uniTest-methods             package:xps             R Documentation

_A _T_w_o-_G_r_o_u_p _U_n_i_t_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     Unitest performs a a two group uni-test such as the 't.test' on
     each row of the expression dataframe. 
      The Unitest returns a dataframe containing the results of the
     test.

     _Usage_

     'uniTest(object)' 
      'uniTest(object, value)<-'

_A_r_g_u_m_e_n_t_s:

  object: object of class 'UniFilter'.

   value: character vector 'c(type, alternative, correction, numperm,
          mu, paired, conflevel, varequ)'

_D_e_t_a_i_l_s:

     The method 'uniTest' initializes the following parameters:

         'type':         a character string specifying the type of test: currently '"t.test"' (default) or '"normal.test"'.
         'alternative':  a character string specifying the alternative hypothesis, must be one of '"two.sided"' (default), '"greater"' or '"less"'.
         'correction':   a correction to adjust p-values for multiple comparisons:
                         'correction="none"': no correction (default).
                         'correction="bonferroni"': Bonferroni correction.
                         'correction="BH" or "fdr"': correction for false discovery rate (Benjamini & Hochberg).
                         'correction="BY"': correction for false discovery rate (Benjamini & Yekutieli).
                         'correction="hochberg"': Hochberg correction.
                         'correction="holm"': Holm correction.
                         'correction="wy"': Westfall-Young step-down adjusted p-chance (E.Manduchi).
         'numperm':      optional number of permutations  used to determine p-chance (default is 0).
         'mu':           a number indicating the true value of the difference in means for a two sample test (default is 0).
         'paired':       a logical indicating whether you want a paired uni-test (default is FALSE).
         'conflevel':    confidence level of the interval (default is 0.95).
         'varequ':       a logical variable indicating whether to treat the two variances as being equal. If 'TRUE' then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used (default is FALSE).

_V_a_l_u_e:

     An initialized 'UniFilter' object.

_A_u_t_h_o_r(_s):

     Christian Stratowa

_R_e_f_e_r_e_n_c_e_s:

     Benjamini, Y., and Hochberg, Y. (1995). Controlling the false
     discovery rate: a practical and powerful approach to multiple
     testing. _Journal of the Royal Statistical Society Series_ B,
     *57*, 289-300.

     Benjamini, Y., and Yekutieli, D. (2001). The control of the false
     discovery rate in multiple testing under dependency. _Annals of
     Statistics_ *29*, 1165-1188.

     Holm, S. (1979). A simple sequentially rejective multiple test
     procedure. _Scandinavian Journal of Statistics_, *6*, 65-70.

     Westfall P.H. and Young S.S. (1993) Resampling-based multiple
     testing:examples and methods for p-value adjustment. _Wiley series
     in probability and mathematical statistics_; Wiley.

     Dudoit S., Yang Y.H., Callow M.J., Speed T.P.  (2000)  Statistical
     methods for identifying differentially expressed genes in
     replicated cDNA microarray experiments.  _Technical report_ *578*;
     UC Berkeley.

     Manduchi E. (2000) Software: tpWY, see: <URL:
     http://www.cbil.upenn.edu/tpWY/>

_E_x_a_m_p_l_e_s:

     unifltr <- UniFilter()
     uniTest(unifltr) <- c("t.test","two.sided","none",0,0.0,FALSE,0.98,TRUE)
     str(unifltr)

