactors(model = Exam3.3.lm1, levels = list(trt = c("1" = 1,"2" = -1)))

#-------------------------------------------------------
##  Trt 1 Vs 3 difference
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(-1, 0, 1, 0))
  )
testFactors(model = Exam3.3.lm1, levels = list(trt = c("1" = 1,"3" = -1)))

#-------------------------------------------------------
##  Average trt0+1  vs Average Trt2+3
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(-1/2, 1/2, 1/2, -1/2))
  )
testFactors(model = Exam3.3.lm1, levels = list(trt = c("0" = 0.5,"1" = 0.5,"2" = -0.5,"3" = -0.5)))

#-------------------------------------------------------
##  Trt1  vs Average Trt0+1+2
#-------------------------------------------------------
contrast(
    emmeans(object  = Exam3.3.lm1, specs = ~trt)
  , list(trt = c(1/3, 1/3, -1, 1/3))
  )
testFactors(model = Exam3.3.lm1, levels = list(trt = c("0" = 1/3,"1" = -1,"2" = 1/3,"3" = 1/3)))

StroupGLMM documentation built on Oct. 2, 2024, 1:07 a.m.