qpHist                package:qpgraph                R Documentation

_H_i_s_t_o_g_r_a_m_s _o_f _n_o_n-_r_e_j_e_c_t_i_o_n _r_a_t_e_s

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

     Plots the distribution of non-rejection rates.

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

     qpHist(nrrMatrix, K=NULL,
            titlehist = "all estimated\nnon-rejection rates", freq=TRUE)

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

nrrMatrix: matrix of non-rejection rates.

       K: concentration matrix of the generative distribution (whenever
          available).

titlehist: main title of the histogram(s).

    freq: logical; if TRUE, the histograms show frequencies (counts) of
          occurrence of the different non-rejection rate values; if
          FALSE, then probability densities are plotted

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

     This function plots histograms using the R-function 'hist' and
     therefore the way they are displayed follows that of this
     R-function.

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

     None

_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' 'qpGraphDensity' '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)

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

     qpHist(nrr.estimates, K)

