Re: st: Testing interaction terms

[Richard Goldstein <richgold@ix.netcom.com>]

note that var00 does not exist and I ignore it here; try:

regress partner var11 var10 var01, hascons
test (var10+var11)/2=var11

Rich

On 6/19/13 9:27 AM, Verena Dill wrote:
> What you wrote is exactly what I did in my regression (output below,
> just for the matter of illustration I included the four categories; 
> var11: var1==1 & var2==1, var10: var1==1 & var2==0, var01: var1==0 &
> var2==1, var00: var1==0 & var2==0; because of the below mentioned
> structure of the data two of the categories are omitted).
> ------------------------------------------------------------------------------
> 
>      partner |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
> 
>        var11 |   .7478253   .3528458     2.12   0.034     .0562602    
> 1.43939
>        var10 |   .9636673   .3315029     2.91   0.004     .3139335   
> 1.613401
>        var01 |          0  (omitted)
>        var00 |          0  (omitted)
>        _cons |    -.63364   .3095231    -2.05   0.041    -1.240294  
> -.0269858
> 
> But my question is: how can I test if the coefficients _b[var11] and
> _b[var10] are equal taking the "interaction"-nature of the variables
> into account? Using only "test _b[var11]= _b[var10 ]"  does not account
> for that. Is there any other procedure I could use here (maybe similar
> to contrast)?
> 
> 
> 
> Am 19.06.2013 14:25, schrieb David Hoaglin:
>> Verena,
>>
>> Because the data have no observations for var1==0&  var2==0, it is not
>> possible to express the combined effect of those variables (in the
>> linear predictor) in the usual way,
>> (effect of var1) + (effect of var2) + (interaction).
>> One alternative approach is to treat the combination of var1 and var2
>> as a categorical variable with three categories: var1==0&  var2==1,
>> var1==1&  var2==0, and var1==1&  var2==1.
>>
>> David Hoaglin
>>
>> On Wed, Jun 19, 2013 at 8:06 AM, Verena Dill<dill@uni-trier.de>  wrote:
>>> I obtained the coefficients from a regression model and want to test
>>> whether
>>> or not the coefficients are significantly different from each other.
>>> The problem now is that the two variables are related to each other like
>>> interactions and only partly overlap.
>>>
>>> var1: variable 1 (dummy)
>>> var2: variable 2 (dummy)
>>> interaction: interaction of variable 1 and variable 2
>>>
>>> tab var1 var2
>>>
>>> var1         |            var2
>>>               |         0          1      |     Total
>>> -----------+----------------------+----------
>>>           0   |         0        122      |       122
>>>           1   |       322        256      |       578
>>> -----------+----------------------+----------
>>>       Total   |       322        378      |       700
>>>
>>> Since no observations exist for var1==0&  var2==0 I can only include the
>>> interaction and one of the variables (just to mention that: From a
>>> theoretical sense it makes sense to do so): "probit var1 interaction"
>>> Now I want to test if I can reject the hypothesis that
>>> _b[var1]=_b[interaction]. If I use the standard command "test" it
>>> does not
>>> account for the fact that these variables are related.
>>>
>>> Because of the nature of my variables I wanted to use the "contrast"
>>> command
>>> but this only works if I'd use something like this before: "probit
>>> var1##var2"  which is not solvable because of the above mentioned
>>> fact that
>>> var1==0&  var2==0 does not exist in the data.
>>>
>>> Can anybody suggest another command that takes into account that the two
>>> variables are interacted or has ideas on how to adjust the
>>> "contrast"-command?
>>>
>>> Any help is greatly appreciated!
>>>
>>> Verena
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