qpCov {qpgraph} | R Documentation

## 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

    qpCov(X, corrected=TRUE)

### Arguments

`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.

### 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

    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]))

[Package qpgraph version 2.14.0 Index]