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From |
angelrlaso@gmail.com |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: Re: Re: st: update seqlogit |

Date |
Thu, 18 Feb 2010 17:38:24 +0000 |

Dear Maarten, You were right: there were some empty cells in the table outcome by origin by sex (in the death row), so I've dropped this outcome. Now the categories are: 1 moved 2 absent 3 long absence 4 refusal 5 interviewed. In relation with the "multinomial" transition, I definitively do not want to merge the categories absent (what mainly means "at work") and long absence (mainly meaning "on vacation"). But if I follow your second proposal, I don't understand very well what I'm modelling. It seems that the transition 3 4: 5 6 (that now should be 2 3: 4 5) is just merging the two categories and the transition 3:4 (now 2:3) is the transition between being absent and being in long absence, which for me it is not a transition (you are either absent or in long absence, but both are deadways in comparison with being at home). One other thing: From bivariate results, I know that having moved is more frequent among males, until 3.4 decades of age and among non-Spaniards (what makes sense in a door-step survey). This is supported in a logistic analysis of having moved again the rest of the categories. (The codes for variables are: sexo 0 female 1 male _Igeograf2_1 being Latin-american vs being Spanish _Igeograf2_2 being Eastern European vs being Spanish _Igeograf2_3 coming from othe medium-low income countries vs being Spanish _Igeograf2_4 coming from high income countries vs being Spanish age in decades (edtd) is broken at 3.4 and 6.4) . xi: logistic moved edtd1 edtd2 edtd3 rentmil i.geograf2*sexo, cluster(psu) Logistic regression Number of obs = 11884 Wald chi2(13) = 1008,74 Prob > chi2 = 0,0000 Log pseudolikelihood = -4858,2263 Pseudo R2 = 0,1076 (Std. Err. adjusted for 1209 clusters in psu) Robust moved Odds Ratio Std. Err. z P>z ] sexo 1,106467 ,0653453 1,71 0,087 edtd1 1,486104 ,0944023 6,24 0,000 edtd2 ,5813945 ,0205634 -15,33 0,000 edtd3 1,720185 ,1036952 9,00 0,000 rentmil 1,004475 ,0053208 0,84 0,399 _Igeograf2_1 4,299125 ,5710822 10,98 0,000 _Igeograf2_2 7,14223 1,115184 12,59 0,000 _Igeograf2_3 4,381366 1,041568 6,21 0,000 _Igeograf2_4 2,632966 ,6537054 3,90 0,000 _IgeoXsexo_1 1,016513 ,1917234 0,09 0,931 _IgeoXsexo_2 ,9295757 ,2048988 -0,33 0,740 _IgeoXsexo_3 1,251563 ,3775649 0,74 0,457 _IgeoXsexo_4 2,251334 ,7256011 2,52 0,012 Seqlogit coefficients are congruent with this only for the sex variable, but not for age nor origin . xi: seqlogit incseqd sexo edtd1 edtd2 edtd3 rentmil i.geograf2, tree(1: 2 3 4 5 , 2 3: 4 5 , 2 :3 , 4 : 5) /// > ofinterest (sexo) over(i.geograf2 edtd1 edtd2 edtd3) or cluster(psu) Robust incseqd Odds Ratio Std. Err. z P>z ] _2_3_4_5v1 sexo 1,823968 ,6438496 1,70 0,089 edtd1 ,762823 ,0684919 -3,02 0,003 edtd2 1,748463 ,0904007 10,81 0,000 edtd3 ,5161561 ,0385954 -8,84 0,000 rentmil ,9955007 ,0052792 -0,85 0,395 _Igeograf2_1 ,2299126 ,0310905 -10,87 0,000 _Igeograf2_2 ,1397021 ,022119 -12,43 0,000 _Igeograf2_3 ,2258214 ,053916 -6,23 0,000 _Igeograf2_4 ,3729059 ,0934941 -3,93 0,000 _sexo_X__I~1 1,003078 ,1932901 0,02 0,987 _sexo_X__I~2 1,085557 ,2416302 0,37 0,712 _sexo_X__I~3 ,8142282 ,2481748 -0,67 0,500 _sexo_X__I~4 ,4677205 ,1517807 -2,34 0,019 _sexo_X_ed~1 ,7895073 ,0960845 -1,94 0,052 _sexo_X_ed~2 ,9480818 ,0663292 -0,76 0,446 _sexo_X_ed~3 1,479669 ,1970169 2,94 0,003 Have I specified something wrong in seqlogit? Many thanks, Angel Rodriguez-Laso El 17/02/2010 16:22, Maarten buis <maartenbuis@yahoo.co.uk> escribiÃ³: > --- On Wed, 17/2/10, angelrlaso@gmail.com wrote: > > I'm having trouble with the definition of one of the > > transitions. > > > > The dependent variable (incseq) codes are: 1 Dead 2 Has > > moved 3 Absent 4 Long absence 5 Refusal 6 Interviewed. > > > > My transitions are: > > > > 1: Dead: rest of the possibilites > > 2: Has moved: absent long-absence refusal interviewed > > 3: Absent or long absence: refusal interviewed > > 4: Refusal: Interviewed > You either need a fifth transition "absent : long absence" > or you need to combine the absent and long absence > categories. So in terms of Stata: > For the first solution use the option > tree(1: 2 3 4 5 6, 2: 3 4 5 6, 3 4 : 5 6, 3 : 4, 5 : 6) > For the second solution first type: > gen byte incseq2 = incseq > recode incseq2 (4 = 3) > than use -seqlogit- with incseq2 as dependent variable > and the following option: > tree(1: 2 3 5 6, 2: 3 5 6, 3 : 5 6, 5 : 6) > > I've tried then: > > > > xi: seqlogit > > incseq sexo edtd1 edtd2 edtd3 rentmil i.geograf2, tree(1:2 3 > > 4 5 6, 2:3 4 5 6, 3: 5 6, 4: 5 6, 5:6) /// > > ofinterest > > (sexo) over(i.geograf2 edtd1 edtd2 edtd3) or > > cluster(psu) > > > > Stata is now in > > > > Iteration 72: log pseudolikelihood = -16765,835 (not > > concave) > This shouldn't happen, the starting values are exactly correct, > so it should converge in the first itteration. There is probably > some form of perfect colinearity or perfect prediction going on. > That can easily happen with -xi-. Do for instance all transitions > happen in all categories of geograf2? > Hope this helps, > Maarten > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > http://www.maartenbuis.nl > -------------------------- * * 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: Re: Re: st: update seqlogit***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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