Example 2.B.6 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-58)

Exam2.B.6 is related to multi batch regression data assuming different forms of linear models keeping batch effect random.

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)
library(nlme)
Table1.2$Batch <- factor(x = Table1.2$Batch)
Exam2.B.6fm1 <-
  lme(
      fixed       = Y~X
    , data        = Table1.2
    , random      = list(Batch = pdDiag(~1), X = pdDiag(~1))
    , correlation = NULL
    , weights     = NULL
  # , subset
    , method      = "REML" #c("REML", "ML")
    , na.action   = na.fail
  # , control     = list()
    , contrasts   = NULL
    , keep.data   = TRUE
  )