About maximalc

maximalc · ‎01-21-2018

I have clustered data for which I'd like to derive a marginal model for a specific quantile. I know how to perform quantile regression in SAS, and I've seen how GLIMMIX can be used to derive a marginal model for the dependent mean. I have thus far been unable to find a method for deriving a marginal model at a specific quantile. Is there currently such a procedure in SAS, or perhaps alternatives that someone could recommend? Thank you.

maximalc · ‎08-09-2016

Just to update, I tried running the same process using DETAILS=ALL and then again with DETAILS=STEPS (with all options enabled), and the only additional information provided were the penalized and unpenalized parameter estimates at each step, along with a lamba range. I was unable to get it to produce a list of alternative candidate effects at each step.

maximalc · ‎08-08-2016

@Rick_SAS wrote: I'm afraid I can't be more specific because I honestly don't know what is happening with your data. You haven't provided any details. What quantiles are you using? How many effects? Are there interaction or higher-order effects? What selection method are you using? Only by looking at your data and the details of the methods can you definitively discover what is happening. Here's a snippet of code to distill the essence of what I am attempting to model: PROC QUANTSELECT data=WORK.DATA seed=1; model y = x1 x2 x3 x4 x5 x6 x7 x8 x9 x10/ quantile = .50 selection = LASSO(select=AICC stop=AICC sh=5 choose=SBC ADAPTIVE) details = SUMMARY; run; So basically, it's median regression with adaptive lasso. The actual number of effects is greater, but I think I can convey the same situation without enumerating the full list. If I run the script as is, the following effects are selected: x1, x3, x4, x7, x8. If I introduce additional effects x11-x19 and run the script, the following effects are selected: x1, x3, x7, x2, x5, x6, x9, x10. None of effects x11-x19 appear in the selection summary output at any step of the selection process, however I will try your suggestion of expanding the output details and report back.

maximalc · ‎08-08-2016

Hi Rick, Your example certainly does appear to parallel my situation, and I agree that correlation is likely a significant contributor to what I am observing. In looking at the DETAILS= option, I see that one of the specifications allows you to see "entry and removal statistics for the top five candidates for inclusion or exclusion at each step". Does this imply that the new variables I introduced were significant enough to make it into the top five candidates somewhere along the way, just not enough to actually be selected, and that this altered the path of the selection process? While that would shed some light, I'm still not sure I understand the situation completely, as I'd still think that the "best" candidate at a particular step would remain the same regardless of whatever comparable (but inferior) effects made it into the top five list. To extend this to your mathematical example, my question would be this: why would the alternate model include X3 instead of X2 if X4-X10 never made it into the model anywhere along the way? I understand the divergent models you specified are equivalent in terms of explanatory power, but I'm still having trouble grasping the mechanics of how the selection path diverged if the model selection summary output in SAS doesn't show X4, X5, or any of the other illusory effects at any of the steps prior to the selection of X3 in the model. Thank you for your patience .

maximalc · ‎08-08-2016

I've been using PROC QUANTSELECT to perform variable selection. After using the process on an initial set of variables and arriving at a proposed model, I decided to add a handful of additional variables into the mix and run QUANTSELECT again (the only change that was made was the addition of 9 extra potential explanatory variables). This second run resulted in a different model, however none of the new variables appeared in the new model. At first I assumed that at least one of these new variables must have entered the model at some point in the selection process and then exited the model later down the pipeline, however it turns out that NONE of the new variables entered the new model at any point in the variable selection process. The new model kept three out of the five variables from the initial model, dropped two from the initial model, and added five more that weren't chosen by the original model but were in the original set of potential effects. The second model ended up being slightly better than the first, but for the life of me I can't figure out why it's not identical to the first model considering none of the new variables ever entered the model. How is it possible that the unselected variables altered the result of PROC QUANTSELECT without ever appearing in the selection process? Or is the outcome of PROC QUANTSELECT somewhat stochastic by nature, and perhaps it would have generated an alternate model even if I hadn't entered any additional potential effects? If anyone could shed some light on the situation, I would be much obliged.

maximalc · ‎06-09-2016

Well Rick, you nailed it. Your simple modification produced HPD intervals that were improved by a factor of 10 on account of the weighting. Additionally, the final betas were 99.9% similar to those achieved via weighted QUANTREG. Both algorithms produced the same R-square, F-statistic, and RMSE, so I think it is safe to say they are converging on the same solution . Thanks for your help!

maximalc · ‎06-09-2016

Thanks for your response, Rick. Regarding your first point, part of my interest in achieving quantile regression via MCMC is to compare its efficacy to QUANTREG for my particular data set. I also like the idea of knowing the distribution of betas that come with resampling in this manner. And, somewhat to my surprise, the MCMC procedure solves the same problem more rapidly than QUANTREG (about twice as quickly if I recall correctly), so there is the time factor as well. Regarding your question about the log-likelihood function, it's taken from an example in the SAS knowledge base ( https://support.sas.com/rnd/app/examples/stat/BayesQuantile/quantile.htm ). It specifies an asymmetric laplace distribution for the dependent variable for the purpose of quantile regression. My knowledge of different types of distributions is very limited, so I don't claim to know the theoretical basis for this choice of function, however I can say that the betas it generates are very similar to those resultant from the unweighted QUANTREG procedure for the same problem, so I do believe it to be applicable. If implementing weighting via MCMC is as simple as muliplying the error term by the weight factor as you've described, then that would be splendid. I will give it a shot and see how the result compares to weighted QUANTREG. Thanks again.

maximalc · ‎06-08-2016

Hello, I have a data set for which the reliability of certain measurements is known to be superior to others. I would like to perform quantile regression using PROC MCMC (which I was able to figure out using the SAS documentation), however I would like to weight each datum according to a weight value included in the data set. Is what I am attempting possible, and if so, how would one go about achieving it? Please let me know if additional information is required in order to provide an appropriate solution. Feel free to modify the following hypothetical example (weight can be represented by W) for this purpose: proc mcmc data=igg seed=5263 propcov=congra ntu=1000 mintune=10 nmc=30000; begincnst; p=0.95; endcnst; parms (b0-b2) 0; prior b: ~ general(0); mu= b0 + b1*age + b2*age2; u = igg - mu; ll = log(p)+log(1-p) - 0.5*(abs(u)+(2*p-1)*u); model igg ~ general(ll); run; Thanks in advance. *I am using the latest build of SAS v9.4

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How to find marginal model for quantile regression?

Re: Unselected variables altering QUANTSELECT results

Re: Unselected variables altering QUANTSELECT results

Re: Unselected variables altering QUANTSELECT results

Unselected variables altering QUANTSELECT results

Re: Weighted regression with PROC MCMC

Re: Weighted regression with PROC MCMC

Weighted regression with PROC MCMC

Re: Weighted regression with PROC MCMC

Re: Weighted regression with PROC MCMC

How to find marginal model for quantile regression?

Re: Unselected variables altering QUANTSELECT results

Re: Unselected variables altering QUANTSELECT results

Re: Unselected variables altering QUANTSELECT results

Unselected variables altering QUANTSELECT results

Re: Weighted regression with PROC MCMC

Re: Weighted regression with PROC MCMC

Weighted regression with PROC MCMC