sinCG                  package:SIN                  R Documentation

_S_I_N _f_o_r _c_h_a_i_n _g_r_a_p_h_s

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

     This function computes the matrix of simultaneous p-values for SIN
     model selection for chain graphs.  SIN assumes that  a dependence
     chain or blocking of the variables is available.

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

     sinCG(blocks, S, n, type="AMP")

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

  blocks: a list of integer vectors with entries amongst 1,...,p where
          p is the number of variables.  Each one of the integer
          vectors specifies a set of variables that form a block in the
          chain graph. Furthermore, a partial ordering of the variables
          is specified by the convention that variables in one block
          are ordered smaller than variables in a block succeeding in
          the list 'blocks'.

       S: a covariance or correlation matrix.

       n: the sample size.

    type: a string equal to either '"AMP"' or '"LWF"' that determines
          whether the chain graph is interpreted with respect to the
          Andersson, Madigan, Perlman Markov property or the Lauritzen,
          Wermuth, Frydenberg Markov property.

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

     A matrix of simultaneous p-values with 'NA' on the diagonal.

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

     Drton, M. & Perlman, M.D.  (2004)  Model Selection for Gaussian
     Concentration Graphs. _Biometrika_, to appear. 

      Drton, M. & Perlman, M.D.  (2004)  A SINful Approach to Gaussian
     Graphical Model Selection.  _Statistical Science_, to appear. 

      Drton, M. & Perlman, M.D.  (2003)  A SINful Approach to Model
     Selection for Gaussian Concentration Graphs. Department of
     Statistics, University of Washington, Technical Report 429.
      <URL:
     http://www.stat.washington.edu/www/research/reports/2003/tr429.pdf
     > 

      Andersson, S.A., Madigan, D. & Perlman, M.D.  (2001) Alternative
     Markov Properties for Chain Graphs. _Scandinavian Journal of
     Statistics_ 28(1): 33-85. 

      Lauritzen, S.  (1996) _Graphical Models_.  Oxford University
     Press: Oxford.

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

     'plotCGpvalues'

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

     data(fowlbones)
     p <- dim(fowlbones$corr)[1]
     blocks <- list(1:2,3:4,5:6)
     sinCG(blocks,fowlbones$corr,fowlbones$n, type="AMP")
     sinCG(blocks,fowlbones$corr,fowlbones$n, type="LWF")
     holm(sinCG(blocks,fowlbones$corr,fowlbones$n, type="AMP"))
     holm(sinCG(blocks,fowlbones$corr,fowlbones$n, type="LWF"))

