AggregateSimple         package:GeneSelector         R Documentation

_S_i_m_p_l_e _a_g_g_r_e_g_a_t_i_o_n _o_f _r_e_p_e_a_t_e_d _r_a_n_k_i_n_g_s

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

     All rankings obtained from perturbed datasets plus the ranking
     from the original dataset are aggregated via 'aggregatefun'.

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

     AggregateSimple(RR, S, aggregatefun = c("mode", "mean", 
                      "median", "quantile"), q = NULL)

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

      RR: An object of class 'RepeatRanking'.

       S: Either an object of class 'StabilityLm' or
          'StabilityOverlap'.

aggregatefun: The statistic to return as aggregation.

          _m_o_d_e The rank occuring most frequently. If two or more ranks
               occur equally often, then weights are used (s.details)

          _m_e_a_n A weighted mean is used. For information on weights,
               s.details. 

          _m_e_d_i_a_n The median of all observed ranks is used.

          _q_u_a_n_t_i_l_e The 'q'-quantile of all observed ranks is used.

       q: Only specified if 'aggregatefun=quantile'

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

     The weights used if 'aggregatefun=mode' or  'aggregatefun=mean'
     decrease linear with decreasing similarity of perturbed dataset
     and original dataset as measured by Stability Measures (object
     'S').

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

     An object of class AggregatedRanking.

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

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

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

     GetRepeatRanking, GetStabilityLm, GetStabilityOverlap,
     AggregateBayes

_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 RankingTstat
     ordT <- RankingTstat(xx, yy, type="unpaired")
     ### Generate Leave-one-out Foldmatrix
     loo <- GenerateFoldMatrix(xx, yy, k=1)
     ### Get all rankings
     loor_ordT <- GetRepeatRanking(ordT, loo)
     ### compute stability measure
     stab_overlap <- GetStabilityOverlap(loor_ordT, decay="linear")
     ### aggregate rankings
     agg_simple_ordT <- AggregateSimple(loor_ordT, stab_overlap, aggregatefun="mean")

