samrocN                 package:SAGx                 R Documentation

_C_a_l_c_u_l_a_t_e _R_O_C _c_u_r_v_e _b_a_s_e_d _S_A_M _s_t_a_t_i_s_t_i_c

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

     Calculation of the regularised t-statistic which minimises  the
     false positive and false negative rates.

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

     samrocN(data=M,formula=~as.factor(g), contrast=c(0,1), N = c(50, 100, 200, 300),B=100, perc = 0.6, 
      smooth = FALSE, w = 1, measure = "euclid", probeset = NULL)

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

    data: The data matrix

 formula: a linear model formula

contrast: the contrast to be estimnated 

       N: the size of top lists under consideration

       B: the number of bootstrap iterations

    perc: the largest eligible percentile of SE to be used as fudge
          factor

  smooth: if TRUE, the std will be estimated as a smooth function of
          expression level

       w: the relative weight of false positives

 measure: the goodness criterion

probeset: probeset ids;if NULL then "probeset 1", "probeset 2", ... are
          used.

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

     The test statistic is based on the one in Tusher et al (2001):


                         d = diff / (s_0 + s)


     where diff is a the estimate of a constrast, s_0 is the
     regularizing constant  and s the standard error.  At the heart of
     the method lies an estimate of the false negative and false
     positive rates. The test is calibrated so that these are
     minimised. For calculation of p-values a bootstrap procedure is
     invoked. Further details are given in Broberg (2003). Note that
     the definition of p-values follows that in Davison and Hinkley
     (1997), in order to avoid p-values that equal zero.

     The p-values are calculated through permuting the rows of the
     design matrix for the columns such that the coresponding contrast 
     coefficient is not zero. This means that factors not tested are
     kept fixed. NB This may be adequate for testing a factor with two
     levels, but it  is not adequate for all linear models.

     samrocN calls the function Xprep which has been improved in terms
     of speed.

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

     An object of class samroc.result.

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

     Per Broberg

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

     Tusher, V.G., Tibshirani, R., and Chu, G. (2001) Significance
     analysis of microarrays applied to the ionizing radiation
     response. _PNAS_ Vol. 98, no.9, pp. 5116-5121

     Broberg, P. (2002) Ranking genes with respect to differential
     expression , <URL:
     http://genomebiology.com/2002/3/9/preprint/0007>

     Broberg. P: Statistical methods for ranking differentially
     expressed genes. Genome Biology 2003, 4:R41 <URL: 
     http://genomebiology.com/2003/4/6/R41>

     Davison A.C. and Hinkley D.V. (1997) Bootstrap Methods and Their
     Application. Cambridge University Press

