rowpAUCs             package:genefilter             R Documentation

_R_o_w_w_i_s_e _R_O_C _a_n_d _p_A_U_C

_D_e_s_c_r_i_p_t_i_o_n:

     Fast rowwise calculation of ROC curves and pAUC.

_U_s_a_g_e:

     rowpAUCs(x, fac, cutpts, p=0.1)

_A_r_g_u_m_e_n_t_s:

       x: 'exprSet' or numeric matrix. The matrix must not contain 'NA'
          values.

     fac: Factor; if 'x' is an 'exprSet', this may also be a character
          vector of length 1 with the name of a covariate variable in
          'x'. 'fac' must have exactly 2 levels.

  cutpts: Matrix with same number of rows as 'x' or vector of length
          greater than 1. It specifies the thresholds for the
          calculation of the ROC curves. Smaller numbers may further
          speed up computations. If missing, the ROC curves are
          calculated between data points.

       p: Numeric vector of length 1. Limit in (0,1) to integrate pAUC
          to.

_D_e_t_a_i_l_s:

     Rowwise calculation of Receiver Operating Characteristic (ROC)
     curves and the corresponding partial area under the curve (pAUC)
     for a given data matrix or 'exprSet'. The function is implemented
     in C and thus reasonably fast and memory efficient.

     The definition of the pAUC uses a naive trapezoidal rule which,
     although less accurate, is faster than more elaborate integrators.

_V_a_l_u_e:

     A list with the calculated specificities and sensitivities for
     each row as matrices, and the corresponding pAUCs.

_A_u_t_h_o_r(_s):

     Florian Hahne <f.hahne@dkfz.de>

_R_e_f_e_r_e_n_c_e_s:

     Pepe MS, Longton G, Anderson GL, Schummer M.:  Selecting
     differentially expressed genes from microarray experiments.
     _Biometrics. 2003 Mar;59(1):133-42._

_S_e_e _A_l_s_o:

     'rocdemo.sca, pAUC'

_E_x_a_m_p_l_e_s:

       
        data(eset)

        r1 = rowttests(eset, "cov2")
        r2 = rowpAUCs(eset, "cov2")

      if(interactive()) {
        plot(r2$pAUC, r1$statistic, pch=16)
        plot(1-r2$spec[1,], r2$sens[1,], pch=16)
      }