AggregatePenalty        package:GeneSelector        R Documentation

_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 _b_y _a _v_a_r_i_a_n_c_e _p_e_n_a_l_t_y

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

     The idea behind this form of aggregation is to find 'reliable'
     candidate genes, i.e. those ones that are highly ranked and little
     variable at the same time. Higher variability is stronger
     penalized.

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

     AggregatePenalty(RR, lambda = NULL, k=5, theta = 50,
                          estimator = c("var", "mad", "iqr", "residuals"), ...)

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

      RR: An object of class 'RepeatRanking'.

  lambda: A positive real number, quantifying the amount of variance
          penalty. Default is 'NULL', an alternative parametrization
          using 'k' and 'theta' is used.

       k: Must be specified combined with 'theta', s.below. Not used if
          'lambda' is given.

   theta: A pragmatic way of finding an appropriate value for 'lambda'
          is to define some threshold rank 'theta' that is still
          considered relevant and some 'k >= 1' that expresses the
          impprtance of the first rank as compared to the threshold
          rank.

estimator: The variance estimator to be used:

          "_v_a_r" The usual variance estimator.

          "_m_a_d" Squared median absolute deviation. 

          "_i_q_r" Interquartile range.

          "_r_e_s_i_d_u_a_l_s" Residuals from a multivariate regression, s.
               StabilityLm

     ...: Further arguments passed to variance,RepeatRanking-method.

_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:

     AggregateSimple, AggregateBayes, AggregatePenalty, AggregatePCA

