acprob                 package:amap                 R Documentation

_R_o_b_u_s_t _p_r_i_n_c_i_p_a_l _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:

     Robust principal component analysis / Analyse en composantes
     principales robuste

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

     acprob(x,h,center=TRUE,reduce=TRUE,kernel="gaussien")

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

       x: Matrix  / data frame

       h: Scalar: bandwidth of the Kernel

  kernel: The kernel used. This must be one of '"gaussien"', 
          '"quartic"', '"triweight"', '"epanechikov"' ,  '"cosinus"' or
          '"uniform"' 

  center: A logical value indicating whether we center data

  reduce: A logical value indicating whether we "reduce" data i.e. 
          divide each column by standard deviation

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

     'acpgen'  compute robust pca. i.e. spectral analysis of a robust
     variance instead of usual variance. Robust variance: see 'varrob'

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

     An object of class *acp*  The object is a list with components:

    sdev: the standard deviations of the principal components.

loadings: the matrix of variable loadings (i.e., a matrix whose columns
          contain the eigenvectors).  This is of class '"loadings"':
          see 'loadings' for its 'print' method.

  scores: if 'scores = TRUE', the scores of the supplied data on the
          principal components.

     eig: Eigen values

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

     Antoine Lucas, <URL: http://genopole.toulouse.inra.fr/~lucas/amap>

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

     acp princomp

