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, method=1, mult=FALSE, model=NULL)

_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 

  method: Determines optimization method. Default is 1, which
          corresponds to a Newton-type method (see 'nlm'). Method 2 is
          based on the Nelder-Mead method (see 'optim').

    mult: If true, tranest will use a vector alpha with one entry per
          sample. Default is false (same alpha for every sample).

   model: Specifies model to be used. Default is to use all variables
          from eS without interactions. See details.

_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.

     'model' is an optional character string, constructed like the
     right-hand side of a formula for lm. It specifies which of the
     variables in the exprSet will be used in the model and whether
     interaction terms will be included. If model=NULL, it uses all
     variables from the exprSet without interactions. Be careful of
     using interaction terms with factors: this often leads to
     overfitting, which will yield an error.

_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, Geun-Cheol Lee and John Tillinghast

_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/>

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

     #library
     library(Biobase)
     library(LMGene)

     #data
     data(sample.eS)

     tranpar <- tranest(sample.eS, 100)
     tranpar
     tranpar <- tranest(sample.eS, mult=TRUE)
     tranpar

