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

Source: R/Exam2.B.6.R
Exam2.B.6.Rd

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

Author

  1. Muhammad Yaseen (myaseen208@gmail.com)

  2. Adeela Munawar (adeela.uaf@gmail.com)

Examples

#-----------------------------------------------------------------------------------
## Nested Model with no intercept
#-----------------------------------------------------------------------------------

data(Table1.2)
Table1.2$Batch <- factor(x = Table1.2$Batch)
library(nlme)
Exam2.B.6fm1 <- lme(
      fixed       = Y ~ X
    , data        = Table1.2
    , random      = list(Batch = pdDiag(~1), X = pdDiag(~1))
    , method      = c("REML", "ML")[1]
    )
Exam2.B.6fm1
#> Linear mixed-effects model fit by REML
#>   Data: Table1.2 
#>   Log-restricted-likelihood: -87.76411
#>   Fixed: Y ~ X 
#> (Intercept)           X 
#>  96.4894724   0.1241651 
#> 
#> Random effects:
#>  Formula: ~1 | Batch
#>         (Intercept)
#> StdDev:    2.511884
#> 
#>  Formula: ~1 | X %in% Batch
#>         (Intercept)  Residual
#> StdDev:    2.192282 0.9529553
#> 
#> Number of Observations: 36
#> Number of Groups: 
#>        Batch X %in% Batch 
#>            4           36 
library(broom.mixed)
tidy(Exam2.B.6fm1)
#> Warning: ran_pars not yet implemented for multiple levels of nesting
#> # A tibble: 2 × 7
#>   effect term        estimate std.error    df statistic  p.value
#>   <chr>  <chr>          <dbl>     <dbl> <dbl>     <dbl>    <dbl>
#> 1 fixed  (Intercept)   96.5      1.39      31     69.2  1.52e-35
#> 2 fixed  X              0.124    0.0263    31      4.72 4.76e- 5