There are many issues to consider when analyzing factorials (including repeated measures) using ranks. By definition, the ranks will have unequal variances, and an unstructured covariance matrix.  Full details, including SAS code, can be found in the book: Brunner, E., Domhof, S., Langer, F. 2002. Nonparametric Analysis of Longitudinal Data in Factorial Experiments. Wiley Publ. Without taking some precautions in the analysis, you will get grossly inflated type I errors. ... View more

You are on the right track with your approach. And in your context, the repeated statement is specifying within-subject variation. But even if you don't use a repeated statement, there is still within-subject variation (a residual variance component). ... View more

I want to follow up on my own message... My suggested approach will work best if the two predictors are about the same magnitude. If x1, for instance, consists of large numbers, and x2 consists of numbers near 0, then total (=x1+x2) will be dominated by x1 in the model fitting.  Also, interpretation of the results will be clearest if x1 and x2 are uncorrelated (which is not likely to be true). I am guessing that low correlation would not be too detrimental. ... View more

Here is an outline of one possible approach. I am assuming by 'weight', you mean parameter, and you want to have a model where the two parameters are equal. If the number of positive and negative events are roughly the same overall, you could just create total = positive_event+negative_event; in a data step, and then fit      model = total / s; Note that Y = a + b*total = a + b*(positive_event+negative_event) = a + b*positive_event + b*negative_event, so now you have a model with equal parameter values. (I am ignoring the random effects for now). The model in your message would be Y = a + b*positive_event + c*negative_event. If you fit the model with REML (the default), you cannot compare the log-likelihoods of your model and this simpler (reduced) one, because the fixed effects are eliminated from the likelihood. But if you fit both models with ML (method=ML on the procedure statement), you could compare the log-likelihoods with a chi-square test. Dealing with the random effect makes all of this a bit trickier. For the reduced model, you probably could use:      random intercept total / subject=id; If you took this approach, and used ML, the difference in the log-likelihoods (really difference in -2LL) would be based on the difference based on the fixed and random effects. There would be a total of 2 df in the difference of the log-likelihoods, one for the fixed effect and one for the random effect. You would not know automatically which of these two terms dominated the result. There may be another approach where you could focus just on the fixed effects. Be aware that ML can give somewhat biased parameter estimates, but if you have a large data set, the bias probably will be small.Also note that the use of random coefficient model could take care of the temporal autocorrelations. That is, you might not need the repeated statement in addition to the random statement. You should explore the differences in fit with and without the repeated statement. Or look carefully at the AR parameter value to see if it is close to 0; or compare AIC values with and without the repeated statement. ... View more

You question is vague, but I assume you want to restrict a parameter to be positive (or negative), or you want more complex restrictions involving multiple parameters. Unfortunately, there is no explicit way of restricting the parameters (nothing like the RESTRICT statement in REG). There are some advanced techniques where one can obtain restrictions by reformulating the model in terms of other created variables. But I don't know of any easy-to-use code for this. If you were more specific in your question, with an example, I could tell you more. ... View more

Agree, without seeing the code or the data, I really can't tell what is being plotted. ... View more

If you were using the LOGISTIC procedure in SAS, I would ask to see your code. But it looks like you are using JMP (???). In LOGISTIC, the default is to model the probability of "0" (or the lowest code), not the probability of "1". This causes confusion for many people, because everything could appear reversed. There are ways to reverse this in LOGISTIC (including using DESCENDING in the procedure statement). Don't know about JMP. Your first graph is likely a plot of the estimated probability (a continuous variable from 0-1) versus the predictor. ... View more

Not sure why you have this question. MCMC is a Bayesian procedure, and one does not expect to get the same results with Bayesian estimation as with MLE (or REML). With noninformative priors, you often get similar results, but not the same. You did not give the code for MCMC, so we cannot check if your program is correct.  For your information, there are multiple versions of the SAS User's Guide, so it does not help to give a page number. You should give a chapter name and section/sub-section heading (or example number from the chapter). ... View more

As Steve has indicated, you definitely want to use and give the results from LSMEANS. You could actually give the results on the log-link scale (with SE), and the inverse link scale (and SE). The analysis is modeling the log of the expected (mean) value as a function of treatment (and random effects). The inverse link is thus giving you estimates of the expected values (what you want). If you are reading SAS for Mixed Models, 2nd edition, you will see that this is clearly the most appropriate approach for showing results (assuming the model is appropriate). If the model is not appropriate, then the basis for your entire analysis is faulty. If you can justify the use of the negative binomial model (with the random effects) to determine if treatment has a significant effect, then it is only logical to use the full set of results from using the model. The arithmetic averages are not better estimates of the expected values (they can be far worse estimates). ... View more

I think the other issue you are getting at involves the logic of calculating the area under the entire ROC curve. Although there are some good statistical reasons to estimate this area under the entire curve (over all TPP and FPP), some authors argue against this approach. These latter authors argue for the partial area under the ROC curve (that is, the area is estimated over a narrower range of FPP, in the region where the diagnostic test is most likely to be used). A google search for "partial area under ROC curve" will give you several hits. I have not tried this with SAS -- I am guessing that one would have to write the code in IML. Pepe (an authority in this area) does have STATA code for the calculations. ... View more

Yes, the macro will not be available for several more months, or later. I suggest you look at a random subset of your data set with the procedure. ... View more