Message/Author 

Jon Heron posted on Monday, November 20, 2006  5:23 am



Hi, I use tech8 when performing multiple random starts to keep track of how long the program has left to run. The problem is that this can create rather large textfiles (my record is 0.5 Gig  my poor PCwas not impressed!) Is there another way I can monitor progress without upsetting my PC? cheers Jon 

Jon Heron posted on Monday, November 20, 2006  5:52 am



On a related note, I can no longer turn tech8 off! 


We also noted the huge text files with TECH8, so now you always get TECH8 to the screen but you only get TECH8 to the output file if you request TECH8 in the OUTPUT command. We felt users would always want to monitor their progress on screen even when they did not want the technical details in the output file. How do you feel about this? 

Jon Heron posted on Monday, November 20, 2006  7:25 am



I think that's a perfect solution! 

Jon Heron posted on Monday, November 27, 2006  3:49 am



... unless you're running a BLRT I use the initial bit of Tech14 output to establish whether I've located the optimal n/n1 class models + then I stop and try a new OPTSEED if this is not the case. If I can't turn off tech8 then the output whizzes past so quickly that I can't make this judgement. 


Our most recent suggestion regarding TECH14 which is under TECH14 in the user's guide on the website is to first find a replicated solution without using TECH14. Then use OPTSEED and TECH14 in conjunction with the LRTSTARTS option. 

Jon Heron posted on Tuesday, November 28, 2006  12:44 am



Hi Linda, I find optimal/replicated solutions for n and n1 class models without using Tech14 and then bring in tech14 to carry out the BLRT. Unfortunately, not all optseeds that replicate the nclass model will recreate the optimal model for *both* n and n1 classes. One way to ensure that it does, appears to be to make sure the classes for the nclass model are ordered in increasing size, however this is not always possible (in my example at least). A quicker alternative I have found is to ensure that the H0LL and H1LL values that are quoted at the start of a tech14 run correspond to the likelihoods for the replicated n and n1 class models. I then know that the BLRT is going to be comparing the two models I want it to. I am now experimenting with different LRTSTARTS/LRTBOOTSTRAP options to see if I can get it to stop after quoting these initial H0LL/H1LL. 


It is not necessary to have an OPTSEED for the k1 class model. It is only necessary to have it for the k class model. The checking of the loglikelihoods is done automatically. 

Jon Heron posted on Wednesday, November 29, 2006  12:07 am



I don't have an OPTSEED for the k1 class model. I use the OPTSEED for the k class model and find that it often wont recreate the optimal k1 class model when it comes to the BLRT. I wonder if this is something to do with the settings we've been using: lrtstarts 0 0 150 15; lrtbootstrap 100; 


The OPTSEED option is only for the k class model. I think you should modify the LRTSTARTS option. The first two numbers are for the k1 class analysis. The last two are for the k class analysis. I would try LRTSTARTS = 2 1 150 15; If that does not work, I would increase the last two numbers. You may just have a difficult model. Some are tougher than others. 

Jon Heron posted on Thursday, November 30, 2006  5:29 am



Hi Linda, we may be going round in circles due to my lack of understanding, so thankyou for your patience. I have found that the k1 class model referred to with 'H0 Loglikelihood Value ' in the BLRT output is strongly dependent on the ordering of classes for the k class model (and hence on the OPTSEED which generates the kclass model). The restriction that the largest class is last (as described in the manual) does not seem sufficient for my model  I have found that the only way to be certain of obtaining the correct k1 class model is to ensure monotonically increasing class sizes within the k class model. Hence I have come up with a way of running a quick BLRT to ensure that the correct models are being referred to, and then running a longer BLRT to estimate the pvalue. This is quicker than attempting (and often failing) to have the kclasses in increasing order of size. 


In our experience, ordering the classes is not necessary. If you would like, you can send your input, data, output, and license number to support@statmodel.com and we can see why you need to do this. 

Matt Moehr posted on Tuesday, March 06, 2007  8:43 am



Linda, I was wondering if/how this thread was resolved because I have a related question. In my case, I used the strategy of choosing a model based on BIC and then confirming with BLRT (tech14). I think the correct solution is somewhere between 3 and 5 classes. The BIC for the four models M5, M4, M3, M2 in the same order: 4831, 4784, 4784, 4809. All of the models seemed to have stable class counts and were well replicated with starts=100 25. When I started using BLRT, the results seem much less clear cut. I think the root of the problem is that the log likeliehood reported in the tech14 section of the output is not the same as the LL I got when I was using the BIC criteria. For example when running a model with 5 classes, the H0 model is a 4class solution and tech14 shows: H0 Loglikelihood Value 2139.25 But when I separately estimated the 4class model, the replicated LL was 2128.5. So two questions: 1) Is it a problem that H0LL != LL(n1) ? 2) Can I compute the LR test statistic based on the replicated LL values (kind of like a naive chisquare), and then compare that LR to the distribution of bootstrap draws? 

linda beck posted on Thursday, August 07, 2008  9:26 am



Using the OPTSEED from am model without Tech14 is intended (sometimes) for not doing the kclass analysis again, when using tech14. But this kclass solution derived from the optseed should be the same as doing the kclass analysis again, am I right!? I ask this, because my kclass model is already very complex and time consuming. I directly want to compute BLRT without computing the stable kclass solution again, so optseed is the choice? 


Yes, The OPTSEED run will give exactly the same solution (check the log likelihood) as the kclass analysis from which you got the OPTSEED value. 


Is ordering of classes necessary when using tech14 and tech11? From your experience, any news since 2006? Unfortunately, I have solutions where the last class is extracted first, but I get the impression, that BLRT and LRT are not influenced by that. 


No ordering needed. 

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