Generators to Construct an Efficient Generalized Class of Minimal Circular Neighbor Designs

Sajid Hussain 1 *, Jamshaid ul Hassan 1, Abdul Salam 1, Hurria Ali 1, and Muhammad Rasheed 1

1Department of Statistics, The Islamia University of Bahawalpur, Pakistan

Original Article Open Access
DOI: https://doi.org/10.32350/sir.81.04

ABSTRACT

A design that is robust to neighbor effects is one that can protect against the neighbor effects. In situations where minimal circular balanced neighbor designs (MCBNDs) cannot be constructed, minimal circular weakly balanced neighbor designs-I (MCWBNDs-I) are preferred, which is an efficient generalized class. MCWBNDs-I are neighbor designs in which v/2 pairs of distinct treatments appear twice as neighbors, while the remaining pairs appear once. New generators are developed in this study to obtain MCWBNDs-I in blocks of three different sizes.

Keywords: circular block, minimal design, neighbor balanced design, strongly balanced neighbor design, neighbor effect
* Corresponding Author: [email protected]
Published: 15-03-2024

1. INTRODUCTION

In field experiments, competition or interference between adjacent units can increase the variability of the results and may reduce efficiency. In experiments where neighboring unit treatments influence the effect of the current unit treatment, balanced neighbor designs (BNDs) are used to control neighbor effects. BNDs reduce bias caused by neighbor effect [1-3].

Rees [4] applied CBNDs in serology (virus research) and presented minimal CBNDs for some odd value of v (number of treatments). Azais [5] suggested that BNDs or partial BNDs control neighbor effects. Hwang [6], Cheng [7], Iqbal [8], Akhtar [9], Shehzad [10], Ahmed [11], Akhtar [12], Shahid [13], and Shahid [14] constructed BNDs for some cases. Misra [15], Chaure and Misra [16], Mishra [17], and Kedia and Misra [18] constructed GNDs for some specific cases. Ahmed [19], Zafaryab [20], Iqbal [21], Ahmed and Akhtar [22] developed some series to generate circular partial BNDs. Noreen [23] developed some series for MCWBNDs-I and MCWBNDs-II in equal block sizes. In this study, some generators have been developed to generate sets of cyclic shifts for MCWBNDs-I in blocks of three different sizes.

2. METHOD OF CYCLIC SHIFTS

For neighbor designs, Iqbal [24] introduced the method of cyclic shifts. Its Rule I is described here only for MCWBNDs-I.

If Sj = [qj1, qj2, …,qj(k-1)], where j = 1, 2, …, l and 1 ≤ qjiv-1. For v, even if each of 1, 2, …, v-1 appears once in S* but v/2 appears twice, then these sets produce MCBND, where S* contains

Example 2.1. S1 = [2,4,5,12], S2 = [9,10,21], S3 = [7,11] produce MCWBND-I for v = 24, k1 = 5, k2 = 4, and k3= 3.

Proof: S* = [2,4,5,12,23,9,10,21,8,7,11,18,22,20,19,12,1,15,14,3,16,17, 13,6]. Here, each of 1, 2, …, 23 appears once except 12 which appears twice. Hence, S1 = [2,4,5,12], S2 = [9,10,21], S3 = [7,11] produce MCWBND-I for v = 24, k1 = 5, k2 = 4, and k3 = 3.

To generate the design, use v blocks for S1. Write 0, 1, …, v-1 in Row 1. Add the 1st value of S1 (mod v) to Row 1 to get Row 2. Similarly, add the 2nd value of S1 (mod v) to Row 2 to get Row 3, and so on, see Table 1.

Table 1. Blocks Obtained from S1 = [2,4,5,12]

1

2

3

4

5

6

7

8

9

10

11

12

0

1

2

3

4

5

6

7

8

9

10

11

2

3

4

5

6

7

8

9

10

11

12

13

6

7

8

9

10

11

12

13

14

15

16

17

11

12

13

14

15

16

17

18

19

20

21

22

23

0

1

2

3

4

5

6

7

8

9

10

13

14

15

16

17

18

19

20

21

22

23

24

12

13

14

15

16

17

18

19

20

21

22

23

14

15

16

17

18

19

20

21

22

23

0

1

18

19

20

21

22

23

0

1

2

3

4

5

23

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

v more blocks are taken for S2, see Table 2.

