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Posted 11-02-2019 09:18 AM
(2439 views)

Does anyone now a good non parametric alternative procedure in sas for GLMM?

I was thinking about Gampl, however, I am not confident about it.

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GAMPL and ADAPTIVEREG are both nonparametric procedures. For a GAMPL example, see "Nonparametric regression for binary response data in SAS"

You might also consider defining a spline effect and using GLIMMIX. To learn more about modeling with spline effects, see this example that uses restricted cubic splines.

You can also read about how to interpret the regression coefficients for a spline-effects model..

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Thanks for your answer!

Is it also possible to use glimmix for data that is not normal distributed (but should be), but with a different distribution. I allready tried to transform the data.

The SAS System The GLM Procedure Dependent Variable: Pancreas_rel Source DF Sum of Squares Mean Square F Value Pr > F Model 3 19.2570463 6.4190154 0.89 0.4518 Error 56 403.6712273 7.2084148 Corrected Total 59 422.9282736 R-Square Coeff Var Root MSE Pancreas_rel Mean 0.045533 76.68989 2.684849 3.500917 Source DF Type I SS Mean Square F Value Pr > F diet 1 0.04273536 0.04273536 0.01 0.9389 strain 1 11.88762207 11.88762207 1.65 0.2044 diet*strain 1 7.32668889 7.32668889 1.02 0.3177 Source DF Type III SS Mean Square F Value Pr > F diet 1 0.02835042 0.02835042 0.00 0.9502 strain 1 10.77579181 10.77579181 1.49 0.2266 diet*strain 1 7.32668889 7.32668889 1.02 0.3177 Panel of Fit Diagnostics for Pancreas_rel Interaction Plot for Pancreas_rel by diet

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Correct. You would use the DIST=NORMAL option and check whether the residuals of the model are approximately normal by looking at a Q-Q plot.

To clarify the difference between the response variable being normally distributed and the RESIDUALS being normally distributed, please see the article "On the assumptions (and misconceptions) of linear regression"

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At first glance, these diagnostic plots look reasonable, except for the three outliers in the pancreas_rel variable. Which plots are bothering you?

If you show us the procedure statements that you are using, we might be able to offer additional advice.

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If your goal is prediction, the predicitons of OLS are still valid even without the normality assumption.

Inferences are robust to mild deviations from the normality-of-residual assumptions, but you could point out in your report that the normality assumptions are dubious. If you want distribution-free inferential statistics, use bootstrap methods.

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I used indeed an interaction in this model! However, with transforming with log, it was not really improved.

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