arrayspc             package:elasticnet             R Documentation

_S_p_a_r_s_e _P_C_s _o_f _M_i_c_r_o_a_r_r_a_y_s

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

     Sparse PC by iterative SVD and soft-thresholding

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

     arrayspc(x,K=1,para,use.corr=FALSE, max.iter=100,trace=FALSE,eps=1e-3)

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

       x: The microarray matrix. 

       K: Number of components. Default is 1. 

    para: The thresholding parameters. A vector of length K.  

use.corr: Perform PCA on the correlation matrix? This option is only
          effective when the argument type is set "data".

max.iter: Maximum number of iterations.

   trace: If TRUE, prints out its progress.

     eps: Convergence criterion.

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

     The function is equivalent to a special case of spca() with the
     quadratic penalty=infinity. It is specifically designed for the
     case p>>n, like microarrays.

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

     A "arrayspc" object is returned.

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

     Hui Zou, Trevor Hastie and Robert Tibshirani

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

     Zou, H., Hastie, T. and Tibshirani, R. (2004) "Sparse principal
     component analysis" _Technical report, Statistics Dept. Stanford
     University_

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

     spca, princomp