Table 2. Blocks Obtained from S2 = [9,10,21]

25

26

27

28

29

30

31

32

33

34

35

36

0

1

2

3

4

5

6

7

8

9

10

11

9

10

11

12

13

14

15

16

17

18

19

20

19

20

21

22

23

0

1

2

3

4

5

6

16

17

18

19

20

21

22

23

0

1

2

3

37

38

39

40

41

42

43

44

45

46

47

48

12

13

14

15

16

17

18

19

20

21

22

23

21

22

23

0

1

2

3

4

5

6

7

8

7

8

9

10

11

12

13

14

15

16

17

18

4

5

6

7

8

9

10

11

12

13

14

15

v more blocks are taken for S3, see Table 3.

Table 3. Blocks Obtained from S3 = [7,11]

49

50

51

52

53

54

55

56

57

58

59

60

0

1

2

3

4

5

6

7

8

9

10

11

7

8

9

10

11

12

13

14

15

16

17

18

18

19

20

21

22

23

0

1

2

3

4

5

61

62

63

64

65

66

67

68

69

70

71

72

12

13

14

15

16

17

18

19

20

21

22

23

19

20

21

22

23

0

1

2

3

4

5

 

6

7

8

9

10

11

12

13

14

15

16

17

Here, pairs (0,12), (1,13), (2,14), (3,15), (4,16), (5,17), (6,18), (7,19), (8,20), (9,21), (10,22), and (11,23) appear twice as neighbors.

3. MCWBNDS-I FOR V (EVEN) = 2IK1+2K2+2K3 IN THREE DIFFERENT BLOCK SIZES

For v even, let m = (m-2)/2. The procedure is described to generate sets of shifts from constructor A or B to obtain MCWBNDs-I in three different block sizes.

Divide values of the selected constructor (C) into i groups, each of k1 values, one of k2 values, and one of k3 values, such that the sum of each group is divisible by v. The required i sets of shifts for k1 and one each for k2 and k3 would be obtained by discarding any one value from each group. Here, a (mod b) ≡ c, which means ‘c’ is remainder if ‘a’ is divided by ‘b’.

Example 3.1. Constructor B = [1, 2, 4, 5, …, 12, 21] for v = 24 can be divided into the following three groups.

G-I = (1,2,4,5,12),    G-II = (8,9,10,21), G-III = (6,7,11)

Deleting the smallest value of each group, following are the sets of shifts to obtain MCWBND-I for v = 14, k1 = 5, k2 = 4, k3 = 3, Es = 0.7410, and En = 0.7772.

S1 = [2,4,5,12], & S2 = [9,10,21], S3 = [7,11]

4. GENERATORS TO OBTAIN MCWBNDS-I FROM CONSTRUCTOR A (M (MOD 4) ≡ 2)

4.1. MCWBNDs-I for k1 = 4l

MCWBNDs-I can be generated for k1 = 4l and

Table 4. MCWBNDs-I obtained from Generator 4.1

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

54

12

11

4

1

A

[4,5,6,7,8,9,10,11,12,16,17],[14,15,18,19,20,21,22,23,25,26], [2,4,27]

0.87

0.90

38

8

6

5

1

A

[2,3,4,5,6,8,9],[19,11,12,13,14],[15,16,17,18]

0.84

0.86

54

12

10

5

1

A

[2,4,8,12,13,16,17,19,21,23,26],[9,10,11,14,18,20,22,25,27], [5,7,15,24]

0.85

0.90

4.2. MCWBNDs-I for k1 = 4l+2

MCWBNDs-I can be generated for k1 = 4l+2 and

Table 5. MCWBNDs-I obtained from Generator 4.2

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

30

6

5

4

1

A

[6,10,12,13,15], [3,7,8,11],[5,9,14]

