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5 EXAMPLE: FIVE TREATMENTS IN RCB DESIGN STRUCTURE

5 EXAMPLE: FIVE TREATMENTS IN RCB DESIGN STRUCTURE

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More Than Two Treatments in a Blocked Design Structure



9



TABLE 10.3

PROC GLM Code to Fit the Within Block Model with

Parameter Estimates

PROC GLM DATA=LONG10; CLASSES TRT BLOCK;

MODEL Y=BLOCK TRT X*TRT/SOLUTION INVERSE;

ESTIMATE “A1-Abar” TRT .8 –.2 –.2 –.2 –.2;

ESTIMATE “A2-Abar” TRT –.2 .8 –.2 –.2 –.2;

ESTIMATE “A3-Abar” TRT –.2 –.2 .8 –.2 –.2;

ESTIMATE “A4-Abar” TRT –.2 –.2 –.2 .8 –.2;

ESTIMATE “A5-Abar” TRT –.2 –.2 –.2 –.2 .8;

Source

Model

Error

Corr Total



df

20

39

59



SS

5724.9054

38.8640

5763.7693



MS

286.2453

0.9965



FValue

287.25



ProbF

0.0000



Source

BLOCK

trt

x*trt



df

11

4

5



SS(III)

658.5902

6.5423

1040.1444



MS

59.8718

1.6356

208.0289



FValue

60.08

1.64

208.76



ProbF

0.0000

0.1834

0.0000



Estimate

–1.3249

0.5019

0.9823

3.0808

0.0000

0.5100

0.6734

0.9135

1.0798

1.4309



Biased

1

1

1

1

1

0

0

0

0

0



StdErr

1.8508

1.7831

1.9378

1.9275



tValue

–0.72

0.28

0.51

1.60



Probt

0.4783

0.7798

0.6151

0.1180



0.0613

0.0579

0.0708

0.0611

0.0693



8.32

11.63

12.90

17.67

20.66



0.0000

0.0000

0.0000

0.0000

0.0000



Parameter

trt 1

trt 2

trt 3

trt 4

trt 5

x*trt 1

x*trt 2

x*trt 3

x*trt 4

x*trt 5



TABLE 10.4

PROC GLM Code for the Within Block Model

Parameter Estimates from Estimate Statements

ESTIMATE

ESTIMATE

ESTIMATE

ESTIMATE

ESTIMATE

Parameter

A1-Abar

A2-Abar

A3-Abar

A4-Abar

A5-Abar



© 2002 by CRC Press LLC



“A1-Abar”

“A2-Abar”

“A3-Abar”

“A4-Abar”

“A5-Abar”

Estimate

–1.9730

–0.1461

0.3343

2.4328

–0.6480



TRT

TRT

TRT

TRT

TRT



.8 –.2 –.2 –.2 –.2;

–.2 .8 –.2 –.2 –.2;

–.2 –.2 .8 –.2 –.2;

–.2 –.2 –.2 .8 –.2;

–.2 –.2 –.2 –.2 .8;

StdErr

1.0980

0.9878

1.2054

1.1397

1.2343



tValue

–1.80

–0.15

0.28

2.13

0.53



Probt

0.0801

0.8832

0.7830

0.0391

–0.6026



10



Analysis of Messy Data, Volume III: Analysis of Covariance



TABLE 10.5

PROC GLM Code to Fit Model to Provide Expected

Mean Squares and Within Block Test of the Equal

Slopes Hypothesis

PROC GLM DATA=LONG10; CLASSES BLOCK TRT;

MODEL Y=BLOCK TRT X X*TRT;

RANDOM BLOCK;

Source

Model

Error

Corrected Total



df

20

39

59



SS

5724.9054

38.8640

5763.7693



MS

286.2453

0.9965



FValue

287.25



ProbF

0.0000



Source

BLOCK

trt

x

x*trt



df

11

4

1

4



SS(I)

1424.9533

3259.8077

911.6756

128.4688



MS

129.5412

814.9519

911.6756

32.1172



FValue

129.99

817.80

914.87

32.23



ProbF

0.0000

0.0000

0.0000

0.0000



Source

BLOCK

trt

x

x*trt



df

11

4

1

4



SS(III)

