# Calculation of the sample covariance matrix

### Description

Calculates the sample covariance matrix, just as the function `cov()`

but returning a `dspMatrix-class`

object which efficiently
stores such a dense symmetric matrix.

### Usage

1 |

### Arguments

`X` |
data set from where to calculate the sample covariance matrix.
As the |

`corrected` |
flag set to |

### Details

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()`

.

### Value

A sample covariance matrix stored as a `dspMatrix-class`

object.
See the `Matrix`

package for full details on this object class.

### Author(s)

R. Castelo

### See Also

`qpPCC`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
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]))
``` |