Exam2.B.5: Example 2.B.5 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

Exam2.B.5 is related to multi batch regression data assuming different forms of linear models.

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

Table1.2

Examples

#-----------------------------------------------------------------------------------
## Nested Model with no intercept
#-----------------------------------------------------------------------------------
data(Table1.2)
Table1.2$Batch <- factor(x = Table1.2$Batch)
Exam2.B.5.lm1 <-
  lm(
         formula     = Y~0+Batch+ Batch/X
       , data        = Table1.2
    #  , subset
    #  , weights
    #  , na.action
       , method      = "qr"
       , model       = TRUE
    #  , x           = FALSE
    #  , y           = FALSE
       , qr          = TRUE
       , singular.ok = TRUE
       , contrasts   = NULL
    #  , offset
    #  , ...
  )
DesignMatrix.lm1 <- model.matrix (object = Exam2.B.5.lm1)
DesignMatrix.lm1
#-----------------------------------------------------------------------------------
## Interaction Model with intercept
#-----------------------------------------------------------------------------------
Exam2.B.5.lm2 <-
  lm(
         formula     = Y~Batch +X+ Batch*X
       , data        = Table1.2
    #  , subset
    #  , weights
    #  , na.action
       , method      = "qr"
       , model       = TRUE
    #  , x           = FALSE
    #  , y           = FALSE
       , qr          = TRUE
       , singular.ok = TRUE
       , contrasts   = NULL
    #  , offset
    #  , ...
  )
DesignMatrix.lm2 <-   model.matrix (object = Exam2.B.5.lm2)
DesignMatrix.lm2
#-----------------------------------------------------------------------------------
## Interaction Model with no intercept
#-----------------------------------------------------------------------------------
Exam2.B.5.lm3 <-
  lm(
         formula     = Y~0 + Batch + Batch*X
       , data        = Table1.2
    #  , subset
    #  , weights
    #  , na.action
       , method      = "qr"
       , model       = TRUE
    #  , x           = FALSE
    #  , y           = FALSE
       , qr          = TRUE
       , singular.ok = TRUE
       , contrasts   = NULL
    #  , offset
    #  , ...
  )
DesignMatrix.lm3 <-   model.matrix(object = Exam2.B.5.lm3)
#-----------------------------------------------------------------------------------
## Interaction Model with intercept  but omitting X term as main effect
#-----------------------------------------------------------------------------------
Exam2.B.5.lm4 <-
  lm(
         formula     = Y~Batch + Batch*X
       , data        = Table1.2
    #  , subset
    #  , weights
    #  , na.action
       , method      = "qr"
       , model       = TRUE
    #  , x           = FALSE
    #  , y           = FALSE
       , qr          = TRUE
       , singular.ok = TRUE
       , contrasts   = NULL
    #  , offset
    #  , ...
  )
DesignMatrix.lm4 <-   model.matrix(object = Exam2.B.5.lm4)
DesignMatrix.lm4

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