Exam5.2 {StroupGLMM}

Example 5.2 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-164)

Description

Exam5.2 three factor main effects only design

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

DataSet5.2

Examples

DataSet5.2$a <- factor( x = DataSet5.2$a)
DataSet5.2$b <- factor( x = DataSet5.2$b)
DataSet5.2$c <- factor(x  = DataSet5.2$c)
##---first adding factor a in model
Exam5.2.lm1 <-
  lm(
      formula     = y~ a
    , data        = DataSet5.2
 #  , subset
 #  , weights
 #  , na.action
    , method      = "qr"
    , model       = TRUE
 #  , x           = FALSE
 #  , y           = FALSE
    , qr          = TRUE
    , singular.ok = TRUE
    , contrasts   = NULL
 #  , offset
 #  , ...
  )
summary( Exam5.2.lm1 )

library(lsmeans) 
##---A first
( Lsm5.2lm1    <-
  lsmeans::lsmeans(
     object  = Exam5.2.lm1
    , specs   = "a"
    # , ...
  )
)
## lsmeans::contrast(object = Lsm5.2lm1 , method = "pairwise")
Anovalm1  <-   anova(object   = Exam5.2.lm1)
Anovalm1

##---then adding factor b in model
Exam5.2.lm2 <-
  lm(
      formula     = y~ a + b
    , data        = DataSet5.2
 #  , subset
 #  , weights
 #  , na.action
    , method      = "qr"
    , model       = TRUE
 #  , x           = FALSE
 #  , y           = FALSE
    , qr          = TRUE
    , singular.ok = TRUE
    , contrasts   = NULL
 #  , offset
 #  , ...
  )
summary( Exam5.2.lm1 )
(Lsm5.2lm2    <-
  lsmeans::lsmeans(
      object  = Exam5.2.lm2
    , specs   = "b"
    # , ...
  )
)
## lsmeans::contrast(object = Lsm5.2lm2, method = "pairwise")
Anovalm2  <-   anova(object   = Exam5.2.lm2)
Anovalm2

##---then adding factor c in model
Exam5.2.lm3 <-
  lm(
      formula     = y~ a + b + c
    , data        = DataSet5.2
#  , subset
#  , weights
#  , na.action
    , method      = "qr"
    , model       = TRUE
#  , x           = FALSE
#  , y           = FALSE
    , qr          = TRUE
    , singular.ok = TRUE
    , contrasts   = NULL
#  , offset
#  , ...
  )
summary( Exam5.2.lm3 )
(Lsm5.2lm3    <-
  lsmeans::lsmeans(
      object  = Exam5.2.lm3
    , specs   = "c"
    # , ...
  )
)
## lsmeans::contrast(object = Lsm5.2lm3, method = "pairwise")
Anovalm3  <-  anova(object   = Exam5.2.lm3)
Anovalm3

##---Now Change the order and add b first in model
Exam5.2.lm4 <-
  lm(
      formula     = y~  b
    , data        = DataSet5.2
 #  , subset
 #  , weights
 #  , na.action
    , method      = "qr"
    , model       = TRUE
 #  , x           = FALSE
 #  , y           = FALSE
    , qr          = TRUE
    , singular.ok = TRUE
    , contrasts   = NULL
 #  , offset
 #  , ...
  )
summary( Exam5.2.lm4 )
(Lsm5.2lm4    <-
  lsmeans::lsmeans(
      object  = Exam5.2.lm4
    , specs   = "b"
    # , ...
  )
)
## lsmeans::contrast(object = Lsm5.2lm4, method = "pairwise")
Anovalm4  <-  anova(object   = Exam5.2.lm4)

##---then adding factor a in model
Exam5.2.lm5 <-
  lm(
      formula     = y~ b + a
    , data        = DataSet5.2
 #  , subset
 #  , weights
 #  , na.action
    , method      = "qr"
    , model       = TRUE
 #  , x           = FALSE
 #  , y           = FALSE
    , qr          = TRUE
    , singular.ok = TRUE
    , contrasts   = NULL
 #  , offset
 #  , ...
  )
summary( Exam5.2.lm5 )
(Lsm5.2lm5    <-
  lsmeans::lsmeans(
      object  = Exam5.2.lm5
    , specs   = "a"
    # , ...
  )
)
## lsmeans::contrast(object = Lsm5.2lm3, method = "pairwise")
Anovalm5  <-  anova(object   = Exam5.2.lm5)
Anovalm5