0.82

0.82

46

10

9

4

1

A

[4,5,6,7,8,9,10,17,23],[12,13,14,15,16,18,19,20],[2,21,22]

0.85

0.89

38

6

4

3

1

A

[3,4,5,6,18],[10,11,12,19,15],[8,13,16],[14,17]

0.84

0.81

62

10

8

3

2

A

[4,8,9,10,14,16,17,21,23],[7,12,18,19,26,27,28,31], [3,6,15,20,24,25,30],[22,29]

0.87

0.88

46

10

8

5

1

A

[4,5,6,7,8,9,10,17,23],[14,15,16,18,19,21,22],[2,11,12,20]

0.85

0.89

4.3. MCWBNDs-I for k1 odd

MCWBNDs-I can be generated for k1 odd and

Table 6. MCWBNDs-I obtained from Generator 4.3

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

54

5

4

3

4

A

[5,14,15,16],[8,9,13,17],[2,6,22,23],[19,20,24,27],[11,12,21], [25,26]

0.87

0.79

78

9

8

4

3

A

[3,4,5,6,7,8,9,34],[14,15,16,17,18,20,21,22], [23,24,25,26,29,27,30,31],[12,28, 33,36,37,38,39],[10,32,35]

0.89

0.88

46

7

5

4

2

A

[3,4,5,6,7,19],[11,12,13,14,15,17],[18,20,22,23],[8,16,21]

0.85

0.76

30

7

5

3

1

A

[2,3,4,5,7,8],[11,12,13,15],[10,14]

0.82

0.82

78

9

7

5

3

A

[3,4,5,6,7,8,9,34],[12,13,14,15,16,17,19,39], [22,23,24,25,26,28,27,38], [31,32,33,35,36,37],[10,18,20,29]

0.89

0.88

4.4. MCWBNDs-I for k1(mod 4) ≡ 1

MCWBNDs-I can be generated for k1(mod 4) ≡ 1 and

Table 7. MCWBNDs-I obtained from Generator 4.4

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

46

9

8

6

1

A

[2,3,4,5,6,7,8,10],[14,15,16,17,18,22,23],[11,12,19,20,21]

0.85

0.89

94

9

8

3

4

A

[3,4,5,6,8,9,10,47],[13,14,15,16,17,18,37,46], [24,25,26,27,28,41,44,45], [23,31,32,33,35,36,42,43],

[11,19,21,29,30,38,39],[34,40]

0.90

0.88

86

13

12

5

2

A

[3,4,5,6,7,8,9,10,11,43,23,41], [14,15,16,17,19,20,21,22,24,25,26,27], [18,28,29,30,31,32,33,34,35,36,37],[38,39,40,42]

0.89

0.92

56

9

7

3

2

A

[3,4,5,6,8,9,26,49],[14,15,17,18,19,23,24,25], [11,12,16,20,21,22],[27,28].

0.87

0.87

94

9

7

4

4

A

[3,4,5,6,8,9,10,47],[13,14,15,16,17,18,37,46], [24,25,26,27,28,42,43,44], [29,30,31,32,33,34,35,36], [19,20,21,38,39,40],[7,41,45]

0.90

0.88

4.5. Generators to Obtain MCWBNDs-I for k1(mod 4) ≡ 3

MCWBNDs-I can be generated for k1(mod 4) ≡ 1 and

Table 8. MCWBNDs-I obtained from Generator 4.5Example 4.5.

