qpEdgeNrr {qpgraph} | R Documentation |
Estimates non-rejection rate for one pair of variables.
## S4 method for signature 'ExpressionSet': qpEdgeNrr(data, N, i=1, j=2, q=1, nTests=100, alpha=0.05, long.dim.are.variables=TRUE, R.code.only=FALSE) ## S4 method for signature 'data.frame': qpEdgeNrr(data, N, i=1, j=2, q=1, nTests=100, alpha=0.05, long.dim.are.variables=TRUE, R.code.only=FALSE) ## S4 method for signature 'matrix': qpEdgeNrr(data, N, i=1, j=2, q=1, nTests=100, alpha=0.05, long.dim.are.variables=TRUE, R.code.only=FALSE)
data |
data set from where the non-rejection rate should be estimated. It
can be either an ExpressionSet object, a data frame, or a matrix.
If it is a matrix and the matrix is squared then this function assumes the
matrix is the sample covariance matrix of the data and the sample size
parameter N should be provided. |
N |
number of observations in the data set. Only necessary when the
sample covariance matrix is provided through the data parameter. |
i |
index or name of one of the two variables. |
j |
index or name of the other variable. |
q |
partial-correlation order. |
nTests |
number of tests to perform for each pair for variables. |
alpha |
significance level of each test. |
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. |
R.code.only |
logical; if FALSE then the faster C implementation is used (default); if TRUE then only R code is executed. |
The estimation of the non-rejection rate for a pair of variables is calculated as the fraction of tests that accept the null hypothesis of independence given a set of randomly sampled q-order conditionals.
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
.
An estimate of the non-rejection rate for the particular given pair of variables.
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.
qpNrr
qpAvgNrr
qpHist
qpGraphDensity
qpClique
# in this graph 3 is conditional independent of 4 given 1 AND 2 I <- matrix(c(FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE), nrow=4, ncol=4, byrow=TRUE) K <- qpI2K(I) X <- qpSampleMvnorm(K, N=100) qpEdgeNrr(X, i=3, j=4, q=1, long.dim.are.variables=FALSE) qpEdgeNrr(X, i=3, j=4, q=2, long.dim.are.variables=FALSE)