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From |
Hey Sky <heyskywalker@yahoo.com> |

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

Subject |
st: gllamm with numerical problems and the setup with discrete latent vars |

Date |
Thu, 6 May 2010 09:37:23 -0700 (PDT) |

hey, all just read the "ML numerical problems" and thought the problem I met I am modeling training behavior with discrete latent variable. I assume there is only one discrete latent variable, and the code is as following. it works well. gen cons=1 eq mu1: cons gllamm training_decision indep, i(id) base(5) link(mlogit) family(binom) ip(f) nrf(1) eq(mu1) the result of upper code for latent variable: Probabilities and locations of random effects ------------------------------------------------------------------------------ ***level 2 (id) loc1: -1.8365, .54899 var(1): 1.0082459 prob: 0.2301, 0.7699 my understanding to the loc1 is: the parameters for the discrete latent variable, type of person, for all people. that is: epsilon_i= alfa_0 + alfa_1*mu mu is type of person which I assume two types here. but how to model it if I assume people with different training decision have different probility to belong to type 1 or 2 person? I have tried the following code and hope I can get: epsilon_i= alfa_edu_0 + alfa_edu_1*mu epsilon_i= alfa_wrk_t0 + alfa_work_t1*mu epsilon_i= alfa_wrknt0 + alfa_wrknt1*mu the code: eq mu1: training eq mu2: work_with_training eq mu3: work_no_training gllamm training_decision indep, i(id) link(mlogit) family(binom) ip(f) nrf(3) eq(mu1 mu2 mu3) but the computer reports numerical problems. I think some place in the code is wrong, because of fortran have given result. any suggestions about it? I wish I make myself clear and thanks a lot for any reply in advance Nan from Montreal * * 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/

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