tranest                package:LMGene                R Documentation

_G_l_o_g _t_r_a_n_s_f_o_r_m_a_t_i_o_n _p_a_r_a_m_e_t_e_r _e_s_t_i_m_a_t_i_o_n _f_u_n_c_t_i_o_n

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

     Finds the best parameters for glog transformation.

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

     tranest(eS, ngenes = -1, starting = FALSE, lambda = 1000, alpha = 0, gradtol = 0.001, lowessnorm = FALSE)

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

      eS: Array data. must be exprSet type. 

  ngenes: Number of genes that is going to be used for the parameter
          estimation 

starting: TRUE, if the given initial parameter values are used 

  lambda: Initial parameter value for lambda 

   alpha: Initial parameter value for alpha 

 gradtol: a positive scalar giving the tolerance at which the scaled
          gradient is considered close enough to zero to terminate the
          algorithm 

lowessnorm: TRUE, if lowess method is going to be used 

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

     The input argument, eS, must be exprSet type from Biobase package.
      If you have a matrix data and information about the considered
     factors, then you can use 'neweS' to conver the data into exprSet.
     Please see 'neweS' in more detail.

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

tranpar : A list containing the best parameter for 'lambda' and 'alpha'

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

     David Rocke and Geun-Cheol Lee

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

     B. Durbin and D.M. Rocke, (2003) Estimation of Transformation
     Parameters for Microarray Data,  Bioinformatics, 19, 1360-1367.

     <URL: http://www.idav.ucdavis.edu/~dmrocke/>

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

     'jggrad2', 'tranest2'

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

     #library
     library(Biobase)
     library(LMGene)

     #data
     data(Smpd0)

     tranpar <- tranest(Smpd0, 100, FALSE, 500, 50, 1e-3, FALSE)
     tranpar
     tranpar <- tranest(Smpd0, -1, FALSE, 500, 50, 1e-3, FALSE)
     tranpar

