Re: st: gologit2 interaction

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 06:57 PM 9/1/2013, lan zhang wrote:
> means significant or not?

I don't understand the question. Did part of it get cut off? 
Sometimes Statalist kills the first line or two of messages. If you 
can clarify I will try to answer.

> and for the interaction in gologit2
> A*i.B is not accepted,
> then I can just use A*B to replace? will STATA recognize this is a 
> interaction??

Unfortunately gologit2 is an older program and does not accept factor 
variable notation. You will have to compute the interactions 
yourself. So, for example, if you want the interaction of x1 and x2 
in the model, then do something like

gen x1Xx2 = x1 * x2

HTH. Rich



> These two questions are kind of stupid, but really confuse me.
> and thanks for your help.
>
> Lan
> On Sep 1, 2013, at 6:42 PM, lan zhang <cat1984@126.com> wrote:
>
> >
> > On Sep 1, 2013, at 7:00 PM, Richard Williams 
> <richardwilliams.ndu@gmail.com> wrote:
> >
> >> At 04:10 PM 9/1/2013, William Buchanan wrote:
> >>> Although I thank you for following the protocol of showing your 
> input and output, you still have yet to acknowledge where you 
> downloaded the package from.  In general I think many folks here 
> would tell you to start off with a simpler model.  Is the 
> coefficient for the interaction term of particular interest or are 
> you trying to increase the model fit by including additional 
> parameters?  How is your outcome distributed?  No one other than 
> you can tell you whether or not you can use that in your study.
> >>
> >> I agree with William's concerns and will add several others. 
> First off though, I will note that gologit2 can be downloaded from 
> SSC and that the support page and troubleshooting FAQs are at
> >>
> >> http://www3.nd.edu/~rwilliam/gologit2/index.html
> >>
> >> http://www3.nd.edu/~rwilliam/gologit2/tsfaq.html
> >>
> >> Now, the concerns I have are
> >>
> >> * Why are you using gologit2 in the first place? Have you tested 
> whether or not the assumptions of the simpler ologit model are 
> violated? Read up on gologit2 at the above links to learn more 
> about it if you haven't already. Don't go to a more complicated and 
> harder to understand approach until you have at least checked out 
> whether something simpler will do.
> >>
> >> * Given that you are using gologit2, why aren't you using 
> options like autofit or pl? A totally unconstrained gologit model 
> isn't worth that much -- if you can't make the model more 
> parsimonious you might as well use mlogit, which is more widely 
> used and understood.
> >>
> >> * You only have 230 cases and yet you are estimating around 50 
> parameters. That is probably way too many for any method that uses 
> maximum likelihood (informal rule of thumb is at least 10 cases per 
> parameter). Like William says, the model probably needs to be 
> simplified, by dropping variables and/or by imposing 
> proportionality constraints.
> >>
> >> * You say "variable like z2targetcrossus is not significant in 
> the raw model." How did you test that? There are actually 3 
> different parameters for each variable. You either need to do a 
> chi-square contrast between models (i.e. estimate a model that 
> doesn't have the variable and then another model that does have it) 
> or else do a wald test, e.g. after your first gologit2 command add 
> a command like
> >>
> >> test z2targetcrossus
> >>
> >> That will provide a joint test for all three of the parameters 
> that are estimated for that variable.
> >>
> >> * Finally, I think you should read up a bit on interactions. You 
> say "variable like z2targetcrossus is not significant in the raw 
> model, but the interaction z2targetrd=z2targetcrossus*rd is 
> significant. How could this happen?" First off, we don't actually 
> know that the z2 var has insignificant effects; you also have the 
> r2 variable as part of the interaction; and in any event there is 
> no reason that things like this can't happen. For example, a 
> variable could have a positive effect on one group and a negative 
> effect on the other. The main effect alone might come out as zero 
