qpSampleMvnorm            package:qpgraph            R Documentation

_S_a_m_p_l_e _m_u_l_t_i_v_a_r_i_a_t_e _n_o_r_m_a_l _o_b_s_e_r_v_a_t_i_o_n_s

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

     Samples independent observations from a multivariate normal
     distribution with a given mean vector and a given concentration
     matrix.

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

     qpSampleMvnorm(K, N, mean = rep(0, nrow(K)))

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

       K: concentration matrix of the multivariate normal distribution.

       N: number of observations to sample.

    mean: mean vector of the multivariate normal distribution.

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

     This function requires the 'mvtnorm' package. This function is
     designed to be used to sample multivariate normal observations
     from a randomly generated concentration matrix that has a
     zero-structure reflecting the conditional independencies from a,
     possibly also randomly generated, undirected graph. The function
     'qpI2K' can be employed to obtain such a concentration matrix.

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

     A matrix where rows correspond to observations and columns to
     random variables.

_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:

     'qpI2K'

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

     nVar <- 50 # number of variables
     maxCon <- 5  # maximum connectivity per variable
     nObs <- 30 # number of observations to simulate

     I <- qpRndGraph(n.vtx=nVar, n.bd=maxCon)
     K <- qpI2K(I)

     X <- qpSampleMvnorm(K, nObs)

     dim(X)

