compNorm               package:nnNorm               R Documentation

_C_o_m_p_a_r_e_s _t_h_e _d_i_s_t_r_i_b_u_t_i_o_n _o_f _s_e_v_e_r_a_l _d_a_t_a _s_e_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function was concieved to easily compare several
     normalization methods in terms  of variability of log-ratios, M.
     Basically it produces two plots: The first is a the density  plot
     of the several matrices passed as arguments, while the second is a
     box plot.  Median of absolute deviations for each method is
     printed on screen.

_U_s_a_g_e:

     compNorm(x,...,bw="AUTO",xlim=c(-3,3),titles="AUTO",type="d") 

_A_r_g_u_m_e_n_t_s:

       x: A matrix of numerical values, e.q. the M values of a data
          set: 'maM(swirl)'. 

     ...: An undefined number of objects similar with 'x'. 

      bw: Band width required to compute the density distribution.
          '"AUTO"'  will adjust 'bw' to a suitable value. 

    xlim: The range for abscissa of the density plots.  

  titles: Names to be displayed the charts legend. '"AUTO"' will use
          the matrices names passed as arguments.              . 

    type: If set to '"d"', density plot will be shown; if set to '"d"'
          box plot will be shown. 

_D_e_t_a_i_l_s:

     This function is used to compare the normalized log ratios M
     obtained with several normalization methods.

_V_a_l_u_e:

     NULL, this function only displays charts and prints on the screen
     some statistics.

_A_u_t_h_o_r(_s):

     Tarca, A.L.

_R_e_f_e_r_e_n_c_e_s:

     A. L. Tarca, J. E. K. Cooke, and J. Mackay. Robust neural networks
     approach for spatial and intensity dependent normalization of cDNA
     data. Bioinformatics. 2004,submitted.

_S_e_e _A_l_s_o:

     'maNormNN'

_E_x_a_m_p_l_e_s:

      # Normalize swirl data with two methods
      data(swirl)
      swirlNN<-maNormNN(swirl[,1])
      swirlLoess<-maNormMain(swirl[,1]) 
      nms<-c("None","Loess","NNets")
      #compare distributions: density plot
      compNorm(maM(swirl[,1]),maM(swirlLoess),maM(swirlNN),xlim=c(- 2,2),bw="AUTO",titles=nms,type="d")
      #compare distributions: box plot
      compNorm(maM(swirl[,1]),maM(swirlLoess),maM(swirlNN),xlim=c(- 2,2),bw="AUTO",titles=nms,type="b")

