qpAvgNrr {qpgraph} | R Documentation |
Estimates average non-rejection rates for every pair of variables.
## 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)
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. |
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
.
A symmetric matrix of estimated average non-rejection rates.
R. Castelo and A. Roverato
Castelo, R. and Roverato, A. Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comp. Biol., accepted, 2008.
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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])