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From |
"Kieran McCaul" <Kieran.McCaul@uwa.edu.au> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: is gllamm appropriate? is it necessary?-more information |

Date |
Thu, 18 Mar 2010 03:35:18 +0800 |

... A couple of questions: 1. When you say you have %minority, %poverty, etc, for each county, does that mean that you have these percentages for each combination of these factors? In other words, do you have the percentages who are ruralpoorminority, ruralpoorwhite, urbanpoorminority, urbanpoorwhite, etc? 2. If the answer is yes to the above, do you have these percentages by age and sex in each county? -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox Sent: Wednesday, 17 March 2010 10:53 PM To: statalist@hsphsun2.harvard.edu Subject: RE: st: is gllamm appropriate? is it necessary?-more information For extra context I guess wildly at what is not explicit here. Jessica is using the counties of the US, which are about 3000 in number. Please remember that this is an international list and that others may not share your presumptions. Nick n.j.cox@durham.ac.uk Jessica Bishop-Royse Some more information about the project I am working on: 1. I have two main research questions here. One: What county-level variables are associated with cause-specific death in 1980? In 2000? And Two: What are the net effects of individual characteristics and county level variables? What are the changes from 1980 to 2000? 2. I don't see county as a control variable- but rather as a cluster variable. In fact, I am not even really interested in county per but rather the variables that I have for counties (like % minority, % poverty, etc.) Eventually I would like to make interaction effects with county (ruralpoorminority counties versus ruralpoorwhite counties, urbanpoorwhite counties, etc.) Ideally, I would like to add these variables to a model along with my individual level predictors. 3. As of now, my cause of death variable is 10 categories, 9 causes and survival. I would like the ability to model both ways (cause 1 versus all others and each cause versus survival). Yesterday at about 2 pm, I set following command up to run. It ran all afternoon, and all night and still hadn't finished. It went through 80 iterations, most of which had the note "not concave" before I finally canceled it this morning. I am sure that I am doing something wrong and it makes me nervous because I haven't even added all the predictors yet. . gllamm causeofdeath black, i(countynumber) Iteration 0: log likelihood = -226514.74 (not concave) Iteration 1: log likelihood = -170460.09 (not concave) Iteration 2: log likelihood = -156165.69 Iteration 3: log likelihood = -155236.77 (not concave) Iteration 4: log likelihood = -154882.46 Iteration 5: log likelihood = -154869.14 Iteration 6: log likelihood = -154848.88 (not concave) Iteration 7: log likelihood = -154814.85 Iteration 8: log likelihood = -154814.29 Iteration 9: log likelihood = -154814.23 Iteration 10: log likelihood = -154814.23 number of level 1 units = 318493 number of level 2 units = 77 Condition Number = 2.4744641 gllamm model log likelihood = -154814.23 ------------------------------------------------------------------------ ------ causeofdeath | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------- ------ black | .024678 .0016439 15.01 0.000 .0214559 .0279 _cons | .024141 .001246 19.37 0.000 .0216988 .0265831 ------------------------------------------------------------------------ ------ Variance at level 1 ------------------------------------------------------------------------ ------ .1547741 (.00038787) Variances and covariances of random effects ------------------------------------------------------------------------ ------ ***level 2 (countynumber) var(1): .00002072 (8.730e-06) ------------------------------------------------------------------------ ------ . gllamm causeofdeath black biryear, i(countynumber) Iteration 0: log likelihood = -226590.33 (not concave) Iteration 1: log likelihood = -170468.78 (not concave) Iteration 2: log likelihood = -154843.19 (not concave) Iteration 3: log likelihood = -154785.59 (not concave) Iteration 4: log likelihood = -154772.62 (not concave) Iteration 5: log likelihood = -154753.11 Iteration 6: log likelihood = -154738.19 Iteration 7: log likelihood = -154738.07 (not concave) Iteration 8: log likelihood = -154738.07 (not concave) Iteration 