proc model dummies

[sasphd]

I am runnig proc model and when I add dummies in my regression the model converges but in the output T-values are B.

what does it mean and How I can solve that????

[Reeza]

You likely over parameterized your model. How did you create your dummy variables? If you had a variable with, say 3 levels, did you include 2 or 3 dummy variables?

[sasphd]

this my model 

disGDFF=a+a1*logage+a2*logTNA+a3*logMT+a4*EXP_RATIO+a5*turn+a6*logtnasq+a7*AG+a8*EI+a9*G+a10*LTG+a11*GI+a12*MC+a13*MRC+a14*SC+a15*MCG;

AG EI G LTG GI MC MRC SC MCG are style dummies variables. by line dummy will be 1 for one style for example G and 0 for others styles 

[Reeza]

That doesn't answer my questions. 

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@sasphd wrote:

this my model 

disGDFF=a+a1*logage+a2*logTNA+a3*logMT+a4*EXP_RATIO+a5*turn+a6*logtnasq+a7*AG+a8*EI+a9*G+a10*LTG+a11*GI+a12*MC+a13*MRC+a14*SC+a15*MCG;

AG EI G LTG GI MC MRC SC MCG are style dummies variables. by line dummy will be 1 for one style for example G and 0 for others styles 

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[sasphd]

so I did not understand your question ?? can you please explain what did you mean by levels
How did you create your dummy variables?
if the style = AG so AG=1 else AG=0

[Reeza]

If you have a categorical variable, say Sex, it has two levels, F and M. 

Using whatever method you used to create the dummy variables did you create one or two dummy variables?

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@sasphd wrote:
so I did not understand your question ?? can you please explain what did you mean by levels
How did you create your dummy variables?
if the style = AG so AG=1 else AG=0

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[sasphd]

for each dummy variable is 1 or 0

and I have nine dummies variables 

[Reeza]

I can't see your data or code so I'm trying to make a generic example that we can both refer to, which is why I'm using SEX as the example. 

If you had SASHELP.CLASS for example and wanted to include Sex in the model, which has 2 levels, F and M. 

You would create N-1 dummy variables, where N is the number of levels. 

So this means 1 dummy variable, such as Sex: 1 = Female, 0 = male. 

I don't know what you did or how you created your variables (I have asked but you never answered the questions) so I'm assuming you created two dummy variables. Because of this, the dummies are linear combinations of each other so that there is redundant information. In this case, there cannot be an estimate for one of the dummy variables. 

Good Luck. 

[sasphd]

Thanks you.
Just to give you an idea of my data

id EI G LTG GI MC MRC SC MCG
1 0 0 0 0 0 0 0 1
2 1 0 0 0 0 0 0 0

[Reeza]

You need to answer the initial questions. 

How many categories did you originally have before creating dummy variables?

Your Answer: 

How many dummy variables did you created?

Your Answer: 

[sasphd]

How many categories did you originally have before creating dummy variables?

Your Answer: 9 categories

How many dummy variables did you created?

Your Answer: 9 categories

[Reeza]

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@sasphd wrote:

How many categories did you originally have before creating dummy variables?

Your Answer: 9 categories

How many dummy variables did you created?

Your Answer: 9 categories

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You need 8, not 9. Because you can figure out the 9th from the other 8. 

So you either get one that's reported as B, or exclude it from the analysis in the first place. This is known as your reference group, what all the other levels are compared to as the 'baseline' for example. 

[Reeza]

https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-dummy-coding/

[sasphd · Accepted solution]

thanks I find the solution. just eliminate the constant in the model.