> or thereabouts but once you split the effects out for each group 
> the sign and significance of the effects could become clear. A 
> possible starting place for more reading on interactions (I am not 
> saying it is the best, but it is one I can find in 10 seconds) is
> >>
> >> http://www3.nd.edu/~rwilliam/stats2/l53.pdf
> >>
> >> Hope this helps.
> >>
> >>> HTH,
> >>> Billy
> >>>
> >>> Sent from my iPhone
> >>>
> >>> On Sep 1, 2013, at 14:59, lan zhang <cat1984@126.com> wrote:
> >>>
> >>>> the raw model:
> >>>> gologit2 ma2 z2targetcrossus z2usinl z2chcross z2chdo woe jv 
> rd network repoffice z2age unique z2location z2chsenior z2reve
> >>>>> nue z2mconcentration z2var65
> >>>>
> >>>> Generalized Ordered Logit Estimates               Number of 
> obs   =        230
> >>>>                LR chi2(48)     =     131.44
> >>>>                 Prob > chi2     =     0.0000
> >>>> Log likelihood = 
> -190.91867                               Pseudo R2       =     0.2561
> >>>>
> >>>> 
> ----------------------------------------------------------------------------------
> >>>>            ma2 |      Coef.   Std. 
> Err.      z    P>|z|     [95% Conf. Interval]
> >>>> 
> -----------------+----------------------------------------------------------------
> >>>> 0                |
> >>>> z2targetcrossus 
> |  -.0173887   .1668386    -0.10   0.917    -.3443863    .3096089
> >>>>        z2usinl 
> |  -.2492047   .1905918    -1.31   0.191    -.6227578    .1243484
> >>>>      z2chcross 
> |    .103573   .1520177     0.68   0.496    -.1943763    .4015223
> >>>>         z2chdo 
> |  -.0393117   .1750591    -0.22   0.822    -.3824213    .3037979
> >>>>            woe 
> |  -.4751755   .6626994    -0.72   0.473    -1.774043    .8236915
> >>>>             jv 
> |  -1.083191   .7963511    -1.36   0.174    -2.644011    .4776286
> >>>>             rd 
> |   .9039168   .4271417     2.12   0.034     .0667344    1.741099
> >>>>        network 
> |   1.410567   .9212801     1.53   0.126    -.3951092    3.216242
> >>>>      repoffice 
> |  -1.001058   .4313348    -2.32   0.020    -1.846458   -.1556568
> >>>>          z2age 
> |   .1168134   .1774538     0.66   0.510    -.2309898    .4646165
> >>>>         unique 
> |   .6194533   .3772757     1.64   0.101    -.1199934      1.3589
> >>>>     z2location 
> |   .1114399   .1717018     0.65   0.516    -.2250895    .4479693
> >>>>     z2chsenior 
> |   .0730508   .1674231     0.44   0.663    -.2550924     .401194
> >>>>      z2revenue 
> |   -.014403    .165149    -0.09   0.931    -.3380891    .3092831
> >>>> z2mconcentration 
> |  -.2459377   .1718248    -1.43   0.152    -.5827081    .0908327
> >>>>        z2var65 
> |   .2098376   .1512144     1.39   0.165    -.0865371    .5062123
> >>>>          _cons 
> |  -.4365704   .6758439    -0.65   0.518      -1.7612    .8880593
> >>>> 
> -----------------+----------------------------------------------------------------
> >>>> 1                |
> >>>> z2targetcrossus 
> |   .5896636   .3993497     1.48   0.140    -.1930474    1.372375
> >>>>        z2usinl 
> |  -5.757507   3.010157    -1.91   0.056    -11.65731    .1422915
> >>>>      z2chcross 
> |   .3443698   .8501671     0.41   0.685    -1.321927    2.010667
> >>>>         z2chdo 
> |   .0274867   .3531697     0.08   0.938    -.6647132    .7196866
> >>>>            woe 
> |  -.1374271   .8539173    -0.16   0.872    -1.811074     1.53622
> >>>>             jv 
> |  -1.625354   1.073093    -1.51   0.130    -3.728578      .47787
> >>>>             rd 
> |   .2289301   .5789769     0.40   0.693    -.9058436    1.363704
> >>>>        network 
> |   .9461952   1.146079     0.83   0.409    -1.300079    3.192469
> >>>>      repoffice 
> |   .1904672   .6567114     0.29   0.772    -1.096663    1.477598
> >>>>          z2age 
> |   .3646071   .2886476     1.26   0.207    -.2011317     .930346
> >>>>         unique 
> |   .1462646   .6053367     0.24   0.809    -1.040174    1.332703
> >>>>     z2location 
> |   .3362546   .2857039     1.18   0.239    -.2237147     .896224
> >>>>     z2chsenior 
> |  -.6377379   .2899929    -2.20   0.028    -1.206114   -.0693622
> >>>>      z2revenue 