658.5902

6.5423

909.9734

128.4688



MS

59.8718

1.6356

909.9734

32.1172



FValue

60.08

1.64

913.16

32.23



ProbF

0.0000

0.1834

0.0000

0.0000



Source

BLOCK



Expected Mean Square (III)

Var(Error) + 4.5455 Var(BLOCK)



TABLE 10.6

Data for the Between Block Analysis

BLOCK

1

2

3

4

5

6

7

8

9

10

11

12



© 2002 by CRC Press LLC



SY

324.8

314.0

344.2

343.3

370.1

324.3

355.8

359.1

389.7

388.4

376.4

372.7



SX1

14.0

15.4

13.5

19.0

12.0

24.1

28.9

18.7

25.6

22.7

25.3

22.6



SX2

11.4

20.6

21.2

9.0

16.9

21.3

23.6

16.0

13.2

28.5

14.8

11.1



SX3

14.4

20.3

15.6

20.8

16.5

14.2

17.8

24.8

27.2

28.5

19.2

18.4



SX4

12.2

11.9

21.6

17.0

31.4

16.2

22.9

19.1

19.4

22.4

26.1

19.8



SX5

21.1

24.3

16.1

17.5

19.0

12.5

26.9

19.8

28.6

16.7

14.9

22.4



More Than Two Treatments in a Blocked Design Structure



11



TABLE 10.7

PROC GLM Code to Fit the Between Block Model, Test

Equality of Slopes and Estimate the Parameters of the Model

PROC GLM data=new; MODEL SY = SX1 SX2

SX5/INVERSE SOLUTION;

CONTRAST ‘SLOPES =’ SX1 1 SX2 –1, SX1 1

1 SX4 –1, SX1 1 SX5 –1;

ESTIMATE “Abar” Intercept 1;

ESTIMATE “B1” SX1 1; ESTIMATE “B2” SX2

“B3” SX3 1;

ESTIMATE “B4” SX4 1; ESTIMATE “B5” SX5

Source

Model

Error

Corrected Total



SX3 SX4

SX3 –1, SX1



1; ESTIMATE

1;



df

5

6

11



SS

6331.0082

793.7584

7124.7667



MS

1266.2016

132.2931



FValue

9.57



ProbF

0.0080



SLOPES =



4



2910.1908



727.5477



5.50



0.0330



Parameter

Abar

B1

B2

B3

B4

B5



Estimate

228.8619

0.9776

–0.9333

2.8539

3.1500

0.1646



StdErr

24.9251

0.6784

0.6271

0.8005

0.6504

0.7776



tValue

9.18

1.44

–1.49

3.57

4.84

0.21



Probt

0.0001

0.1996

0.1872

0.0119

0.0029

0.8394



variance table is a partition of the Type I block sum of squares from the within block

analyses. The block totals are based on five observations; thus the corrected total

sum of squares in Table 10.7 is five times larger than the Type I SS BLOCKS in

Table 10.5. The MSERROR from Table 10.7 provides the estimate of 5(σˆ ε2 + 5σˆ 2b ) =

132.2931 which yields σˆ 2b = 5.0924. Using the Type III expected mean squares in

ˆ 2b = 12.9524. The reason for

Table 10.5, the method of moments estimate of σ b2 is σ

this discrepancy is that the between block covariate variation is contaminating the

between block analysis (which uses Type 1 analysis) but is removed from the within

block analysis (which uses the Type 3 analysis).

Table 10.8 contains the within block estimates of the slopes, the between block

estimates of the slopes, their estimated covariance matrices, and the combined

estimates of the slopes with the corresponding estimated covariance matrix. The

covariance matrices for the within and between block estimates are from the respective

partitions of the inverse of the X′′ X matrix from each model. The statistic to test the

parallelism hypothesis based on the combined information using Equation 10.12 is

*

32.519. Using the approximation in Equation 10.13, the value of F.05,4,df

= 2.623,

c

which provides the approximate number of denominator degrees of freedom as 37.

Again, the test for equal slopes using the combined estimates of the slopes indicates

there is sufficient evidence to conclude the slopes are not all equal.



