errRates            package:GeneSelectMMD            R Documentation

_C_a_l_c_u_l_a_t_i_n_g _F_D_R, _F_N_D_R, _F_P_R, _a_n_d _F_N_R _f_o_r _a _r_e_a_l _m_i_c_r_o_a_r_r_a_y _d_a_t_a _s_e_t

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     errRates(obj.gsMMD)

_A_r_g_u_m_e_n_t_s:

obj.gsMMD: an object returned by 'gsMMD', 'gsMMD.default', 'gsMMD2', or
          'gsMMD2.default' 

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     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

_A_u_t_h_o_r(_s):

     Weiliang Qiu stwxq@channing.harvard.edu, Wenqing He
     whe@stats.uwo.ca, Xiaogang Wang stevenw@mathstat.yorku.ca, Ross
     Lazarus ross.lazarus@channing.harvard.edu

_R_e_f_e_r_e_n_c_e_s:

     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._ <URL:
     http://www.bepress.com/ijb/vol4/iss1/20>

_E_x_a_m_p_l_e_s:

       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)

