AggregateBayes         package:GeneSelector         R Documentation

_B_a_y_e_s_i_a_n _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:

     The aggregated rank results from a posterior characteristic
     (argument 'posteriorfun' below). The discrete prior is
     symmetrically centered around the rank obtained from the original
     dataset. The Likelihood is based on a normal distribution with
     variance 'sigma' (s. below).

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

     AggregateBayes(RR, S, tau, sigma = c("MAD", "sd"), 
                     posteriorfun = 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'.

     tau: The prior variance. Controls the confidence  in the rank
          obtained from the original dataset.
           Should not be too large (<=1) in order to save computing
          time.

   sigma: How the standard deviation for the Likelihood is to be
          estimated from the data (=ranks from perturbed datasets).
          '"MAD"' is a (weighted) MAD,  '"sd"' a (weighted) standard
          deviation.

posteriorfun: Which statistic should be applied to the posterior
          distribution as a summary. If '"quantile"' is chosen, then it
          should be specified via the argument 'q'. 

       q: The posterior quantile used as summary statistic.
           Only used if 'posteriorfun' is '"quantile"'

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

     The prior has support only in the range '[r0-2*tau;r0+2*tau]',
     where 'r0' is the prior mode (rank from the original dataset).
      The weights for the estimation of 'sigma' decrease linearly 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,
     AggregateSimple

_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_ordT <- AggregateBayes(loor_ordT, stab_overlap, tau=1)

