qpPCC: Estimation of Pearson correlation coefficients

Description

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

Usage

"qpPCC"(X)
"qpPCC"(X, long.dim.are.variables=TRUE)
"qpPCC"(X, long.dim.are.variables=TRUE)

Arguments

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.

Value

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

Details

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.

Examples

Run this code

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]))