tranestmult              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 _f_o_r _m_u_l_t_i_p_l_e _p_a_r_a_m_e_t_e_r_s

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

     A sub-function of 'tranest' which searches the best parameters for
     glog transformation.

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

     tranestmult (eS, starting = FALSE, lambda = 1000, alpha = 0, gradtol = 0.001, lowessnorm=FALSE, method=1, max_iter=200, model=NULL)

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

      eS: Array data. must be exprSet type. 

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: Set optimization method; default is modified Gauss-Newton
          (nlm). See 'tranest'.

max_iter: Max. number of iterations of 'nlm' to use in optimization.

   model: Model in terms of vlist which is compared to transformed
          expression data. See 'tranest'.

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

     This is primarily an internal function. The normal way of calling
     it would  be to call 'tranest' with the option mult=TRUE. The
     input argument eS must be exprSet type from Biobase package.  If
     you have 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 (not a vector) containing the best parameter for
          'lambda' and the best vector for '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:

     'tranest', 'tranest2'

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

     #library
     library(Biobase)
     library(LMGene)

     #data
     data(sample.eS)

     tranpar <- tranestmult(sample.eS, lambda= 500, alpha=50)
     tranpar

