mlica                 package:mlica                 R Documentation

_M_a_x_i_m_u_m _l_i_k_e_l_i_h_o_o_d _i_m_p_l_e_m_e_n_t_a_t_i_o_n _o_f _I_n_d_e_p_e_n_d_e_n_t _C_o_m_p_o_n_e_n_t _A_n_a_l_y_s_i_s

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

     This function performs ICA using a maximum likelihood framework
     and takes as arguments parameters to control the number of
     algorithm runs and convergence criteria.

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

     mlica(prNCP, nruns = 10, tol = 1e-04, maxit = 300, fail.th = 5, learn.mu = 1)

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

   prNCP: The output object from 'proposeNCP'.

   nruns: The number of converged algorithm runs sought (function
          returns the best solution according to the log-likelihood
          value). 

     tol: Tolerance level for establishing convergence of run.

   maxit: Maximum number of iterations to allow per run. 

 fail.th: A threshold on the number of consecutive runs that fail to
          converge.

learn.mu: Learning parameter for fixed point algorithm (note that this
          need not be changed since it has already been optimised).

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

     A list with following components: 

       A: Estimate of the mixing matrix.

       B: Estimate of the inverse mixing matrix.

       S: Estimate of the source matrix.

       X: Normalised data matrix.

     ncp: Number of independent components.

      NC: Binary number specifying whether best run converged or
          not.(=1 indicates convergence,=0 indicates no convergence).

      LL: Log likelihood value of best run.

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

     Andrew Teschendorff aet21@cam.ac.uk

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

_1 Hyvaerinen A., Karhunen J., and Oja E.: _Independent Component
     Analysis_, John Wiley and Sons, New York, (2001).

_2 Kreil D. and MacKay D. (2003): _Reproducibility Assessment of
     Independent Component Analysis of Expression Ratios from DNA
     microarrays_, Comparative and Functional Genomics *4* (3),300-317.

_3 Liebermeister W. (2002): _Linear Modes of gene expression determined
     by independent component analysis_, Bioinformatics *18*, no.1,
     51-60.

_4 Chiappetta P., Roubaud MC. and Torresani B.: _Blind source separation
     and the analysis of microarray data_, J. Comput. Biol. 2004;
     11(6):1090-109.

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

     data(simMAdata);
     dataX <- simMAdata[[1]];
     prPCA <- PriorNormPCA(dataX);
     prNCP <- proposeNCP(prPCA,0.1);
     a.best.l <- list();
     for( i in 1:5){
      a.best.l[[i]] <- mlica(prNCP,nruns=5);
     }
     checkICA <- CheckStability(a.best.l,0.7);
     sourceS <- simMAdata[[3]];
     print(cor(a.best.l[[1]]$S,sourceS));
     sModes <- SortModes(a.best.l[[1]],c.val=0.5);