© 2002 by CRC Press LLC



12



Analysis of Messy Data, Volume III: Analysis of Covariance



TABLE 10.8

Between and Within Block Estimates of Slopes, Estimated Covariance

Matrices and the Resulting Combined Estimate of the Slopes and

Covariance Matrix

Slope

β1

β2

β3

β4

β5



Within Block Estimate of Slopes and Covariance Matrix

Estimate

Covariance Matrix

0.5100

0.003773

0.000156

0.000376

0.000093

0.6734

0.000156

0.003363

0.000160

0.000175

0.9135

0.000376

0.000160

0.005030

0.000058

1.0798

0.000093

0.000175

0.000058

0.003749

1.4309

0.000227

–0.000138

0.000371

–0.000175



0.000227

–0.000138

0.000371

–0.000175

0.004812



Slope

β1

β2

β3

β4

β5



Between Block Estimate of Slopes and Covariance Matrix

Estimate

Covariance Matrix

0.9776

0.003479

–0.000447

–0.001007

–0.000291

–0.9333

–0.000447

0.002972

–0.000566

–0.000407

2.8539

–0.001007

–0.000566

0.004844

–0.000200

3.1500

–0.000291

–0.000407

–0.000200

0.003198

0.1646

–0.000626

0.000717

–0.001300

0.000653



–0.000626

0.000717

–0.001300

0.000653

0.004571



Slope

β1

β2

β3

β4

β5



Combined Estimates of the Slopes With Standard Errors (STD),

t-Test of Each Slope Equal to Zero (T), and Significance Level (P)

Estimate

STD

T

P

LSD(.05)

0.518

0.060993

8.485

0.0000

0.123574

0.668

0.057599

11.596

0.0000

0.116688

0.928

0.070419

13.184

0.0000

0.142675

1.099

0.060825

18.075

0.0000

0.123220

1.425

0.068891

20.685

0.0000

0.139574



Test of the Equal Slopes Hypothesis Using the Combined Information

UC = 32.519 sign. level < .0001



Next the within block solution vector in Table 10.3 is transformed from the setto-zero restrictions to the sum-to-zero restrictions. The required matrix is in

Table 10.9. The H1 design matrix for the beta-hat model in Equation 10.7 is in

Table 10.9. The set-to-zero estimates (of the intercepts) are in Table 10.10 and the

sum-to-zero estimates (of the intercepts) are in Table 10.11.



Table 10.12 contains the between block information where the intercept is 5α.



The first step is to transform to α . by multiplying by the transformation matrix in

Table 10.12 with the rescaled between block estimate of the parameters. The resulting combined estimate of θ is in Table 10.13, which was computed by Equation 10.9.

The LSD(.05) values were computed using the method described following

Equation 10.10.

This computation of the combined estimates of the parameters can be accomplished using PROC MIXED. Table 10.14 contains the PROC MIXED code to fit

the desired model where the fixed effects are TRT and X*TRT and the random effect

is BLOCK. The “PARMS (5.09) (.99651)/ HOLD = 1,2” statement was used to

© 2002 by CRC Press LLC



More Than Two Treatments in a Blocked Design Structure



13



TABLE 10.9

Within Block Transformation Matrices



0.0

0.8

–0.2

–0.2

–0.2

–0.2

0.0

0.0

0.0

0.0

0.0



Transformation Matrix to Convert Set-to-Zero Estimates

of Intercepts to Sum-to-Zero Estimates

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

–0.2

–0.2

–0.2

–0.2

0.0

0.0

0.0

0.0

0.0

0.8

–0.2

–0.2

–0.2

0.0

0.0

0.0

0.0

0.0

–0.2

0.8

–0.2

–0.2

0.0

0.0

0.0

0.0

0.0

–0.2

–0.2

0.8

–0.2

0.0

0.0

0.0

0.0

0.0

–0.2

–0.2

–0.2

0.8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0



0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0



Design Matrix of the Within Block Estimates

Used to Combine the Estimates

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0



0.0

1.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0



0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0

0.0



0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.0



TABLE 10.10

Within Block Estimates of Regression Model Parameters for Set-to-Zero

Restrictions on the Intercepts



α1 – α5

–1.3249



α2 – α5

0.5019



α3 – α5

0.9823



Parameters and Estimates

α4 – α5

α5 – α5

β1

β2

3.0808

0.0000

0.5100 0.6734



β3

0.9135



β4

1.0798



β5

1.4309



cause PROC MIXED to use those specified values as the estimates of the variance

components. The estimates of the slopes are directly comparable to those in

Table 10.12 and the estimates of the intercepts in Table 10.14 can be computed by

