cmatbrcd.mae {soptdmaeA}

Computes the treatment information matrix

Description

Computes the information matrix (C-matrix) for treatment effects under either the linear fixed effects model or the linear mixed effects model setting for a given block or row-column design.

Usage

cmatbrcd.mae(trt.N, blk.N, theta, des, dtype)

Arguments

trt.N

integer, specifying number of treatments v of design des.

blk.N

integer, specifying number of arrays (blocks or columns) b of design des.

theta

numeric, representing a function of the ratio of random array variance and random error variance. It takes any value between 0 and 1, inclusive.

des

matrix, a 2 x b block or row-column design with b arrays and v treatments.

dtype

character, specifying the design type. For block designs, dtype = "blkd" and for row-column deigns, dtype = "rcd".

Value

Returns a v x v treatment information matrix (C-matrix).

Author(s)

Dibaba Bayisa Gemechu, Legesse Kassa Debusho, and Linda Haines

References

Debusho, L. K., Gemechu, D. B., and Haines, L. M. (2016). Algorithmic construction of optimal block designs for two-colour cDNA microarray experiments using the linear mixed model. Under review.

Gemechu D. B., Debusho L. K. and Haines L. M. (2014). A-optimal designs for two-colour cDNA microarray experiments using the linear mixed effects model. Peer-reviewed Proceedings of the Annual Conference of the South African Statistical Association for 2014 (SASA 2014), Rhodes University, Grahamstown, South Africa. pp 33-40, ISBN: 978-1-86822-659-7.

See Also

soptdmaeA, fixparsoptd.mae

Examples

##Information matrix

     trt.N <- 4 
     blk.N <- 4 
     theta <- 0.3 
     dsgn <- rbind(1:4,c(2:4,1))
     dtype <- "rcd"

     cmatbrcd.mae(trt.N = 4, blk.N = 4, theta = 0.2, des = dsgn, dtype = "rcd")