qpCov: Calculation of the sample covariance matrix
In qpgraph: Estimation of genetic and molecular regulatory networks from high-throughput genomics data

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

Calculates the sample covariance matrix, just as the function cov() but returning a dspMatrix-class object which efficiently stores such a dense symmetric matrix.

1	qpCov(X, corrected=TRUE)

`X`	data set from where to calculate the sample covariance matrix. As the `cov()` function, it assumes the columns correspond to random variables and the rows to multivariate observations.
`corrected`	flag set to `TRUE` when calculating the sample covariance matrix (default; and set to `FALSE` when calculating the uncorrected sum of squares and deviations.

This function makes the same calculation as the cov function but returns a sample covariance matrix stored in the space-efficient class dspMatrix-class and, moreover, allows one for calculating the uncorrected sum of squares and deviations which equals (n-1) * cov().

A sample covariance matrix stored as a dspMatrix-class object. See the Matrix package for full details on this object class.

R. Castelo

qpPCC

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))

S <- qpCov(X)

## estimate Pearson correlation coefficients by scaling the sample covariance matrix
R <- cov2cor(as(S, "matrix"))

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

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

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