aoptgdtd {Aoptbdtvc} | R Documentation |
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 <mandal.stat@gmail.com>
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)