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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
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Switzerland
Tel ++41 (0)21 692-3438
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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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