aoptgdtd {Aoptbdtvc}

A-optimal group divisible treatment designs

Description

This function generates A-optimal group divisible treatment (GDT) designs for test vs control comparisons with specified parameters

Usage

aoptgdtd(m,n,b,k,ntrial)

Arguments

m

number of rows such that m*n = number of test treatments

n

number of columns such that m*n = number of test treatments

b

number of blocks

k

block size

ntrial

number of trials, default is 5

Value

It either returns a text message or a design. If a design is found, it returns a list with following components

parameters

parameters of the design

design

generated A-optmal GDT design

N

incidence matrix of the generated A-optmal GDT design

NNP

concurrence matrix of the generated design

Note

The function is useful to construct A-optimal GDT designs for number of test treatments <= 30 and up to block size 10. May not be very useful for m*n > 30. For k<=3, designs with larger number of test treatment may be obtained.

Author(s)

Baidya Nath Mandal <[e-mail]>

References

Jacroux, M. (1989). The A-optimality of block designs for comparing test treatments with a control, Journal of the American Statistical Association 84(405), 310-317.

Mandal, B. N., Parsad, R. and Dash, S. (2017). A-optimal block designs for comparing test treatments with control treatment(s) - an algorithmic approach, upcoming project report, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.

Examples

## construct an A-optimal GDT design with 12 (= 4 x 3) test treatments 
##in 12 blocks each of size 6
aoptgdtd(m=4,n=3,b=12,k=6)
## construct an A-optimal GDT design with 8 (= 4 x 2) test treatments 
##in 8 blocks each of size 4
aoptgdtd(m=4,n=2,b=8,k=4)
##design does not exist
aoptgdtd(4,2,8,2)
##Design not found
## Not run: aoptgdtd(3,3,15,3)