qp {qp} | R Documentation |
This package provides functions for implementing the q-order partial-correlation graph search algorithm, q-partial, or qp, algorithm for short. The qp algorithm is a robust procedure for structure learning of undirected Gaussian graphical Markov models (UGGMMs) from "small n, large p" data, that is, multivariate normal data coming from a number of random variables p larger than the number of multidimensional data points n as in the case of, e.g., microarray data.
jmlr06data
synthetic data used in the referenced article
qp.search
calculates the estimates of the non-rejection
rates for every pair of variables
qp.edge.prob
calculates the estimate of the non-rejection
rate for a particular pair of variables, this function is also called
by qp.search
qp.ci.test
performs a test for conditional independence
qp.analyse
provides some exploratory analyses on the
output of qp.search
qp.clique
calculates the maximum clique size as a function
of the minimum threshold on the non-rejection rate for removing an edge
qp.hist
shows a histogram of the estimated non-rejection
rates obtained through qp.search
qp.graph
returns the qp-graph, in the form of an incidence
matrix, resulting of thresholding the non-rejection rates in the output
of qp.search
qp.matrix.image
makes an image plot of the absolute value
of an inverse correlation matrix
qp.get.cliques
finds the set of cliques of an undirected
graph
The package provides an implementation of the procedures described by Castelo and Roverato (2006) and is a contribution to the gR-project described by Lauritzen (2002).
Robert Castelo, Departament de Ci`encies Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.
Alberto Roverato, Dipartimento di Scienze Statistiche, Universit`a di Bologna, Italy.
Lauritzen, S. L. (2002). gRaphical Models in R. R News, 3(2)39.
Castelo, R. and Roverato, A. (2006). A robust procedure for Gaussian graphical model search from microarray data with p larger than n, J. Mach. Learn. Res., 7:2621-2650