msecalcmult              package:LMGene              R Documentation

_M_S_E _c_a_l_c_u_l_a_t_i_o_n _f_u_n_c_t_i_o_n

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

     Computes the mean square error and gradient for the global anova

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

     msecalcmult(eS, lam, alpha, lowessnorm=FALSE, R, grads=TRUE)

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

      eS: Array data. must be exprSet type. 

     lam: A parameter for glog transformation 

   alpha: A parameter for glog transformation 

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

       R: The residual matrix, i.e., identity minus the hat matrix

   grads: If true, return gradient as well as error. Not used with some
          kinds of optimization.

_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:

   msev : A vector which contains MSE and gradient of two parameters

_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(sample.eS)

     lmod <- GetLMObj(sample.eS)
     X <- lmod$x
     U <- svd(X)$u
     H = crossprod(t(U), t(U))
     n = dim(H)[1]
     R = diag(rep(1,n)) - H

     msecalc(sample.eS,500,50, FALSE, R)