combining the average intercept with each of the estimates of the intercepts deviating

from the average. A contrast statement is included in Table 10.14 to provide a test

of the equal slopes hypothesis. The F statistic has a value of 32.52 with 4 and

© 2002 by CRC Press LLC



14



Analysis of Messy Data, Volume III: Analysis of Covariance



TABLE 10.11

Within Block Estimates of Regression Model Parameters with Sum-to-Zero

Restrictions on the Intercepts





α

0.







α1 – α

–1.973







α2 – α

–0.146







α3 – α

0.334



Parameters and Estimates





α4 – α

α5 – α

β1

2.432

–0.648

0.510



β2

0.673



β3

0.913



β4

1.07



β5

1.43



TABLE 10.12

Between Block Estimates of Regression Model Parameters

and Estimated Covariance Matrices

Parameters

5α–



β1



228.862



0.9776



4.696085

–0.024078

–0.037388

–0.035901

–0.060125

–0.078422



–0.024078

0.003479

–0.000447

–0.001007

–0.000291

–0.000626



0.2

0

0

0

0

0



0

1

0

0

0

0



β2



β3



Parameter Estimates

–0.9333

2.8539

Covariance Matrix

–0.037388

–0.035901

–0.000447

–0.001007

0.002972

–0.000566

–0.000566

0.004844

–0.000407

–0.000200

0.000717

–0.001300



β4



β5



3.1500



0.1646



–0.060125

–0.000291

–0.000407

–0.000200

0.003198

0.000653



–0.078422

–0.000626

0.000717

–0.001300

0.000653

0.004571





Transformation matrix to rescale α

0

0

0

0

1

0

0

1

0

0

0

0



0

0

0

0

1

0



0

0

0

0

0

1



Rescaled between block estimate of parameters and covariance matrix.

α–

β1

β2

β3

β4

β5

45.7724

0.9776

–0.9333

2.8539

3.1500

0.1646



39 degrees of freedom. The least squares means from the analysis in Table 10.14

are in Table 10.15. The least squares means are predicted values evaluated at X = 20.

Table 10.16 contains the PROC MIXED code that uses REML estimates of the

variance components to carry out the analysis. The estimates of the block and residual

variance components are 13.6077 and 0.9967, respectively. The estimates of the

slopes and intercepts in Table 10.16 are quite similar to those in Table 10.14, the

difference being that different sets of estimates of variances components were used

in the computations. A contrast statement is included in Table 10.16 to provide a

test of the equal slopes hypothesis. The F statistic has a value of 32.28 with 4 and

39.2 degrees of freedom. The number of denominator degrees of freedom was

© 2002 by CRC Press LLC



More Than Two Treatments in a Blocked Design Structure



15



TABLE 10.13

Combined Estimates of the Parameters with Standard

Errors (STD), t-Test of Each Parameter Equal to Zero (T),

and Significance Level (P) for Example 9.1

Parameter



α



α1 – α



α2 – α



α3 – α



α4 – α



α5 – α

β1

β2

β3

β4

β5



Estimate

52.879

–1.999

0.075

0.165

2.166

–0.406

0.518

0.668

0.928

1.099

1.425



Std Err

0.889635

1.094679

0.983428

1.201541

1.133910

1.228441

0.060993

0.057599

0.070419

0.060825

0.068891



t-value

59.439

–1.826

0.076

0.137

1.910

–0.331

8.485

11.596

13.184

18.075

20.685



Sign Level

0.0000

0.0755

0.9399

0.8916

0.0635

0.7428

0.0000

0.0000

0.0000

0.0000

0.0000



LSD(.05)

2.020255

2.216329

1.991971

2.432836

2.297224

2.488505

0.123574

0.116688

0.142675

0.123220

0.139574



obtained using the KR (Kenward-Roger) option. This test statistic is very similar to

that obtained in Table 10.8. Finally, Table 10.17 contains the least squares means

for the five treatments evaluated at X = 15, 20, and 25, respectively. For this data

set, all pairwise comparisons between pairs of least squares means evaluated at each

of the values of X are significantly different.



