Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Jessica Bishop-Royse <jessibishoproyse@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Wed, 17 Mar 2010 11:11:38 -0400 |

Cause of death is a nominal variable, and combining causes is definitely on the list of things to do if I dont have enough observations. So I can, when modeling cause of death, put it in a model with only the county level factors to determine which county level vars are important, using mlogit? what if I want to use individual level variables like age and education? I have tried mlogit before doing this (combining individual and county level vars and I get a lot of noise). jcbr On 3/17/10, Partha Deb <partha.deb@hunter.cuny.edu> wrote: > Jessica, > > Is cause specific death just a multinomial (nominal) variable? If it > is, and assuming that each of the 9 causes has a reasonable fraction of > observations (else consider combining some of the causes), why not > estimate a multinomial logit? > > mlogit causeofdeath black, cluster(countynumber) > > or > > mlogit causeofdeath black i.countynumber > > HTH > > Partha > > > > Jessica Bishop-Royse wrote: >> you're right. i am actually using counties in Florida which are 67. >> Sorry for the confusion. >> >> On 3/17/10, Nick Cox <n.j.cox@durham.ac.uk> wrote: >> >>> 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/ >>> >>> >> >> >> > > -- > Partha Deb > Professor of Economics > Hunter College > ph: (212) 772-5435 > fax: (212) 772-5398 > http://urban.hunter.cuny.edu/~deb/ > > Emancipate yourselves from mental slavery > None but ourselves can free our minds. > - Bob Marley > > * > * 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/ > -- Jessi Bishop-Royse http://sites.google.com/site/wakullagirlssoccer/ http://sites.google.com/site/jessicacbishoproyse/Home "Without a struggle, there can be no progress." Fredrick Douglass * * 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/

**Follow-Ups**:**Re: st: is gllamm appropriate? is it necessary?-more information***From:*Partha Deb <partha.deb@hunter.cuny.edu>

**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>

**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:*Partha Deb <partha.deb@hunter.cuny.edu>

- Prev by Date:
**Re: st: is gllamm appropriate? is it necessary?-more information** - Next by Date:
**st: How do I run a 3-way repeated ANOVA?** - Previous by thread:
**Re: st: is gllamm appropriate? is it necessary?-more information** - Next by thread:
**Re: st: is gllamm appropriate? is it necessary?-more information** - Index(es):