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Re: st: gologit2 interaction
From
lan zhang <[email protected]>
To
[email protected]
Subject
Re: st: gologit2 interaction
Date
Sun, 1 Sep 2013 19:57:40 -0400
means significant or not?
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??
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 <[email protected]> wrote:
>
> On Sep 1, 2013, at 7:00 PM, Richard Williams <[email protected]> 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 <[email protected]> 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
>>>> ----------------------------------------------------------------------------------
>>>>
>>>> my question is: variable like z2targetcrossus is not significant in the raw model, but the interaction z2targetrd=z2targetcrossus*rd is significant.
>>>> How could this happen?
>>>> can i still use this result in my study?
>>>>
>>>> *
>>>> * For searches and help try:
>>>> * http://www.stata.com/help.cgi?search
>>>> * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/
>>> * http://www.ats.ucla.edu/stat/stata/
>>
>> -------------------------------------------
>> Richard Williams, Notre Dame Dept of Sociology
>> OFFICE: (574)631-6668, (574)631-6463
>> HOME: (574)289-5227
>> EMAIL: [email protected]
>> WWW: http://www.nd.edu/~rwilliam
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/