errRates {GeneSelectMMD} | R Documentation |
Calculating FDR, FNDR, FPR, and FNR for a real microarray data set based on the mixture of marginal distributions.
errRates(obj.gsMMD)
obj.gsMMD |
an object returned by |
We first fit the real microarray data set by the mixture of marginal distributions. Then we calculate the error rates based on the posterior distributions of a gene belonging to a gene cluster given its gene profiles. Please refer to Formula (7) on the page 6 of the paper listed in the Reference section.
A vector of 4 elements:
FDR |
the percentage of nondifferentially expressed genes among selected genes. |
FNDR |
the percentage of differentially expressed genes among unselected genes. |
FPR |
the percentage of selected genes among nondifferentially expressed genes |
FNR |
the percentage of un-selected genes among differentially expressed genes |
Jarrett Morrow remdj@channing.harvard.edu, Weiliang Qiu Weiliang.Qiu@gmail.com, Wenqing He whe@stats.uwo.ca, Xiaogang Wang stevenw@mathstat.yorku.ca, Ross Lazarus ross.lazarus@channing.harvard.edu
Qiu, W.-L., He, W., Wang, X.-G. and Lazarus, R. (2008). A Marginal Mixture Model for Selecting Differentially Expressed Genes across Two Types of Tissue Samples. The International Journal of Biostatistics. 4(1):Article 20. http://www.bepress.com/ijb/vol4/iss1/20
## Not run: library(ALL) data(ALL) eSet1 <- ALL[1:100, ALL$BT == "B3" | ALL$BT == "T2"] mem.str <- as.character(eSet1$BT) nSubjects <- length(mem.str) memSubjects <- rep(0,nSubjects) # B3 coded as 0, T2 coded as 1 memSubjects[mem.str == "T2"] <- 1 obj.gsMMD <- gsMMD(eSet1, memSubjects, transformFlag = TRUE, transformMethod = "boxcox", scaleFlag = TRUE, quiet = FALSE) round(errRates(obj.gsMMD), 3) ## End(Not run)