9: log likelihood = -154738.07 (not concave) Iteration 10: log likelihood = -154738.07 (not concave) Iteration 11: log likelihood = -154738.07 (not concave) Iteration 12: log likelihood = -154738.07 (not concave) Iteration 13: log likelihood = -154738.07 (not concave) Iteration 14: log likelihood = -154738.07 (not concave) Iteration 15: log likelihood = -154738.07 (not concave) Iteration 16: log likelihood = -154738.07 (not concave) Iteration 17: log likelihood = -154738.07 (not concave) Iteration 18: log likelihood = -154738.07 (not concave) Iteration 19: log likelihood = -154738.07 (not concave) Iteration 20: log likelihood = -154738.07 (not concave) Iteration 21: log likelihood = -154738.07 (not concave) Iteration 22: log likelihood = -154738.07 (not concave) Iteration 23: log likelihood = -154738.07 (not concave) Iteration 24: log likelihood = -154738.07 (not concave) Iteration 25: log likelihood = -154738.07 (not concave) Iteration 26: log likelihood = -154738.07 (not concave) Iteration 27: log likelihood = -154738.07 (not concave) Iteration 28: log likelihood = -154738.07 (not concave) Iteration 29: log likelihood = -154738.07 (not concave) Iteration 30: log likelihood = -154738.07 (not concave) Iteration 31: log likelihood = -154738.07 (not concave) Iteration 32: log likelihood = -154738.07 (not concave) Iteration 33: log likelihood = -154738.07 (not concave) Iteration 34: log likelihood = -154738.07 (not concave) Iteration 35: log likelihood = -154738.07 (not concave) Iteration 36: log likelihood = -154738.07 (not concave) Iteration 37: log likelihood = -154738.07 (not concave) Iteration 38: log likelihood = -154738.07 (not concave) Iteration 39: log likelihood = -154738.07 (not concave) Iteration 40: log likelihood = -154738.07 (not concave) Iteration 41: log likelihood = -154738.07 (not concave) Iteration 42: log likelihood = -154738.07 (not concave) Iteration 43: log likelihood = -154738.07 (not concave) Iteration 44: log likelihood = -154738.07 (not concave) Iteration 45: log likelihood = -154738.07 (not concave) Iteration 46: log likelihood = -154738.07 (not concave) Iteration 47: log likelihood = -154738.07 (not concave) Iteration 48: log likelihood = -154738.07 (not concave) Iteration 49: log likelihood = -154738.07 (not concave) Iteration 50: log likelihood = -154738.07 (not concave) Iteration 51: log likelihood = -154738.07 (not concave) Iteration 52: log likelihood = -154738.07 (not concave) Iteration 53: log likelihood = -154738.07 (not concave) Iteration 54: log likelihood = -154738.07 (not concave) Iteration 55: log likelihood = -154738.07 (not concave) Iteration 56: log likelihood = -154738.07 (not concave) Iteration 57: log likelihood = -154738.07 (not concave) Iteration 58: log likelihood = -154738.07 (not concave) Iteration 59: log likelihood = -154738.07 (not concave) Iteration 60: log likelihood = -154738.07 (not concave) Iteration 61: log likelihood = -154738.07 (not concave) Iteration 62: log likelihood = -154738.07 (not concave) Iteration 63: log likelihood = -154738.07 (not concave) Iteration 64: log likelihood = -154738.07 (not concave) Iteration 65: log likelihood = -154738.07 (not concave) Iteration 66: log likelihood = -154738.07 (not concave) Iteration 67: log likelihood = -154738.07 (not concave) Iteration 68: log likelihood = -154738.07 (not concave) Iteration 69: log likelihood = -154738.07 (not concave) Iteration 70: log likelihood = -154738.07 (not concave) Iteration 71: log likelihood = -154738.07 (not concave) Iteration 72: log likelihood = -154738.07 (not concave) Iteration 73: log likelihood = -154738.07 (not concave) Iteration 74: log likelihood = -154738.07 (not concave) Iteration 75: log likelihood = -154738.07 (not concave) Iteration 76: log likelihood = -154738.07 (not concave) Iteration 77: log likelihood = -154738.07 (not concave) Iteration 78: log likelihood = -154738.07 (not concave) Iteration 79: log likelihood = -154738.07 (not concave) Iteration 80: log likelihood = -154738.07 (not concave) (Maximization aborted) . end of do-file . What do you think? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: is gllamm appropriate? is it necessary?-more information***From:*Jessica Bishop-Royse <jessibishoproyse@gmail.com>

**RE: st: is gllamm appropriate? is it necessary?-more information***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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