There are many reasons for lack of convergence. You might be getting close before the iteration limit is reached. Suggest you add these options to the procedure statement:            maxiter=1000 maxfunc=1000 You should look at the iteration history to see if the algorithm is getting close to convergence, but is jumping around a lot in terms of the "objective". You could see if convergence is achieved if you relax theconvergence criterion to      convg=1e-6 (the default is convg=1e-8). The latter is a bit dangerous, because you may not be at the optimum solution. But this can help in checking things out.   You are fitting a straight-line model to the data. If the moisture trend with year is not very straight, then convergence would also be an issue. ... View more

I somewhat disagree with the 'solution' post. With he PRIOR statement, one is getting true Bayesian analysis with PROC MIXED. See the very nice presentation in chapter 13 of the book SAS for Mixed Models, 2nd edition (2006). One just does not have much control over the priors. A Jeffrey's prior is used for the variance parameters (these are based on the data, but are considered to be non-informative). A flat (constant) improper prior is used for the fixed effects. There is direct sampling from the posteriors (using a independence chain). With these restrictions, the posterior is conditionally multivariate normal. The statistics paper describing the approach in MIXED is: Wolfinger and Kass (2000). Non-conjugate Bayesian Analysis of Variance Component Models. BIOMETRICS 56:768-774.   For more flexible approaches to Bayesian anlaysis, one has to use the other listed procedures. ... View more

It pays to read the User's Guide. The getting started example shows you exactly how to fit a nonlinear model. This is a different model from what you want, but you can see where you need to write the equation and define the parameters. I added the plot statement to show you how easy it is to get predicted values and confidence/prediciton intervals. /* From SAS User's Guide */ data Enzyme; input Concentration Velocity @@; datalines; 0.26 124.7 0.30 126.9 0.48 135.9 0.50 137.6 0.54 139.6 0.68 141.1 0.82 142.8 1.14 147.6 1.28 149.8 1.38 149.4 1.80 153.9 2.30 152.5 2.44 154.5 2.48 154.7 ; proc nlin data=Enzyme method=marquardt hougaard plots=all; parms theta1=155 theta2=0 to 0.07 by 0.01; model Velocity = theta1*Concentration / (theta2 + Concentration); run; Just change the model and parms statements. It is important to have good starting values.  You also mentioned class variables. NLIN does not have a class statement. You would have to use dummy variables (0,1) for the different class levels. But this gets more complicated, and I am not describing this here, since you are apparently new to this. You would find the textbook by Schabenberger and Pierce (2002) very helpful for this. With class variables, you have to decide what parameter is being affected by the factor (which parameter is being affected by the factor). ... View more

No, it is unbalanced, whether you put a missing value record or drop those records. ... View more

Yes, the time variable appears in two different forms (with two different names) in the proc. The one version is a class variable (used just in the repeated statement) and the other version is continuous, used in the model statement, etc. The use of the class time variable (whatever you call it) assures that observations are tracked properly in constructing the R covariance matrix. It should take care of missing values, etc. There are examples of this in several places, including in the SAS for Mixed Models, 2nd edtion book. With the memory problem, you may need to switch to HPMIXED procedure. ... View more

Sometimes it is very hard to find the problem observation. Your message says subject 4. Look carefully. ... View more

  You probably have more than one observation per class level per subject for the repeated measure. You can only have one value for each time for each factor level. Ths is usually due to a typing error entering the data.  See this excelelnt article about errors with mixed model procedures. http://support.sas.com/resources/papers/proceedings12/332-2012.pdf ... View more