qpNrr {qpgraph} | R Documentation |
Estimates non-rejection rates for every pair of variables.
## 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)
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. |
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
.
A symmetric matrix of estimated non-rejection rates.
R. Castelo and A. Roverato
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.
qpAvgNrr
qpEdgeNrr
qpHist
qpGraphDensity
qpClique
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])