Exam2.B.5: Example 2.B.5 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 2.B.5 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-57)

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)
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)
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)
DesignMatrix.lm3 <-   model.matrix(object = Exam2.B.5.lm3)
DesignMatrix.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)
DesignMatrix.lm4 <-   model.matrix(object = Exam2.B.5.lm4)
DesignMatrix.lm4

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