Title: | Factorial Optimal Designs for Two-Colour cDNA Microarray Experiments |
---|---|
Description: | Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments. |
Authors: | Dibaba Bayisa Gemechu, Legesse Kassa Debusho, and Linda Haines |
Maintainer: | Dibaba Bayisa Gemechu <[email protected]> |
License: | GPL-2 |
Version: | 1.0.3 |
Built: | 2024-11-19 06:29:59 UTC |
Source: | CRAN |
Used to compute factorial A-, D- or E-optimal designs for two-colour cDNA microarray experiments.
factoptd(narys, Optcrit = "", desvect,...) ## Default S3 method: factoptd(narys, Optcrit = "", desvect,...) ## S3 method for class 'factoptd' print(x, ...)
factoptd(narys, Optcrit = "", desvect,...) ## Default S3 method: factoptd(narys, Optcrit = "", desvect,...) ## S3 method for class 'factoptd' print(x, ...)
narys |
integer, specifying number of arrays. |
desvect |
matrix, specifying design vectors (see Debusho, Haines and Gemechu (2014) for more details). |
Optcrit |
character, specifying the optimality criteria to be used. |
x |
the object to be printed. |
... |
not used. |
factoptd
computes factorial optimal designs for the two-colour cDNA microarray experiments for a given design vectors and number of arrays by making use to the complete enumeration methods proposed in Debusho, Haines and Gemechu (2014).
Returns resultant factorial A-, D- or E-optimal design(s) with their corresponding score value. Specifically:
call |
the method call. |
b |
number of arrays. |
desvect |
Design vestors |
Optcrit |
optimality criteria. |
tnfd |
Total number of resultant optimal factorial design(s) |
optfctd |
obtained factorial optimal design. Each row of |
optscv |
score value of the optimality criteria |
NB: The function factoptd
also saves the summary of the resultant factorial optimal design(s) in .csv format in the R session's temporary directory.
Dibaba Bayisa Gemechu, Legesse Kassa Debusho, and Linda Haines
Debusho, L. K., Gemechu, D. B. and Haines, L. M. (2014). Optimal Factorial Designs for Two-Colour Microarray Experiments: Properties Of Admissible Designs, A-, D- And E-Optimality Criteria. Peer-reviewed Proceedings of the Annual Conference of the South African Statistical Association for 2014 (SASA 2014), Rhodes University, Grahmstown, South Africa. pp 17 - 24, ISBN: 978-1-86822-659-7.
##To obtain factorial A-optimal design for a given ##design vector using 9 slides/arrays, set narys <- 9 #Number of arrays desvect = rbind(c(0,2,-2),c(-2,0,-2), c(-2,2,0),c(0,2,2), c(-2,0,2),c(-2,-2,0)) #Design vector Optcrit <- "A" #Optimality criteria factoptdA <- factoptd(narys = 9, Optcrit = "A", desvect = rbind(c(0,2,-2),c(-2,0,-2),c(-2,2,0),c(0,2,2),c(-2,0,2),c(-2,-2,0))) print(factoptdA)
##To obtain factorial A-optimal design for a given ##design vector using 9 slides/arrays, set narys <- 9 #Number of arrays desvect = rbind(c(0,2,-2),c(-2,0,-2), c(-2,2,0),c(0,2,2), c(-2,0,2),c(-2,-2,0)) #Design vector Optcrit <- "A" #Optimality criteria factoptdA <- factoptd(narys = 9, Optcrit = "A", desvect = rbind(c(0,2,-2),c(-2,0,-2),c(-2,2,0),c(0,2,2),c(-2,0,2),c(-2,-2,0))) print(factoptdA)