errRates: Calculating FDR, FNDR, FPR, and FNR for a real microarray...
In GeneSelectMMD: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions

Description Usage Arguments Details Value Author(s) References Examples

View source: R/errRates.R

Calculating FDR, FNDR, FPR, and FNR for a real microarray data set based on the mixture of marginal distributions.

1	errRates(obj.gsMMD)

obj.gsMMD

an object returned by gsMMD, gsMMD.default, gsMMD2, or gsMMD2.default

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)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Programming is running. Please be patient...
Data transformation ( boxcox ) performed
Gene profiles are scaled so that they have mean zero and variance one!
Programming is running. Please be patient...
******** initial parameter estimates method>> Ttest  *******
paraIniMatRP[,i]>>
    pi.1     pi.2    mu.c1   tau.c1     r.c1 delta.n1   tau.n1     r.n1 
   0.050    0.870    0.556   -0.505   -2.599   -0.056   -0.058   -3.026 
    mu.2    tau.2      r.2    mu.c3   tau.c3     r.c3 delta.n3   tau.n3 
  -0.002    0.008   -3.565   -0.659   -0.667   -1.678    0.093   -0.224 
    r.n3 
  -2.541 

********** loop=
[1] 0
********** loop=
[1] 1
********** loop=
[1] 2
********** loop=
[1] 3
Total iterations for EM algorithm= 3 
llkhVec>>
    Ttest 
-1615.003 

*******************************************************

Initial parameter estimates>>
           Ttest
pi.1       0.050
pi.2       0.870
pi.3       0.080
mu.c1      0.556
sigma2.c1  0.604
rho.c1     0.003
mu.n1     -0.389
sigma2.n1  0.944
rho.n1     0.001
mu.2      -0.002
sigma2.2   1.008
rho.2      0.000
mu.c3     -0.659
sigma2.c3  0.513
rho.c3     0.097
mu.n3      0.439
sigma2.n3  0.799
rho.n3     0.029

Initial loglikelihood>>
    Ttest 
-1774.942 

Final loglikelihood based on initial estimates>>
    Ttest 
-1615.003 

Final parameter estimates>>
     pi.1      pi.2      pi.3     mu.c1 sigma2.c1    rho.c1     mu.n1 sigma2.n1 
    0.056     0.896     0.048     0.557     0.559    -0.069    -0.390     0.903 
   rho.n1      mu.2  sigma2.2     rho.2     mu.c3 sigma2.c3    rho.c3     mu.n3 
   -0.047    -0.002     0.981    -0.027    -0.841     0.282     0.036     0.569 
sigma2.n3    rho.n3 
    0.616    -0.039 

Final loglikelihood>>
    Ttest 
-1615.003 

*******************************************************

  FDR  FNDR   FPR   FNR 
0.027 0.000 0.003 0.000