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

62

7

6

4

3

A

[4,5,6,7,18,19],[11,13,14,15,30,31],[12,17,20,21,23,29], [16,22,25,24,28],[8,26,29]

0.87

0.85

46

7

6

3

2

A

[4,5,6,9,10,11],[7,12,14,15,19,23],[13,16,18,17,20],[21,22]

0.85

0.84

72

7

5

3

4

A

[10,22,23,24,26,33],[7,11,28,30,31,35],[13,17,18,25,32,34],

[3,12,16,20,29,63],[8,14,19,27],[21,36]

 

 

70

11

9

4

2

A

[2,3,4,5,7,8,9,10,11,12],[14,15,16,18,19,20,21,22,23,35], [45,29,30,31,32,33,34,28],[17,24,25]

0.88

0.90

5. GENERATORS TO OBTAIN MCWBNDS-I FROM CONSTRUCTOR B (M (MOD 4) ≡ 3)

5.1.  MCWBNDs-I for k1 = 4l

MCWBNDs-I can be generated for k1 = 4l and

Table 9. MCWBNDs-I obtained from Generator 5.1

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

40

8

7

5

1

B

[3,4,6,7,8,35,12],[10,11,12,13,16,17],[14,18,19,20]

0.83

0.87

56

12

11

5

1

B

[26,5,6,8,9,10,11,49,12,25,4],[2,13,14,15,18,19,20,21,22,23], [17,24,27,28]

0.87

0.91

5.2. MCWBNDs-I for k1 = 4l+2

MCWBNDs-I can be generated for k1 = 4l+2 and

Table 10. MCWBNDs-I obtained from Generator 5.2

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

40

6

5

3

2

B

[4,8,12,35,20],[11,13,14,16,17],[6,7,10,15],[18,19]

0.84

0.82

64

10

9

3

2

B

[3,4,5,6,7,9,10,26,56],[14,15,16,17,18,19,20,28,32], [12,21,23,24, 25,27,30,29],[22,31]

0.88

0.89

48

10

9

5

1

B

[2,3,4,5,8,9,10,42,12],[13,14,15,16,17,18,19,21], [20,22,23,24]

0.86

0.81

64

10

8

4

2

B

[3,4,5,6,7,9,10,26,56],[14,15,16,17,18,19,20,28,32], [21,23,24,25,27,31,29],[11,22,30]

0.88

0.89

5.3.  MCWBNDs-I for k1 odd

MCWBNDs-I can be generated for k1 odd and

Table 11. MCWBNDs-I obtained from Generator 5.3

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

32

7

6

3

1

B

[3,5,6,7,13,28],[9,10,11,12,14],[15,16]

0.83

0.83

80

9

8

5

3

B

[3,4,5,6,7,8,9,36],[14,15,16,17,38,18,39,70], [22,23,24,25,26,27,32,40], [35,29,30,31,33,28,34],[11,12,19,37]

0.89

0.88

32

7

5

4

1

B

[3,5,6,7,13,28],[11,12,15,16],[8,9,14]

0.83

0.83

40

9

7

4

1

B

[7,8,9,10,11,18,19,35],[6,12,13,14,15,16],[2,17,20]

0.84

0.87

48

7

6

4

2

B

[4,5,7,17,18,42],[10,11,13,15,14,24],[12,19,20,21,22],[8,16,23]

0.86

0.84

5.4. MCWBNDs-I for k1(mod 4) ≡ 1

MCWBNDs-I can be generated for k1(mod 4) ≡ 1 and

Table 12. MCWBNDs-I obtained from Generator 5.4

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

24

5

4

3

1

B

[2,4,5,12],[9,10,21],[7,11]

0.80

0.78

98

9

8

4

3

B

[3,4,5,7,8,9,10,48],[13,14,15,16,17,18,46,47], [24,25,26,27,34,42,43,44], [29,30,31,32,33,35,37,40], [11,19,20,28,36,38,39],[41,45,84]

0.87

0.88

80

9

7

6

3

B

[3,4,5,6,7,8,9,36],[13,14,16,17,22,38,39,70], [23,24,25,26,27,31,32,34], [19,20,28,29,30,33], [15,21,35,37,40]

0.89

0.88

56

9

7

3

2

B

[2,5,9,10,14,19,24,28],[4,6,8,13,15,18,22,23], [16,17,21,26,27,49],[20,25]

 

 

