Contrasts necessary to test for significance of differences between each pair of slopes

[samillioneer]

I have a categorical predictor that has 5 groups in it. I want to create contrasts necessary to test for significance of differences between each pair of slopes and then use the Bonferroni adjustment to preserve the overall alpha for the simultaneous tests of slopes and draw a conclusion about the regression slopes. 

In case of a predictor with 3 groups, the SAS code would look like:

ods select SolutionF Contrasts;
contrast 'A vs B' pos*pts 1 -1 0;
contrast 'A vs C' pos*pts 1 0 -1;
contrast 'B vs C' pos*pts 0 1 -1;

.. How to work in case of 5 groups ?

[sld]

Please provide more detail. See the posting guidelines here:

https://communities.sas.com/t5/Getting-Started/How-to-get-fast-helpful-answers/ta-p/226133/jump-to/f...

and here

http://support.sas.com/resources/papers/proceedings12/189-2012.pdf

In particular, post the full code for the statistical model, and show what you've tried so far.

[samillioneer]

Thank you for correcting my rookie mistake, I'll be elaborate henceforth. My dataset consists of response(R) and predictors(a,b,c,d,e) + a categorical predictor(x, which has five groups x1,x2,x3,x4,x5).

My regression model is R = const+a(beta1)+b(beta2)+c(beta3)+d(beta4)+e(beta5)+x(beta6)+a*x*(beta7)

I want to test for significance of differences between each pair of slopes corresponding to a*x1, a*x2,a*x3, a*x4,a*x5. We will have 10 such pairs ( 5C2). 

Here's the SAS code that produces a reparametrized model and gives the solutions table for the intercepts and slopes

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data hye;
input a $ b c d e;
datalines;

/*data goes here*/

;

proc mixed data=hye method=type3;
class x;
model = b c d a a*x / noint solution;

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Further, I would like to construct the ten contrasts necessary to test for significance of differences between each pair of slopes. I'm completely at loss how to go about it. 

In case of three contrasts(3C2) originating from 3 groups for a categorical predictor, the code would be like this:

ods select SolutionF Contrasts;
contrast 'x1 vs x2' a*x 1 -1 0;
contrast 'x1 vs x3' a*x 1 0 -1;
contrast 'x2 vs x3' a*x 0 1 -1;

[sld · Accepted solution]

Thank you for the additional information. You've made an excellent start. 

The INPUT statement in your DATA step is probably wrong: it specifies a as a categorical variable ("a $"), and it is missing x. Perhaps you mean it to be

input x $ a b c d e;

The ESTIMATE statement in the GLIMMIX (but not the MIXED) procedure has an ADJUST option that will work for what you want:

http://documentation.sas.com/?docsetId=statug&docsetTarget=statug_glimmix_syntax10.htm&docsetVersion...

Try something like

proc glimmix data=hye;
  class x;
  model = x a*x b c d e / noint solution;
  estimate 'x1 vs x2' a*x  1 -1  0  0  0,
           'x1 vs x3' a*x  1  0 -1  0  0,
         /* add the other 7 comparisons */
           'x4 vs x5' a*x  0  0  0  1 -1 
         / adjust=bon stepdown adjdfe=row;
  run;

Note the change I've made in the MODEL statement: I added e, and replaced a with x; if the effect of the change is not obvious to you, run the model without b, c, d, and e to see how the model is parameterized with respect to a and x.

Note, too, that for your equation to match the code above, there will be no intercept (const), there will be no "a*beta1", and "beta6" (and "beta7") will actually be 5 parameters, one for each level of x.

By default, the estimates will be computed at b=c=d=e=0. Because there are no interactions involving these factors in the model, the estimates that you want will not be affected by the values of b, c, d, and e.

I have not run this code, and so I don't guarantee it will work, it might need tweaking (and I'm not sure about your data input).

[samillioneer]

Thank you for such a detailed response and a working solution. It indeed worked!

[sld]

Excellent! Thanks for letting me know.