Hi Mark, The problem you're having is that the estimated covariance matrix of your data is singular, or non-invertible. Thus, when Amelia tries to invert the matrix to obtain imputations, things fall apart. This can happen for a number of reasons, but it is usually due to some sort of (nearly) linear dependence in the columns of the data. This can happen if there are linear functions of other variables (a common problem here is accidentally leaving all of the dummies for a categorical variable in the model). One way to track down the problem variables is the following: svd(data)$d < .0005 The values that are TRUE from this output correspond to the variables in the dataset that are causing problems (0.0005 is ad hoc, you're just looking for really small values). If this isn't the problem, it is possible that there is too little data to estimate the covariance matrix. You can always add what we call the empirical or ridge prior that makes estimation easier when the covariance matrix is nearly singular. The argument for this is "empri" and a good value to start with is 1% of the rows of the data. One more issue is that it is possible that your choice of prior could be leading the algorithm into parts of the parameter space with singular covariance matrices, especially if you are putting strong priors on the variables. This is especially possible if you are putting variable-wide priors (as you are doing). If you are trying to impose logical constraints on the data, it might be preferable to to transform the variable onto an unbounded scale, impute and then retransform. This allows the constraints to bind without necessarily putting heavy prior beliefs on the model. (I sent an email to the list a few weeks ago detailing how this might be done for more complex cases). Lastly, we're trying to put together some more informative error messages for this problem. Sorry for the crypticism. Hope that helps, matt. On Sun, Mar 16, 2008 at 6:51 PM, Mark Manger &lt;mark.manger(a)mcgill.ca&gt; wrote: - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia