dksPerm                package:dualKS                R Documentation

_E_s_t_i_m_a_t_e _s_i_g_n_i_f_i_c_a_n_c_e _o_f _s_i_g_n_a_t_u_r_e _s_c_o_r_e_s.

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

     The distribution of Kolmogorov Rank Sum scores generated by  this
     package depends on a variety of factors including the size of the
     signature and the total number of genes measured in each  sample. 
     For a given classifier, this function bootstraps an  approximate
     distribution for the scores and then identifies  optimum
     parameters for the gamma distribution that best fits the 
     bootstrap distribution.  The corresponding gamma probability 
     function is then returned, allowing p-values for one or more 
     scores to be readily computed.

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

     dksPerm(eset, class, n=100, samples=100, type="up", rescale=FALSE, 
                     verbose=FALSE, method="kort")

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

    eset: An 'ExpressionSet' or 'matrix' containing the gene 
          expression data to be used for bootstrapping.

   class: A factor with two or more levels indicating which  class each
          sample in the expression set belongs OR  an integer
          indicating which column of pData(eset)  contains this
          information.

       n: The number of genes per class to use in the bootstrap 
          signature.

 samples: The number of bootstrap samples to generate.  A  value of at
          least 1000 give good results, but may take a  a while.

    type: One of "up", "down", or "both".   See 'dksTrain'.

 rescale: Logical indicating whether scores should be rescaled  to
          range c(0,1).

 verbose: Set to TRUE if you want more evidence of progress while data
          is being processed.  Set to FALSE if you  want your CPU
          cycles to be used on analysis and not  printing messages.

  method: One of either 'kort' or 'yang'.  Should match that  used for
          'dksTrain'.

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

     A function (1-pgamma(x, ...)) with the appropriate parameters 
     preset based on log likelihood maximization relative to the
     bootstrapped  distribution.

_N_o_t_e:

     All arguments should match those used by 'dksClassify', otherwise
     the estimated p-values will not meaningfully describe  the
     distribution of scores generated by that function.

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

     Eric J. Kort

_S_e_e _A_l_s_o:

     'dksTrain', 'dksSelectGenes', 'dksClassify', 'DKSGeneScores', 
     'DKSPredicted',  'DKSClassifier'

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

             data("dks")
             p.value <- dksPerm(eset, 1, samples=25)
             # this is not nearly enough samples, but will suffice for 
             # the demonstration.  See the vignette for more informative 
             # example.
             p.value(250)
             p.value(1500)