10.6 EXAMPLE: BALANCED INCOMPLETE BLOCK

DESIGN STRUCTURE WITH FOUR TREATMENTS

The data in Table 10.18 consist of 4 treatments in blocks of size 3 arranged in

16 blocks to form a BIB design structure where Y is the response variable and X is

the covariate. The results in the previous sections could be used to obtain the within

block and the between block estimates of the parameters of the models and then

those estimates could be combined. That part of the process is left to the reader as

an exercise. The objective of the analysis in this section is to use PROC MIXED to

provide estimates of the model’s parameters and then demonstrate the process of

computing the least squares means for the incomplete block design structure. The

PROC MIXED code in Table 10.19 contains the terms necessary to fit the desired

model. The fixed effects are TRT and X*TRT and the random effect is BLOCK.

The estimates of the block and residual variance components are 15.5409 and

35.9386, respectively. The estimates of the model parameters are included in the

lower part of Table 10.19. The intercept is the estimate of the intercept for treatment 4

and the estimates corresponding to the levels of TRT are the deviations of the

intercepts from that of treatment 4. The least squares means evaluated at X = 100

are in Table 10.20. Since the model has unequal slopes, the least squares means need

to be evaluated at (at least) three values of X (only one was chosen for demonstration

purposes).

© 2002 by CRC Press LLC



16



Analysis of Messy Data, Volume III: Analysis of Covariance



TABLE 10.14

PROC MIXED Code to Use the Method of Moments Estimates

of the Variance Components by Employing the Hold Option

PROC MIXED CL COVTEST DATA=LONG10;;

CLASS BLOCK TRT;

MODEL Y=TRT X*TRT/NOINT SOLUTION DDFM=KR;

RANDOM BLOCK;

LSMEANS TRT/DIFF AT X=20;

PARMS (5.09) (.99651)/HOLD=1,2;

CONTRAST ‘EQUAL SLOPES’ X*TRT 1 –1 0 0 0, X*TRT 1 0 –1 0 0,

X*TRT 1 0 0 –1 0, x*TRT 1 0 0 0 –1;

CovParm

BLOCK

Residual



Estimate

5.090000

0.996510



Effect

trt

x*trt



NumDF

5

5



DenDF

39

39



FValue

730.39

213.81



ProbF



Label

EQUAL SLOPES



NumDF

4



DenDF

39



FValue

32.52



ProbF

0.0000



trt

1

2

3

4

5

1

2

3

4

5



Estimate

50.8797

52.9537

53.0436

55.0445

52.4730

0.5176

0.6679

0.9284

1.0994

1.4250



StdErr

1.4204

1.2248

1.5662

1.4096

1.5500

0.0610

0.0576

0.0704

0.0608

0.0689



df

1

1

1

1

1

1

1

1

1

1



Effect

trt

trt

trt

trt

trt

x*trt

x*trt

x*trt

x*trt

x*trt



tValue

35.82

43.23

33.87

39.05

33.85

8.49

11.60

13.18

18.08

20.69



TABLE 10.15

Least Squares Means Computed Using the Method

of Moments Estimates of the Variance Components

from Table 10.14

Effect

trt

trt

trt

trt

trt



© 2002 by CRC Press LLC



trt

1

2

3

4

5



x

20.00

20.00

20.00

20.00

20.00



Estimate

61.2307

66.3117

71.6113

77.0333

80.9738



StdErr

0.7122

0.7290

0.7123

0.7122

0.7122



df

1

1

1

1

1



tValue

85.97

90.97

100.53

108.16

113.70



Probt

0.0074

0.0070

0.0063

0.0059

0.0056



Probt

0.0178

0.0147

0.0188

0.0163

0.0188

0.0747

0.0548

0.0482

0.0352

0.0308



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