qpAvgNrr               package:qpgraph               R Documentation

_A_v_e_r_a_g_e _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 average non-rejection rates for every pair of variables.

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

     ## S4 method for signature 'ExpressionSet':
     qpAvgNrr(data, qOrders=4, nTests=100, alpha=0.05,
                                        pairup.i=NULL, pairup.j=NULL,
                                        long.dim.are.variables=TRUE,
                                        type=c("arith.mean"), verbose=TRUE,
                                        R.code.only=FALSE)
     ## S4 method for signature 'data.frame':
     qpAvgNrr(data, qOrders=4, nTests=100, alpha=0.05,
                                     pairup.i=NULL, pairup.j=NULL,
                                     long.dim.are.variables=TRUE,
                                     type=c("arith.mean"), verbose=TRUE,
                                     R.code.only=FALSE)
     ## S4 method for signature 'matrix':
     qpAvgNrr(data, qOrders=4, nTests=100, alpha=0.05,
                                 pairup.i=NULL, pairup.j=NULL,
                                 long.dim.are.variables=TRUE,
                                 type=c("arith.mean"), verbose=TRUE,
                                 R.code.only=FALSE)

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

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

 qOrders: either a number of partial-correlation orders or a vector of
          vector of particular orders to be employed in the
          calculation.

  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 the
          data is a data frame or a matrix, the longer dimension is the
          one defining the random variables; if FALSE, then random
          variables are assumed to be at the columns of the data frame
          or matrix.

    type: type of average. By now only the arithmetic mean is
          available.

 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 when specifying a vector of particular orders 'q', these
     values 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 within each 'q' value and
     quadratically in 'p'.

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

     A symmetric matrix of estimated average 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. Reverse engineering molecular
     regulatory networks from microarray data with qp-graphs. _J. Comp.
     Biol., accepted_, 2008.

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

     'qpNrr' '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)

     avgnrr.estimates <- qpAvgNrr(X, verbose=FALSE)

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

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

