RankingSoftthresholdT      package:GeneSelector      R Documentation

_R_a_n_k_i_n_g _v_i_a _t_h_e '_s_o_f_t-_t_h_r_e_s_h_o_l_d' _t-_s_t_a_t_i_s_t_i_c

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

     The 'soft-threshold' statistic is constructed using a linear
     regression model with the 'L1' penalty (also referred to as LASSO
     penalty). In special cases (like here) the LASSO estimator can be 
     calculated analytically and is then called 'soft threshold'
     estimator (Wu,2005).
      For 'S4' method information, see RankingSoftthresholdT-methods.

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

     RankingSoftthresholdT(x, y, type = c("unpaired", "paired", "onesample"),
                           lambda = c("lowess", "cor", "user"), userlambda = NULL, 
                           gene.names = NULL, ...)

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

       x: A 'matrix' of gene expression values with rows corresponding
          to genes and columns corresponding to observations or
          alternatively an object of class 'ExpressionSet'.
           If 'type = paired', the first half of the columns
          corresponds to  the first measurements and the second half to
          the second ones.  For instance, if there are 10 observations,
          each measured twice, stored in an expression matrix 'expr', 
          then 'expr[,1]' is paired with 'expr[,11]', 'expr[,2]' with
          'expr[,12]', and so on.

       y: If 'x' is a matrix, then 'y' may be a 'numeric' vector or a
          factor with at most two levels.
           If 'x' is an 'ExpressionSet', then 'y' is a character
          specifying the phenotype variable in the output from 'pData'.
           If 'type = paired', take care that the coding is analogously
          to the requirement concerning 'x'


          "_u_n_p_a_i_r_e_d": two-sample test.

          "_p_a_i_r_e_d": paired test. Take care that the coding of 'y' is
               correct (s. above)

          "_o_n_e_s_a_m_p_l_e": 'y' has only one level.  Test whether the true
               mean is different from zero.


  lambda: s. details

userlambda: A user-specified value for 'lambda', s. details.

gene.names: An optional vector of gene names.

     ...: Currently unused argument.

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

     There are currently three ways of specifying the shrinkage
     intensity 'lambda'. Both '"lowess"' and '"cor"' are relatively
     slow, especially if rankings are repeated (GetRepeatRanking).
     Therefore, a 'reasonable' value can be set by the user.

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

     An object of class 'GeneRanking'.

_N_o_t_e:

     The code is a modified version of that found in the 'st' package
     of Opgen-Rhein and Strimmer (2007).

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

     Martin Slawski martin.slawski@campus.lmu.de 
      Anne-Laure Boulesteix <URL: http://www.slcmsr.net/boulesteix>

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

     Wu, B. (2005). Differential gene expression using penalized linear
     regression models: The improved SAM statistic. _Bioinformatics,
     21, 1565-1571_

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

     GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT,
     RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma,
      RankingEbam, RankingWilcEbam, RankingSam,  RankingBstat,
     RankingShrinkageT,   RankingPermutation, RankingGap

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

     ### Load toy gene expression data
     data(toydata)
     ### class labels
     yy <- toydata[1,]
     ### gene expression
     xx <- toydata[-1,]
     ### run RankingSoftthresholdT
     softt <- RankingSoftthresholdT(xx, yy, type="unpaired")

