qpGraphDensity            package:qpgraph            R Documentation

_D_e_n_s_i_t_i_e_s _o_f _r_e_s_u_l_t_i_n_g _q_p-_g_r_a_p_h_s

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

     Calculates and plots the graph density as function of the
     non-rejection rate.

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

     qpGraphDensity(nrrMatrix, threshold.lim=c(0,1), breaks=5,
                    plot=TRUE, qpGraphDensityOutput=NULL,
                    density.digits=0,
                    titlegd="graph density as function of threshold")

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

nrrMatrix: matrix of non-rejection rates.

threshold.lim: range of threshold values on the non-rejection rate.

  breaks: either a number of threshold bins or a vector of threshold
          breakpoints.

    plot: logical; if TRUE makes a plot of the result; if FALSE it does
          not.

qpGraphDensityOutput: output from a previous call to 'qpGraphDensity'.
          This allows one to plot the result changing some of the
          plotting parameters without having to do the calculation
          again.

density.digits: number of digits in the reported graph densities.

 titlegd: main title to be shown in the plot.

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

     The estimate of the sparseness of the resulting qp-graphs is
     calculated as one minus the area enclosed under the curve of graph
     densities.

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

     A list with the graph density as function of threshold and an
     estimate of the sparseness of the resulting qp-graphs across the
     thresholds.

_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:

     'qpNrr' 'qpAvgNrr' 'qpEdgeNrr' 'qpClique'

_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)

     # the higher the q the sparser the qp-graphs

     nrr.estimates <- qpNrr(X, q=1, verbose=FALSE)

     qpGraphDensity(nrr.estimates, plot=FALSE)$sparseness

     nrr.estimates <- qpNrr(X, q=5, verbose=FALSE)

     qpGraphDensity(nrr.estimates, plot=FALSE)$sparseness

