[R-sig-teaching] Analyze Nested and Split-Plot Designs with R

[Richard M. Heiberger]

Steven,

I assume you mean Montgomery 8th edition (he changed chapter numbers recently).

Please state what you expect as output.

For your first attempt, you have the case wrong (purity instead of Purity).

Are you reading Supplier and Batch as factors.  It can't be determined from the
printed table in your email.  Use dump (or dput) next time.   dput is
designed for R.

With the above corrections, your first model formula gives Table 14.4
and your second
formula gives Table 14.5.

To get Table 14.6 you need to use the Error() function in the model formula.
Here are statements for all three tables.

Rich

## dump("MontEx14.1","")
MontEx14.1 <-
structure(list(Supplier = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("1",
"2", "3"), class = "factor"), Batch = structure(c(1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L,
4L), .Label = c("1", "2", "3", "4"), class = "factor"), Purity = c(1L,
-1L, 0L, -2L, -3L, -4L, -2L, 0L, 1L, 1L, 4L, 0L, 1L, -2L, -3L,
0L, 4L, 2L, -1L, 0L, -2L, 0L, 3L, 2L, 2L, 4L, 0L, -2L, 0L, 2L,
1L, -1L, 2L, 3L, 2L, 1L)), .Names = c("Supplier", "Batch", "Purity"
), row.names = c(NA, -36L), class = "data.frame")



MontEx14.1$Supplier <- factor(MontEx14.1$Supplier)
MontEx14.1$Batch <- factor(MontEx14.1$Batch)

MontEx14.1.aov1 <- aov(Purity ~ Supplier/Batch, data=MontEx14.1)
summary(MontEx14.1.aov1)

MontEx14.1.aov2 <- aov(Purity ~ Supplier*Batch, data=MontEx14.1)
summary(MontEx14.1.aov2)

MontEx14.1.aov3 <- aov(Purity ~ Supplier + Error(Supplier:Batch),
data=MontEx14.1)
summary(MontEx14.1.aov3)


On Thu, Jan 8, 2015 at 1:06 PM, Steven Stoline <sstoline at gmail.com> wrote:
> Dear All:
>
> example 14.1, Montgomery, chapter 14. *Supplier* is a *fixed factor*,
> *Batches* is a *random factor* nested within the fixed factor Supplier.
>
> How to analyze these data in R in two ways:
>
> 1- Nested Design
>
> fit <- aov(purity~Supplier/Batch)
>
>
> it did not give me the expected output.
>
>
> 2- as a factorial (suppliers Fixed, Batches Random)
>
> fit.out <- aov(Purity~Supplier*Batch, data=have)
>
> it did not give me the expected output.
>
>
> Here is the data set:
> ===============
>
>> data
>   Supplier Batch  Purity
> 1     1  1
> 1     1 -1
> 1     1  0
> 1     2 -2
> 1     2 -3
> 1     2 -4
> 1     3 -2
> 1     3  0
> 1     3  1
> 1     4  1
> 1     4  4
> 1     4  0
> 2     1  1
> 2     1 -2
> 2     1 -3
> 2     2  0
> 2     2  4
> 2     2  2
> 2     3 -1
> 2     3  0
> 2     3 -2
> 2     4  0
> 2     4  3
> 2     4  2
> 3     1  2
> 3     1  4
> 3     1  0
> 3     2 -2
> 3     2  0
> 3     2  2
> 3     3  1
> 3     3 -1
> 3     3  2
> 3     4  3
> 3     4  2
> 3     4  1
>
>
> many thanks
> Steven
>
> --
> Steven M. Stoline
> 1123 Forest Avenue
> Portland, ME 04112
> sstoline at gmail.com
>
>         [[alternative HTML version deleted]]
>
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