96

9

7

5

4

B

[3,5,7,8,9,10,19,33],[13,14,17,30,37,40,47,84], [15,21,22,24,25,26,27,28], [23,29,31,32,34,35,36,48], [11,18,38,39,42,43],[41,44,45,46]

0.90

0.88

5.5. Generators to Obtain MCWBNDs-I for k1(mod 4) ≡ 3

MCWBNDs-I can be generated for k1(mod 4) ≡ 3 and

Table 13. MCWBNDs-I obtained from Generator 5.5

v

k1

k2

k3

i

C

Sets of Shifts

Es

En

64

7

6

5

3

B

[4,5,6,7,19,20],[10,11,13,15,22,56],[16,17,18,21,23,31], [14,24,25,26,27], [28,29,30,32]

0.88

0.85

48

7

6

4

2

B

[5,7,11,12,15,42],[9,10,13,14,18,24],[16,17,19,20,22], [3,21,23]

0.86

0.85

72

7

5

3

4

B

[4,5,6,7,23,24],[11,12,13,14,21,63],[16,17,18,20,22,36], [2,25,26,27,28,35], [29,31,32,33],[30,34]

0.88

0.85

72

11

9

5

2

B

[2,3,4,5,6,7,8,15,30,63],[13,16,17,18,19,20,21,22,23,36], [14,24,25,26,27, 29,28,31],[32,33,34,35]

0.88

0.90

EFFICIENCY OF SEPARABILITY AND OF NEIGHBOR EFFECTS

6.1. Efficiency of Neighbor Effects

The efficiency factor for both direct and neighbor effects is the harmonic mean of eigenvalues (non-zero) of the respective information matrix [25, 26]. For a high value of En, the design would be suitable to estimate neighbor effects.

6.2 Efficiency for Separability

[27] developed the following measure of efficiency for separability (Es).

Es=[1-1/(v√(v-1))]×100%

7. CONCLUSION

New generators have been developed to generate sets of shifts in order to obtain MCWBNDs-I in blocks of three different sizes. MCWBNDs-I obtained through these newly developed generators possess high values of Es and En. Therefore, these designs are efficient to control neighbor effects as well as to estimate the direct effect and neighbor effects independently.

CONFLICT OF INTEREST

The authors of the manuscript have no financial or non-financial conflict of interest in the subject matter or materials discussed in this manuscript.

DATA AVALIABILITY STATEMENT

Data availability is not applicable as no new data was created.

