Create positive-definite 3x3 covariance matrix given specified correlation values Let's say I have 3 variables, A, B, and C and I want to generate data where A and B are correlated at r=x, A and C are correlated at r=y, and B and C are correlated at r=z. 


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*Is there any algorithm that will tell me, given specified values for x, y, and z, if any set of variances for A, B and C will yield a positive definite covariance matrix? 

*Given the existence of such an algorithm, is there another algorithm that, in cases where a positive definite covariance matrix is possible, will give me a set of variances that achieve a positive definite matrix?

 A: To follow up on @cardinal's comment: your $x$, $y$, and $z$ define a $(3 \times 3)$ correlation matrix $R$. Since a correlation matrix also is a possible covariance matrix (of standardized variables), it has to be positive definite. This is the case if all eigenvalues are $> 0$. If $R$ is indeed positive definite, then all vectors $\boldsymbol{s}$ of variances (i.e., numbers $> 0$) will turn $\boldsymbol{R}$ into a positive definite covariance matrix $\boldsymbol{\Sigma} = \boldsymbol{D}_{s}^{1/2} \boldsymbol{R} \boldsymbol{D}_{s}^{1/2}$, where $\boldsymbol{D}_{s}^{1/2}$ is the square root of the diagonal matrix made from $\boldsymbol{s}$.
So just construct $R$ from $x, y, z$, and check if the eigenvalues are all $> 0$. If so, you're good, and you can transform any set of data to have a corresponding covariance matrix with arbitrary variances:
x <- 0.5
y <- 0.3                            # changing this to -0.6 makes it not pos.def.
z <- 0.4
R <- matrix(numeric(3*3), nrow=3)   # will be the correlation matrix
diag(R) <- 1                        # set diagonal to 1
R[upper.tri(R)] <- c(x, y, z)       # fill in x, y, z to upper right
R[lower.tri(R)] <- c(x, y, z)       # fill in x, y, z to lower left
eigen(R)$values                     # get eigenvalues to check if pos.def.

gives
[1] 1.8055810 0.7124457 0.4819732

So our $\boldsymbol{R}$ here is positive definite. Now construct the corresponding covariance matrix from arbitrary variances.
vars  <- c(4, 16, 9)                # the variances
Sigma <- diag(sqrt(vars)) %*% R %*% diag(sqrt(vars))

Generate some data matrix $\boldsymbol{X}$ that we will transform to later have exactly that covariance matrix.
library(mvtnorm)                    # for rmvnorm()
N  <- 100                           # number of simulated observations
mu <- c(1, 2, 3)                    # some arbitrary centroid
X  <- round(rmvnorm(n=N, mean=mu, sigma=Sigma))

To do that, we first orthonormalize matrix $\boldsymbol{X}$, giving matrix $\boldsymbol{Y}$ with covariance matrix $\boldsymbol{I}$ (identity).
orthGS <- function(X) {             # implement Gram-Schmidt algorithm
    Id <- diag(nrow(X))
    for(i in 2:ncol(X)) {
        A <- X[ , 1:(i-1), drop=FALSE]
        Q <- qr.Q(qr(A))
        P <- tcrossprod(Q)
        X[ , i] <- (Id-P) %*% X[ , i]
    }
    scale(X, center=FALSE, scale=sqrt(colSums(X^2)))
}

Xctr <- scale(X, center=TRUE, scale=FALSE)  # centered version of X
Y    <- orthGS(Xctr)                        # Y is orthonormal

Transform matrix $\boldsymbol{Y}$ to have covariance matrix $\boldsymbol{\Sigma}$ and centroid $\boldsymbol{\mu}$.
Edit: what's going on here: Do a spectral decomposition $\boldsymbol{\Sigma} = \boldsymbol{G} \boldsymbol{D} \boldsymbol{G}^{t}$, where $\boldsymbol{G}$ is the matrix of normalized eigenvectors of $\boldsymbol{\Sigma}$, and $\boldsymbol{D}$ is the corresponding matrix of eigenvalues. Now matrix $\boldsymbol{G} \boldsymbol{D}^{1/2} \boldsymbol{Y}$ has covariance matrix $\boldsymbol{G} \boldsymbol{D}^{1/2} Cov(\boldsymbol{Y}) \boldsymbol{D}^{1/2} \boldsymbol{G}^{t} = \boldsymbol{G} \boldsymbol{D} \boldsymbol{G}^{t} = \boldsymbol{\Sigma}$, as $Cov(\boldsymbol{Y}) = \boldsymbol{I}$.
eig    <- eigen(Sigma)
A      <- eig$vectors %*% sqrt(diag(eig$values))
XX1ctr <- t(A %*% t(Y)) * sqrt(nrow(Y))
XX1    <- sweep(XX1ctr, 2, mu, "+")         # move centroid to mu

Check that the correlation matrix is really $\boldsymbol{R}$.
> all.equal(cor(XX1), R)
[1] TRUE

For other purposes, the question might now be: How do I find a positive definite matrix that is "very similar" to a pre-specified one that is not positive definite. That I don't know.
Edit: corrected some square roots
A: If you're not given a complete collection of correlations, but only a partial set, then you can construct a semidefinite programming feasibility problem that can be used to determine whether or not there is a correlation matrix that has the specified correlations.  This can be solved by various SDP solvers such as SeDuMi and SDPT3.  
