qpGraph               package:qpgraph               R Documentation

_T_h_e _q_p-_g_r_a_p_h

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

     Obtains a qp-graph from a matrix of non-rejection rates

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

     qpGraph(nrrMatrix, threshold=NULL, topPairs=NULL, pairup.i=NULL, pairup.j=NULL,
             return.type=c("incidence.matrix", "edge.list", "graphNEL", "graphAM"))

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

nrrMatrix: matrix of non-rejection rates.

threshold: threshold on the non-rejection rate above which pairs of
          variables are assumed to be disconnected in the resulting
          qp-graph.

topPairs: number of edges from the top of the ranking, defined by the
          non-rejection rates in 'nrrMatrix', to use to form the
          resulting qp-graph. This parameter is incompatible with a
          value different from 'NULL' in 'threshold'.

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

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

return.type: type of data structure on which the resulting undirected
          graph should be returned. Either a logical incidence matrix
          with cells set to TRUE when the two indexing variables are
          connected in the qp-graph (default), or a list of edges in a
          matrix where each row corresponds to one edge and the two
          columns contain the two vertices defining each edge, or a
          'graphNEL-class' object, or a 'graphAM-class' object.

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

     This function requires the 'graph' package when
     'return.type=graphNEL' or 'return.type=graphAM'.

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

     The resulting qp-graph as either an incidence matrix, a 'graphNEL'
     object or a 'graphAM' object, depending on the value of the
     'return.type' parameter. Note that when some gold-standard graph
     is available for comparison, a value for the parameter 'threshold'
     can be found by calculating a precision-recall curve with
     'qpPrecisionRecall' with respect to this gold-standard, and then
     using 'qpPRscoreThreshold'. Parameters 'threshold' and 'topPairs'
     are mutually exclusive, that is, when we specify with 'topPairs=n'
     that we want a qp-graph with 'n' edges then 'threshold' cannot be
     used.

_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' 'qpAnyGraph' 'qpGraphDensity'
     'qpClique' 'qpPrecisionRecall' 'qpPRscoreThreshold'

_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)

     # the higher the threshold
     g <- qpGraph(nrr.estimates, threshold=0.9)

     # the denser the qp-graph
     (sum(g)/2) / (nVar*(nVar-1)/2)

     # the lower the threshold
     g <- qpGraph(nrr.estimates, threshold=0.5)

     # the sparser the qp-graph
     (sum(g)/2) / (nVar*(nVar-1)/2)

