Maybe you can try the code below to generate semi-definite positive matrix
set.seed(2)
v <- runif(183,0.6,0.8)
corr <- `diag<-`(tcrossprod(v),1)
I am trying to make a random matrix correlation over 183 variables to calculate a Cholesky decomposition and correlate 183 random normals. For the creation of the correlation matrix the following reproducible code is the one I am using:
First I set the seed
set.seed(2)
Then I generate random numbers between 0.6 and 0.8
corr <- matrix(runif(183*183, min = 0.6, max = 0.8), 183, 183)
The next step is turning the diagonal into ones
for (i in 1:183) {
for (j in 1:183) {
if (i == j) {
corr[i,j] <- 1
}
}
}
Last step is making it symmetric
for (i in 1:183) {
for (j in 1:183) {
if (i < j) {
corr[i,j] <- corr[j,i]
}
}
}
The problem I run into arise when I try to make the cholesky decomposition
cholesky <- chol(corr)
I get the following error:
Error in chol.default(corr) : the leading minor of order 14 is not positive definite
How could I make my correlation matrix semi definite positive?
Maybe you can try the code below to generate semi-definite positive matrix
set.seed(2)
v <- runif(183,0.6,0.8)
corr <- `diag<-`(tcrossprod(v),1)
upvoted. maybe can improve on the answer by referring to literature on numerical stability of covariance matrices. and why using t(X) %*% X helps to generate a stable one