> |   .4384878   .2596045     1.69   0.091    -.0703277    .9473032
> >>>> z2mconcentration 
> |  -.3356207   .3044584    -1.10   0.270    -.9323481    .2611067
> >>>>        z2var65 
> |    .680389   .2555405     2.66   0.008     .1795389    1.181239
> >>>>          _cons 
> |  -3.852209   1.534456    -2.51   0.012    -6.859688   -.8447304
> >>>> 
> -----------------+----------------------------------------------------------------
> >>>> 2                |
> >>>> z2targetcrossus 
> |  -1.859479   .9033436    -2.06   0.040        -3.63   -.0889579
> >>>>        z2usinl 
> |   29.60616   10.01538     2.96   0.003      9.97637    49.23595
> >>>>      z2chcross 
> |  -6.148175    2.31656    -2.65   0.008    -10.68855   -1.607802
> >>>>         z2chdo 
> |  -2.199313   .9065691    -2.43   0.015    -3.976156   -.4224705
> >>>>            woe 
> |   4.507415   2.227588     2.02   0.043     .1414227    8.873408
> >>>>             jv 
> |   20.13766   566.9975     0.04   0.972    -1091.157    1131.432
> >>>>             rd 
> |  -3.677817   1.353729    -2.72   0.007    -6.331076   -1.024557
> >>>>        network 
> |  -20.33935   566.9957    -0.04   0.971    -1131.631    1090.952
> >>>>      repoffice 
> |   5.012434   2.120188     2.36   0.018     .8569423    9.167927
> >>>>          z2age 
> |   .5531285   .5823306     0.95   0.342    -.5882184    1.694475
> >>>>         unique 
> |   -7.34904   2.068999    -3.55   0.000     -11.4042   -3.293877
> >>>>     z2location 
> |   3.362253   .9824052     3.42   0.001     1.436774    5.287732
> >>>>     z2chsenior 
> |   .4177627   .4609106     0.91   0.365    -.4856056    1.321131
> >>>>      z2revenue 
> |   .8860638   .4915586     1.80   0.071    -.0773735    1.849501
> >>>> z2mconcentration 
> |   1.555764    .692565     2.25   0.025     .1983613    2.913166
> >>>>        z2var65 
> |  -.5325391    .556961    -0.96   0.339    -1.624163    .5590843
> >>>>          _cons 
> |    8.84202   3.903878     2.26   0.024      1.19056    16.49348
> >>>> 
> ----------------------------------------------------------------------------------
> >>>> model with interaction:
> >>>> . gologit2 ma2 z2targetcrossus z2usinl z2chcross z2chdo woe jv 
> rd network repoffice z2age unique z2location z2chsenior z2reve
> >>>>> nue z2mconcentration z2var65 z2targetrd
> >>>>
> >>>> Generalized Ordered Logit Estimates               Number of 
> obs   =        230
> >>>>                                                 LR 
> chi2(51)     =     148.17
> >>>>                                                 Prob > 
> chi2     =     0.0000
> >>>> Log likelihood = -182.55401                       Pseudo 
> R2       =     0.2887
> >>>>
> >>>> 
> ----------------------------------------------------------------------------------
> >>>>            ma2 |      Coef.   Std. 
> Err.      z    P>|z|     [95% Conf. Interval]
> >>>> 
> -----------------+----------------------------------------------------------------
> >>>> 0                |
> >>>> z2targetcrossus 
> |  -.1086327   .1786952    -0.61   0.543    -.4588687    .2416034
> >>>>        z2usinl 
> |   -.224747   .1906251    -1.18   0.238    -.5983653    .1488713
> >>>>      z2chcross 
> |   .1182669    .153385     0.77   0.441    -.1823621     .418896
> >>>>         z2chdo 
> |  -.1046159   .1873578    -0.56   0.577    -.4718305    .2625987
> >>>>            woe 
> |   -.531372   .6963073    -0.76   0.445    -1.896109    .8333653
> >>>>             jv 
> |   -1.16429   .8444907    -1.38   0.168    -2.819462     .490881
> >>>>             rd 
> |  -.2352473   .6686808    -0.35   0.725    -1.545838    1.075343
> >>>>        network 
> |   1.654026   .9829236     1.68   0.092    -.2724686    3.580521
> >>>>      repoffice 
> |  -1.165368   .4452497    -2.62   0.009    -2.038042   -.2926951
> >>>>          z2age 
> |   .0913011   .1810404     0.50   0.614    -.2635316    .4461339
> >>>>         unique 
> |   .6011205   .3888613     1.55   0.122    -.1610336    1.363275
> >>>>     z2location 
> |   .1073559   .1764745     0.61   0.543    -.2385278    .4532396
> >>>>     z2chsenior 
> |    .111229   .1726965     0.64   0.520    -.2272498    .4497079
