Calculation of the sample covariance matrix
Calculates the sample covariance matrix, just as the function
but returning a
dspMatrix-class object which efficiently
stores such a dense symmetric matrix.
data set from where to calculate the sample covariance matrix.
flag set to
This function makes the same calculation as the
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
Matrix package for full details on this object class.
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]))
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.