qpNrr                package:qpgraph                R Documentation

_N_o_n-_r_e_j_e_c_t_i_o_n _r_a_t_e _e_s_t_i_m_a_t_i_o_n

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

     Estimates non-rejection rates for every pair of variables.

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

     ## S4 method for signature 'ExpressionSet':
     qpNrr(data, q=1, nTests=100, alpha=0.05, pairup.i=NULL,
                                     pairup.j=NULL, long.dim.are.variables=TRUE,
                                     verbose=TRUE, R.code.only=FALSE)
     ## S4 method for signature 'data.frame':
     qpNrr(data, q=1, nTests=100, alpha=0.05, pairup.i=NULL,
                                  pairup.j=NULL, long.dim.are.variables=TRUE,
                                  verbose=TRUE, R.code.only=FALSE)
     ## S4 method for signature 'matrix':
     qpNrr(data, q=1, nTests=100, alpha=0.05, pairup.i=NULL,
                              pairup.j=NULL, long.dim.are.variables=TRUE,
                              verbose=TRUE, R.code.only=FALSE)

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

    data: data set from where to estimate the non-rejection rates. It
          can be an ExpressionSet object, a data frame or a matrix.

       q: partial-correlation order to be employed.

  nTests: number of tests to perform for each pair for variables.

   alpha: significance level of each test.

pairup.i: subset of vertices to pair up with subset 'pairup.j'

pairup.j: subset of vertices to pair up with subset 'pairup.i'

long.dim.are.variables: logical; if TRUE it is assumed that when data
          are in a data frame or in a matrix, the longer dimension is
          the one defining the random variables (default); if FALSE,
          then random variables are assumed to be at the columns of the
          data frame or matrix.

 verbose: show progress on the calculations.

R.code.only: logical; if FALSE then the faster C implementation is used
          (default); if TRUE then only R code is executed.

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

     Note that the possible values of 'q' should be in the range 1 to
     'min(p,n-3)', where 'p' is the number of variables and 'n' the
     number of observations. The computational cost increases linearly
     with 'q' and quadratically in 'p'.

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

     A symmetric matrix of estimated non-rejection rates.

_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:

     'qpAvgNrr' 'qpEdgeNrr' 'qpHist' '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)

     summary(nrr.estimates[upper.tri(nrr.estimates) & I])

     summary(nrr.estimates[upper.tri(nrr.estimates) & !I])

