conf.design {conf.design} | R Documentation |
Construct designs with specified treatment contrasts confounded with blocks.
conf.design(G, p, block.name="Blocks", treatment.names=<<see below>>)
G |
Matrix whose rows define the contrasts to be confounded. For example in a
3^4 experiment with A B^2 C and B C D confounded with blocks (together with
their generalized interactions), the matrix G would be rbind(c(1,2,1,0)),
c(0,1,1,1)). The number of columns of G is the number of factors.
|
p |
The common number of levels for each factor. Must be a prime number. |
block.name |
Name to be given to the factor defining the blocks of the design. |
treatment.names |
Name to be given to the treatment factors of the design. If G has a dimnames attribute, then dimnames[[2]] is the default, otherwise T1, T2, |
For a single replicate of treatments, blocks are calculated using the confounded contrasts in the standard textbook way. The method is related to that of Collings (1989).
A design with a Blocks factor defining the blocks and Treatment factors defining the way treatments are allocated to each plot. Not in random order.
None.
Collings, B. J. (1989) Quick confounding. Technometrics, v31, pp107-110.
conf.set, direct.sum, fac.design, fractionate
# Generate a 3^4 factorial with A B^2 C and B C D confounded with blocks. d34 <- conf.design(rbind(c(1,2,1,0), c(0,1,1,1)), p=3, treatment.names = LETTERS[1:4])