REFERENCES

  1. Azais JM. Design of experiments for studying intergenotypic competition. J Royal Stat Soc. 1987;49(3):334–345. https://doi.org/10.1111/j.2517-6161.1987.tb01704.x
  2. Kunert J. Randomization of neighbour balanced designs. Biometric J. 2000;42(1):111–118. https://doi.org/10.1002/(SICI)1521-4036(200001)42:1%3C111::AID-BIMJ111%3E3.0.CO;2-L
  3. Tomar JS, Jaggi S, Varghese C. On totally balanced block designs for competition effects. J App Stat. 2005;32(1):87–97. https://doi.org/10.1080/0266476042000305177
  4. Rees DH. Some designs of use in serology. Biometrics. 1967;23(4):779–791. https://doi.org/10.2307/2528428
  5. Azais JM, Bailey RA, Monod H. A catalogue of efficient neighbor-designs with border plots. Biometrics. 1993;49(4):1252–1261. https://doi.org/10.2307/2532269
  6. Hwang FK. Constructions for some classes of neighbor designs. Ann Stat. 1973;1(4):786–790. https://www.jstor.org/stable/2958325
  7. Cheng CS. Construction of optimal balanced incomplete block designs for correlated observations. Ann Stat. 1983;11(1),240–246. https://doi.org/10.1214/aos/1176346074
  8. Iqbal I, Tahir MH, Ghazali SSA. Circular neighbor-balanced designs using cyclic shifts. Sci China Series A:Math. 2009;52(10):2243–2256. https://doi.org/10.1007/s11425-009-0063-1
  9. Akhtar M, Ahmed R, Yasmin F. A catalogue of nearest neighbor balanced designs in circular blocks of size five. Pak J Stat. 2010;26(2):e397.
  10. Shehzad F, Zafaryab M, Ahmed R. Some series of proper generalized neighbor designs. J Stat Plan Infer. 2011;141(12):3808–3818. https://doi.org/10.1016/j.jspi.2011.06.016
  11. Ahmed R, Akhtar M. Designs balanced for neighbor effects in circular blocks of size six. J Stat Plan Inf. 2011;141(2):687–691. https://doi.org/10.1016/j.jspi.2010.07.018
  12. Ahmed R, Akhtar M. Construction of neighbor balanced designs in linear blocks. Stat-Theory Methods. 2011;40(17):1–8. https://doi.org/10.1080/03610926.2010.493278
  13. Shahid MR, Zakria M, Shehzad F, Ahmed, R. Some important classes of generalized neighbor designs in linear blocks. Commun Stat-Simul Comp. 2017;46(3):1991–1997. https://doi.org/10.1080/03610918.2015.1026990
  14. Shahid MR, Ahmed R, Shehzad F, Muhammad YS. Development of some useful generators to obtain partially neighbor balanced designs. J King Saud Uni Sci. 2019;31(1):24–26. https://doi.org/10.1016/j.jksus.2017.07.009
  15. Misra BL, Nutan B. Families of neighbor designs and their analysis. Commun Stat-Simul Comp. 1991;20(2-3):427–436. https://doi.org/10.1080/03610919108812963
  16. Chaure NK, Misra BL. On construction of generalized neighbor designs. Sankhya: Indian J Stat Ser B.1996;58(2):245–253.
  17. Mishra NS. Families of proper generalized neighbor designs. J Stat Plan Infer. 2007;13(5):1681–1686. https://doi.org/10.1016/j.jspi.2006.09.017
  18. Kedia RG, Misra BL. On construction of generalized neighbor design of use in serology. Stat Probab Lett. 2008;78(3):254–256. https://doi.org/10.1016/j.spl.2007.05.030
  19. Ahmed R, Akhtar M, Tahir MH. Economical generalized neighbor designs of use in Serology.  Comp Stat Data Anal. 2009;53(12):4584–4589. https://doi.org/10.1016/j.csda.2009.05.019
  20. Zafarya M, Shehzad F, Ahmed R. Proper generalized neighbor designs in circular blocks. J Stat Plan Infer. 2010;140(11):3498–3504. https://doi.org/10.1016/j.jspi.2010.05.021
  21. Iqbal I, Tahir MH, Aggarwal ML, Ali A, Ahmed, I. Generalized neighbor designs with block size 3. J Stat Plan Infer. 2012;142(3):626–632. https://doi.org/10.1016/j.jspi.2011.08.015
  22. Ahmed R, Akhtar M. Designs partially balanced for neighbor effects. Aligarh J Stat. 2012;32:41–53.
  23. Noreen K, Tahir MH, Rasheed M, Hassan MU, Ahmed R. Some important classes of non-directional minimal circular weakly balanced neighbor designs. Commun Stat-Simul Comp. 2023:1–10. https://doi.org/10.1080/03610918.2023.2196382
  24. Iqbal I. Construction of Experimental Designs Using Cyclic Shifts [dissertation]. University of Kent at Canterbury,UK: 1991.
  25. Pearce SC, Calinski T, Marshall TD. The basic contrasts of an experimental design with special reference to the analysis of data. Biometrika. 1974;61(3):449–460. https://doi.org/10.1093/biomet/61.3.449
  26. James AT, Wilkinson GN. Factorization of the residual operator and canonical decomposition of non-orthogonal factors in the analysis of variance. 1971;58(2):279–294. https://doi.org/10.1093/biomet/58.2.279
  27. Divecha J, Gondaliya J. Construction of minimal balanced Cross over designs having good efficiency of separability. Elect J Stat. 2014;8(2):2923–2936. https://doi.org/10.1214/14-EJS979