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Re: st: Plotting interactions


From   John Antonakis <[email protected]>
To   [email protected]
Subject   Re: st: Plotting interactions
Date   Mon, 30 Sep 2013 13:43:20 +0200

Right......not including the main effects is tantamount to having an omitted variable--the interaction will certainly correlate with its constituents.

See:

Evans, M. G. 1991. The problem of analyzing multiplicative composites. American Psychologist, 46(1): 6-15.

Best,
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor:
The Leadership Quarterly
Organizational Research Methods
__________________________________________

On 30.09.2013 13:34, David Hoaglin wrote:
Hi, Amal.

I won't speak to the subsequent programming, but it is unusual for the
predictors in a regression model to include the interaction of two
variables and not include the "main effect" of either of those
variables.  Would the results make better sense if your model included
the main effects of ethnicity_bi2 and smoke2?  You can include those
and the two-variable interaction by using the ## operator instead of
#.  If you simply want to combine those two variables in a 6-category
predictor (and have the first category, ethnicity_bi2 = 1 and smoke2 =
2, as part of the constant term), then that is what your current model
does.

David Hoaglin

On Mon, Sep 30, 2013 at 4:41 AM, Amal Khanolkar <[email protected]> wrote:
Hi All,

I'm trying to plot interactions post regression using the following syntax:

The model:

. regress bwtgestage_sd i.ethnicity_bi2#i.smoke2 sex ib2.magecat i.parity ib2.education i.famsit_new ib2.MBMI5 gestage_wk if multibirth==1, vce(robust)

Linear regression                                      Number of obs = 1144571
                                                        F( 22,1144548) = 4543.46
                                                        Prob > F      =  0.0000
                                                        R-squared     =  0.0820
                                                        Root MSE      =  .94207

--------------------------------------------------------------------------------------
                      |               Robust
        bwtgestage_sd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
ethnicity_bi2#smoke2 |
                 1 3  |  -.3777867   .0023854  -158.37   0.000    -.3824621   -.3731114
                 2 2  |  -.1160349   .0063877   -18.17   0.000    -.1285545   -.1035153
                 2 3  |  -.4195231   .0107743   -38.94   0.000    -.4406403   -.3984059
                 3 2  |  -.4354954   .0044767   -97.28   0.000    -.4442696   -.4267213
                 3 3  |   -.577776   .0153539   -37.63   0.000    -.6078691    -.547683
                      |
                  sex |   .0127302   .0017618     7.23   0.000     .0092772    .0161832
                      |
              magecat |
                   1  |   .0813647   .0062532    13.01   0.000     .0691086    .0936207
                   3  |  -.0483047   .0024836   -19.45   0.000    -.0531725   -.0434369
                   4  |  -.0756843   .0028012   -27.02   0.000    -.0811745    -.070194
                   5  |  -.1036756   .0037371   -27.74   0.000    -.1110003    -.096351
                   6  |  -.1385867   .0075963   -18.24   0.000    -.1534752   -.1236982
                      |
               parity |
                   2  |    .308966   .0020534   150.47   0.000     .3049415    .3129905
                   3  |   .4244317   .0026556   159.83   0.000     .4192269    .4296365
                      |
            education |
                   1  |   -.044489   .0029772   -14.94   0.000    -.0503241   -.0386539
                   3  |   .0334391   .0024809    13.48   0.000     .0285766    .0383016
                   4  |   .0482377   .0025474    18.94   0.000     .0432449    .0532304
                      |
           famsit_new |
                   3  |  -.0321022   .0055319    -5.80   0.000    -.0429445   -.0212598
                   4  |   -.023603   .0077085    -3.06   0.002    -.0387113   -.0084947
                      |
                MBMI5 |
                   1  |  -.2915349   .0039634   -73.56   0.000    -.2993029   -.2837669
                   3  |    .249358   .0023809   104.73   0.000     .2446914    .2540245
                   4  |   .3691747   .0042171    87.54   0.000     .3609092    .3774401
                      |
       gestage_wktemp |  -.0000848   .0005237    -0.16   0.871    -.0011111    .0009416
                _cons |  -.0348456   .0209644    -1.66   0.096    -.0759352     .006244


I then use the following :

qui foreach x of var magecat {
         sum `x', d
         replace `x' = r(p50)
         }
predict p
predict se, stdp
tw (line p ethnicity_bi2 if smoke2==2, sort) (line p ethnicity_bi2 if smoke2==3, sort)


I would like to know if the line above 'qui foreach x of var magecat' actually does indicate all categories of all variables magecat onwards as specified in the model including the continuous variable gestaga_wk?

I don't seem to get a graph I was expecting  - or at least I can't make sense of it. Have I specified the graph correctly or is there a better way plot interactions from a regression model?

Thanks!
Amal
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