Dear Rich: First thank you very much. it helped a lot.
The F value and its p-values corresponding to the factor "Supplier"are different from the ones in the textbook, Montgomery 8th ed. But conclusion stay same (Pvalue > 0.05) In the last model (the third one) used for Table 14.6 , I am not sure what is wrong. > MontEx14.1.aov3 <- aov(Purity ~ Supplier + Error(Supplier:Batch), data=MontEx14.1) Warning message: In aov(Purity ~ Supplier + Error(Supplier:Batch), data = MontEx14.1) : Error() model is singular many thanks Steven On Thu, Jan 8, 2015 at 1:58 PM, Richard M. Heiberger <[email protected]> wrote: > 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 <[email protected]> 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 > > [email protected] > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > [email protected] mailing list > > https://stat.ethz.ch/mailman/listinfo/r-sig-teaching > -- Steven M. Stoline 1123 Forest Avenue Portland, ME 04112 [email protected] [[alternative HTML version deleted]] _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-teaching
