qpPCC: Estimation of Pearson correlation coefficients
In qpgraph: Estimation of genetic and molecular regulatory networks from high-throughput genomics data

Description Usage Arguments Details Value Author(s) See Also Examples

Estimates Pearson correlation coefficients (PCCs) and their corresponding P-values between all pairs of variables from an input data set.

## S4 method for signature 'ExpressionSet'
qpPCC(X)
## S4 method for signature 'data.frame'
qpPCC(X, long.dim.are.variables=TRUE)
## S4 method for signature 'matrix'
qpPCC(X, long.dim.are.variables=TRUE)

`X`	data set from where to estimate the Pearson correlation coefficients. It can be an ExpressionSet object, a data frame or a matrix.
`long.dim.are.variables`	logical; if TRUE it is assumed that when `X` is a data frame or 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.

The calculations made by this function are the same as the ones made for a single pair of variables by the function cor.test but for all the pairs of variables in the data set, with the exception of the treatment of missing values, since only complete observations across all variables in X are used.

A list with two matrices, one with the estimates of the PCCs and the other with their P-values.

R. Castelo and A. Roverato

qpPAC

require(graph)
require(mvtnorm)

nVar <- 50 ## number of variables
nObs <- 10 ## number of observations to simulate

set.seed(123)

g <- randomEGraph(as.character(1:nVar), p=0.15)

Sigma <- qpG2Sigma(g, rho=0.5)
X <- rmvnorm(nObs, sigma=as.matrix(Sigma))

pcc.estimates <- qpPCC(X)

## get the corresponding boolean adjacency matrix
A <- as(g, "matrix") == 1

## Pearson correlation coefficients of the present edges
summary(abs(pcc.estimates$R[upper.tri(pcc.estimates$R) & A]))

## Pearson correlation coefficients of the missing edges
summary(abs(pcc.estimates$R[upper.tri(pcc.estimates$R) & !A]))