BIBD and Adjusted Sums of Squares Type I

BIBD and Adjusted Sums of Squares Type I and Type III Sums of Squares 1

Confounding in Unbalanced Designs �When designs are “unbalanced”, typically with missing values, our estimates of Treatment Effects can be biased. �When designs are “unbalanced”, the usual computation formulas for Sums of Squares can give misleading results, since some of the variability in the data can be explained by two or more variables. 2

Example BIBD from Hicks 3

What would be a simple linear model and what does it say about any treatment mean comparison? Notation for layout and model. 4

Suppose we want to compare treatment 1 to treatment 2? The model says that if we compare averages, then we get a possible bias due to block. 5

Type I vs. Type III in partitioning variation �If an experimental design is not a balanced and complete factorial design, it is not an orthogonal design. �If a two factor design is not orthogonal, then the SSModel will not partition into unique components, i. e. , some components of variation may be explained by either factor individually (or simultaneously). �Type I SS are computing according to the order in which terms are entered in the model. �Type III SS are computed in an order independent fashion, i. e. each term gets the SS as though it were the last term entered for Type I SS. 6

If BIBD, design is unbalanced and some variation may be explained by either factor. If we use a Venn diagram: 7

Notation for Hicks’ example There are only two possible factors, Block and Trt. There are only three possible simple additive models one could run. In SAS syntax they are: �Model 1: Model Y=Block; �Model 2: Model Y=Trt; �Model 3: Model Y=Block Trt; 8

Adjusted SS notation Each model has its own “Model Sums of Squares”. These are used to derive the “Adjusted Sums of Squares”. �SS(Block)=Model Sums of Squares for Model 1 �SS(Trt)=Model Sums of Squares for Model 2 �SS(Block, Trt)=Model Sums of Squares for Model 3 9

The Sums of Squares for Block and Treatment can be adjusted to remove any possible confounding. Adjusting Block Sums of Squares for the effect of Trt: SS(Block|Trt)= SSModel(Block, Trt)- SSModel(Trt) Adjusting Trt Sums of Squares for the effect of Block: SS(Trt|Block)= SSModel(Block, Trt)- SSModel(Block) 10

From Hicks’ Example �SS(Block)=100. 667 �SS(Trt)=975. 333 �SS(Block, Trt)=981. 500 11

For SAS model Y=Block Trt; Source df Type I SS Type III SS Block 3 SS(Block) SS(Block|Trt) =100. 667 =981. 50 -975. 333 SS(Trt|Block) Trt 3 =981. 50 -100. 667 12

ANOVA Type III and Type I (Block first term in Model) 13

For SAS model Y=Trt Block; Source Trt df 3 Type I SS SS(Trt) =975. 333 Block 3 SS(Block|Trt) =981. 50 -975. 333 Type III SS SS(Trt|Block) =981. 50 -100. 667 SS(Block|Trt) =981. 50 -975. 333 14

ANOVA Type III and Type I (Trt. First term in Model) 15

How does variation partition? SS Total Variation Block TRT Block or Trt Error 16

How this can work-I Hicks example 17

When does case I happen? In Regression, when two Predictor variables are positively correlated, either one could explain the “same” part of the variation in the Response variable. The overlap in their ability to predict is what is adjusted “out” of their Sums of Squares. 18

Example BIBD From Montgomery (things can go the other way) 19

ANOVA with Adjusted and Unadjusted Sums of Squares 20

Sequential Fit with Block first 21

Sequential Fit with Treatment first 22

LS Means Plot 23

LS Means for Treatment, Tukey HSD 24

How this can work- II Montgomery example 25

When does case II happen? Sometimes two Predictor variables can predict the Response better in combination than the total of they might predict by themselves. In Regression this can occur when Predictor variables are negatively correlated. 26