Error Error In Parameter Estimate Covariance Computation. Sas
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Proc Genmod Error In Parameter Estimate Covariance Computation
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Consulting Quick Question Consultations Hourly Statistical Consulting Results Section Review Statistical Project Services Free Webinars Webinar Recordings Contact Customer Login Statistically Speaking Login Workshop Center Login All Logins When the Hessian Matrix http://www.theanalysisfactor.com/wacky-hessian-matrix/ goes Wacky by Karen Grace-Martin If you have run mixed models much at all, you have undoubtedly been haunted by some version of this very obtuse warning: “The Hessian (or G or D) Matrix is not positive definite. Convergence has stopped.” Or “The Model has not Converged. Parameter Estimates from the last iteration are displayed.” What on earth does that mean? Let’s start error in with some background. If you’ve never taken matrix algebra, these concepts can be overwhelming, so I’m going to simplify them into the basic issues that arise for you, the data analyst. If you’d like a more mathematical and thorough answer, see one of the references. The D Matrix (called G by SAS) is the matrix of the variances and covariances of the error in parameter random effects. The variances are listed on the diagonal of the matrix and the covariances are on the off-diagonal. So a model with a random intercept and random slope (two random effects) would have a 2×2 D matrix. The variances of the intercept and slope would be on the diagonal and their covariance would be in the one non-diagonal place. Without getting into the math, a matrix can only be positive definite if the entries on the main diagonal are non-zero and positive. This makes sense for a D matrix, because we definitely want variances to be positive (remember variances are squared values). The Hessian Matrix is based on the D Matrix, and is used to compute the standard errors of the covariance parameters. The iterative algorithms that estimate these parameters are pretty complex, and they get stuck if the Hessian Matrix doesn’t have those same positive diagonal entries. The result is you can’t trust the reported results, no matter how much they look like the results you usually get. The software was unable to come up with stable estimates. It means that for some r