Hi, Longleaf       I am the correspondence author of %HPGLIMMIX SAS macro, thank you for your interests in it.       This macro and the associated academic paper are just published by Journal of Statistical Software at this URL: Journal of Statistical Software        You can find everything in that link. Enjoy!       This macro project was initiated in 2011 and it took almost 3 years to perfect it, write a paper and publish them to a peer-reviewed academic journal. Liang ... View more

There are several types of Tobit models QLIM can estimate, but LIFEREG can only estimate Type 1, which is the standard Tobit model. ... View more

In a Mixed model, the random effects are SAMPLED from a potentially large population and may not be replicated. In the general case, you can only do marginal prediction, integrating out random effects, if the levels of random subjects in scoring data set is different from what you used in modeling. What you can do is conducting a monte carlo based approach. On the other hand if the levels in the scoring data set ARE REPLICATES of those in training data, then what you can do is simply transpose the random effects solution dataset and combined with the fixed effect data set to be the scoring coefficient data set, then apply the design matrices with the scoring coefficient data set in PROC SCORE, just like how you would score in GLM. ... View more

change the variable name CNT in data one appropriately, and use PROC SURVEYSELECT with strata check the manual of SURVEYSELECT ... View more

you can try the P-spline smoothing using MIXED & GLIMMIX > Thanks topkatz for your very detailed reply. It's > very much appreciated. > > With bands I mean bins. > > I wasn't clear enough. I wanted to find a binning > method that could find disjoint distributions across > the sample. It should be possible for empirical data > with different peaks. Is it called mixture > distribution? Is there an analytical approach to > this? > > "You want a small number of bins with low variation? > Does this mean you want the sums of squares around > the means within each bin to be small?" > > Correct. Then it becomes an optimization problem > since I still want a small number of bins. Integer > programming may be the tool I'm looking for to > accomplish this? > > I am going to use the variable for segmentation and > I'm curious whether it is possible to use an > univariate approach, "to let the data speak for > itself", instead of using a binning technique that > compares to the state of some other variable. > > d points there "why to bin". > > /dante ... View more

you can find some popular Data Mining algorithms in STAT, such as KNN, LDA, CDA, various regression models, (regularized) Logistic, LASSO (v9.2), disjoint partition clustering and hierarchical clustering, SVD&PCA, Bagging, etc. With some tweak, you can also implement algorithms such as AdaBoost, LSI & vector space model in TextMining, the efficiency is not on par with EM, though. consult my blog for more: http://www.sas-programming.com You get extra that is not available in STAT, such as Neural Network, CART, etc ... View more

could u detail the clustering algorithm and process you used in your analysis? plain k-mean clustering on raw/simply stdized data? > By no means am I an expert on the matter. I haven't > used PROC Similarity, so I can't comment about the > proc. > > A friend of mine told me once he was clustering a > number of series according to their measurements > across time. That didn't sit right with me, so I > created some data to see how it would work. > > I started by creating several series of data with no > pattern across time, but with different averages. > Even with telling the software to standardize the > data, the series that had similar means were > clustered together. > > I then created data with patterns across time, with > some series having the same pattern. And those > series that had different patterns across time were > given similar means. The clustering grouped the > series that had similar means, even though the > patterns across time were different. Also, the > series that had similar patterns were not grouped, > because the means were different. > > I then took that same data and made all the means > equal. When I did the clustering, the groups with > similar patterns were put together. > > So, to make along story short, how do you quantify a > pattern across time, so you can cluster series that > are similar. That's a hard question. The patterns > have to be more obvious than differences in series > averages, or other things you don't want to cluster > on. > > I'm sure there's more to this, but I'm not educated > enough on the subject to comment futher. > > Message was edited by: jonathan@jmp ... View more

check my SCSUG 2009 paper SVD Filtered Temporal Usage Pattern Analysis and Clustering the main idea is SVD on a toeplitz matrix like the SSA technique used in Meteorology you can obtain a copy either at SCSUG.org or at my blog http://www.sas-programming.com/2009/09/svd-filtered-temporal-usage-pattern.html > Hello Folks, > > Have any one tried using Proc Similarity or any other > technique for clustering similar time series > patterns. > > Thanks, > Karteek Message was edited by: oloolo ... View more