ngca                  package:fmri                  R Documentation

_N_o_n-_G_a_u_s_s_i_a_n _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:

     The function performs Non-Gaussian Component Analysis as described
     in Blanchard et.al. (2005).

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

     ngca(data,L=c(1000,1000,1000),T=10,m=3,eps=1.5,npca=min(dim(x)[2],dim(x)[1]),filter.time="None",filter.space=FALSE,method="temporal",h.space=3,h.time=3,keepv=FALSE)

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

    data: Observation matrix (dimension Nxd) 

       L: Number basis functions in each of four classes. 

       T: Number of Fast ICA iterations  

       m: Number of non-Gaussian components. 

     eps: Threshold (defaults to 1.5)

    npca: Reduce space to 'npca' principal components. This can be 
          used to avoid standardizing by numerically singular
          covariance matrices. In fMRI this allows to reduce the
          dimensionality assuming that the interesting non-Gaussian
          directions are also characterized by larger variances.

filter.time: Choice of temporal filtering before analysis: '"None"',
          '"Low"', '"Both"', '"High"' (default '"None"')

filter.space: Choice of spatial filtering before analysis: logical,
          default 'FALSE'

  method: Either '"spatial"' or '"temporal"'. Specifies the type of
          NGCA to perform. 

 h.space: bandwidth for spatial filtering. default 3

  h.time: bandwidth for temporal filtering. default 3

   keepv: if 'TRUE' intermediate results from fast ICA step are kept.

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

     The function performs Non-Gaussian Component Analysis as described
     in Blanchard et.al. (2006). The procedure uses four classes of
     basis functions, i.e. Gauss-Power3, Hyperbolic Tangent and the
     real and complex part of the Fourier class. See  Blanchard et.al.
     (2005) for details.

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

     The function returns a list with components 

   ihat : Matrix containing the first m NGCA directions as columns.

   sdev : Standard deviations of the principal components of  the
          thresholded ICA directions

   xhat : first m components of the rotated data

      v : If 'keepv==TRUE' the set of directions 'v^{(k)}'

  normv : If 'keepv==TRUE' the norm of each 'v^{(k)}'.

     ...

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

     J\"org Polzehl polzehl@wias-berlin.de

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

     Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V. and
     M\"uller  K.-R. (2005). In Search of Non-Gaussian Components of a
     High-Dimensional Distribution. Journal of Machine Learning
     Research. pp. 1-48.

