qpRndGraph              package:qpgraph              R Documentation

_R_a_n_d_o_m _u_n_d_i_r_e_c_t_e_d _g_r_a_p_h_s _w_i_t_h _m_a_x_i_m_u_m _c_o_n_n_e_c_t_i_v_i_t_y _d_e_g_r_e_e

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

     Builds a random undirected graph with a bounded maximum
     connectivity degree (boundary) on every vertex.

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

     qpRndGraph(n.vtx, n.bd)

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

   n.vtx: number of vertices.

    n.bd: maximum boundary for every vertex.

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

     This is a very simple function to generate random undirected
     graphs where we impose a maximum order of correlation between
     disconnected vertices when using it to sample multivariate normal
     data reflecting the conditional independencies encoded in this
     graph. Note that the maximum order of correlation between two
     disconnected vertices is bounded by the minimum degree of
     connectivity of the two vertices.

     The algorithm employed is not designed to enforce a uniform
     probability distribution on the set of graphs with the given
     maximum boundary that may be sampled with positive probability.

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

     The incidence matrix of the resulting graph.

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

     R. Castelo and A. Roverato

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

     Castelo, R. and Roverato, A. A robust procedure for Gaussian
     graphical model search from microarray data with p larger than n,
     _J. Mach. Learn. Res._, 7:2621-2650, 2006.

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

     'qpSampleMvnorm'

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

     nVertices <- 50 # number of vertices
     maxCon <- 5  # maximum connectivity per vertex

     I <- qpRndGraph(n.vtx=nVertices, n.bd=maxCon)

     summary(rowSums(I))

