cmatbd: Computes the treatment information matrix

In Boptbd: Bayesian Optimal Block Designs

Description

The function cmatbd 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 design of size 2.

Usage

cmatbd(trt.N, blk.N, theta, des)

Arguments

trt.N	integer, specifying number of treatments, v.
blk.N	integer, specifying number of blocks, b.
theta	numeric, representing function of the ratio of random block variance and random error variance. It takes any value between 0 and 1, inclusive.
des	matrix, a 2 x b block design with b blocks of size k = 2 and v treatments.

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. (2018). Algorithmic construction of optimal block designs for two-colour cDNA microarray experiments using the linear mixed effects model. Communications in Statistics - Simulation and Computation, https://doi.org/10.1080/03610918.2018.1429617.

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

Boptbd, fixparBbd, intcbd

Examples

##Information matrix

     trt.N <- 3 

     blk.N <- 3 

     theta <- 0.2 

     des <- intcbd(trt.N = 4, blk.N = 3)

     cmatbd(trt.N, blk.N, theta, des)

Boptbd documentation built on March 26, 2020, 8:38 p.m.