> >>>>      z2revenue 
> |  -.0261089   .1675079    -0.16   0.876    -.3544183    .3022005
> >>>> z2mconcentration 
> |  -.2419455    .176234    -1.37   0.170    -.5873579    .1034668
> >>>>        z2var65 
> |   .2004429   .1538167     1.30   0.193    -.1010322    .5019181
> >>>>     z2targetrd 
> |   .5360898   .2728751     1.96   0.049     .0012645    1.070915
> >>>>          _cons 
> |  -.1930397   .7108041    -0.27   0.786     -1.58619    1.200111
> >>>> 
> -----------------+----------------------------------------------------------------
> >>>> 1                |
> >>>> z2targetcrossus 
> |   .9581676   .4375794     2.19   0.029     .1005277    1.815808
> >>>>        z2usinl 
> |  -7.478695   3.056712    -2.45   0.014    -13.46974    -1.48765
> >>>>      z2chcross 
> |    .183797   1.136537     0.16   0.872    -2.043775    2.411369
> >>>>         z2chdo 
> |   .0680997   .3728366     0.18   0.855    -.6626467    .7988461
> >>>>            woe 
> |  -.0332427   .8441079    -0.04   0.969    -1.687664    1.621178
> >>>>             jv 
> |  -2.196873   1.394514    -1.58   0.115    -4.930071    .5363247
> >>>>             rd 
> |   2.356204   1.012723     2.33   0.020     .3713034    4.341105
> >>>>        network 
> |   .5403172     1.4498     0.37   0.709    -2.301238    3.381872
> >>>>      repoffice 
> |      1.179   .7543806     1.56   0.118    -.2995585    2.657559
> >>>>          z2age 
> |   .3700746   .3043611     1.22   0.224    -.2264622    .9666115
> >>>>         unique 
> |   .0186732   .6888141     0.03   0.978    -1.331378    1.368724
> >>>>     z2location 
> |   .4302243   .3273268     1.31   0.189    -.2113244    1.071773
> >>>>     z2chsenior 
> |  -.9859917   .3561425    -2.77   0.006    -1.684018   -.2879651
> >>>>      z2revenue 
> |   .5751533   .2707602     2.12   0.034     .0444731    1.105833
> >>>> z2mconcentration 
> |   -.287354    .334713    -0.86   0.391    -.9433795    .3686715
> >>>>        z2var65 
> |   .7992744   .2936887     2.72   0.006     .2236551    1.374894
> >>>>     z2targetrd 
> |  -.7707145   .3850767    -2.00   0.045    -1.525451   -.0159781
> >>>>          _cons 
> |  -4.906128   1.712643    -2.86   0.004    -8.262847    -1.54941
> >>>> 
> -----------------+----------------------------------------------------------------
> >>>> 2                |
> >>>> z2targetcrossus 
> |   -1.40224    .940126    -1.49   0.136    -3.244853     .440373
> >>>>        z2usinl 
> |   27.85302   9.856849     2.83   0.005     8.533951    47.17209
> >>>>      z2chcross 
> |  -5.887844   2.251756    -2.61   0.009     -10.3012   -1.474484
> >>>>         z2chdo 
> |  -2.131811   .9187053    -2.32   0.020     -3.93244   -.3311814
> >>>>            woe 
> |   4.412064   2.322375     1.90   0.057    -.1397075    8.963836
> >>>>             jv 
> |   19.98229   628.5635     0.03   0.975     -1211.98    1251.944
> >>>>             rd 
> |  -2.206978   1.697812    -1.30   0.194    -5.534629    1.120673
> >>>>        network 
> |  -20.38483   628.5619    -0.03   0.974    -1252.343    1211.574
> >>>>      repoffice 
> |   5.196042   2.137634     2.43   0.015     1.006356    9.385727
> >>>>          z2age 
> |   .7685141   .6105997     1.26   0.208    -.4282392    1.965267
> >>>>         unique 
> |  -8.068043   2.163387    -3.73   0.000     -12.3082   -3.827882
> >>>>     z2location 
> |   3.371957   1.005542     3.35   0.001     1.401131    5.342782
> >>>>     z2chsenior 
> |   .3486575   .5132131     0.68   0.497    -.6572217    1.354537
> >>>>      z2revenue 
> |   .9434554   .5230482     1.80   0.071    -.0817001    1.968611
> >>>> z2mconcentration 
> |   1.711886   .6913372     2.48   0.013     .3568901    3.066882
> >>>>        z2var65 
> |  -.4395993   .5783425    -0.76   0.447     -1.57313    .6939311
> >>>>     z2targetrd 
> |  -.6754823   .5818942    -1.16   0.246    -1.815974    .4650093
> >>>>          _cons 
> |   8.627572   3.824513     2.26   0.024     1.131665    16.12348
> >>>> 
> ----------------------------------------------------------------------------------
> >>>>

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

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