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Posted 11-28-2016 03:05 PM
(2582 views)

Is there a way to switch the default dummy-coding of CLASS variables to effect-coding in PROC GLIMMIX?

My goal is to obtain tests for each level of a fixed class variable against sample average in PROC GLIMMIX. In PROC LOGISTIC the CLASS statement has an option ‘PARAM=EFFECT’ to accomplish this. There does not seem to be an equivalent for that in PROC GLIMMIX. Am I missing something?

If I can't change the way a CLASS variable is parameterized by GLIMMIX, what would you recommend for me to achieve my goal? I know I could manually effect-code each level of my class variable in the source dataset and enter all of those effect-coded variables into the model. I can also use a series of ESTIMATE statements to compute the desired targets by using dummy-code default. Trouble is that my class variable has over 400 levels and both of the options above require a whole lot of coding. I have hard time believing that SAS took away PARAM=EFFECT option without an easy alternative.

Do you have any other suggestions?

Thanks,

Haris

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SAS Tech Support came through rapidly: DIFF=ANOM option in the LSMEANS statement in PROC GLIMMIX is exactly what I was looking for.

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Haris, Since you figured it out, maybe you can help me? I added a "lsmeans var / diff=anom" line to my proc glimmix code and all that did was lines to my output populated with . (missing values) for the main effects. Interestingly, they are the inverse in direction, but the same in magnitude as the original output without the lsmeans var / diff=anom line.

I have to conduct these follow up tests for two separate analyses. In one the two model variables are categorical with two levels each and their interaction. In a second, I have a two categorical variables with 2 levels each, a linear variable, the quadratic of that linear variable, and all the higher-order interactions.

Any help would be much appreciated.

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I would recommend manually coding your categorical variables if you want to use effect coding.

For example, if you have a categorical variable called **hospital** with values of "Hospital A", "Hospital B", and "Hospital C", rather than entering hospital into proc glimmix in the class statement, instead create two new variables, "Hospital_A" and "Hospital_B" (Hospital C is arbitrarily designated the reference group).

If hospital = "Hospital A", then Hospital_A = 1, hospital = "Hospital B", then Hospital_A = 0, and if hospital = "Hospital C", then Hospital_A = -1. Use the same logic for the Hospital_B variable.

Manual coding allows you to choose which category to use as a reference and directly find the effect code coefficients in the Solutions for Fixed Effects output (use the "solution" option in the model statement). I would also use manual coding when using weighted effect coding, when there are an unequal number of observations across categories.

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RobertF,

Manual coding is nice if you have a reasonable number of categories. My categorical variable is approaching 500 levels.

Another nice thing about DIFF=ANOM is that it gives you effects for each level of the predictor without the need to re-run the model with a different reference category to obtain the delta for that level.

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