https://communities.sas.com/t5/Statistical-Procedures/interpreting-variability-explained-by-exposure-in-multilevel/m-p/533931#M26890 <P>Under a linear regression, the parameter estimates are chosen in such a way to minimize the residual variance.&nbsp; That minimization gives you the concept of an R**2 statistic, and you can talk about the percentage of variance that a model explains.</P> <P>&nbsp;</P> <P>With a logistic regression, you are calculating the parameter estimates using maximum likelihood.&nbsp; You are not minimizing a residual variance, hence you don't have a traditional R**2 statistic to fall back on.&nbsp; Several psuedo-R**2 measures have been proposed in the statistical literature, but I don't know that any universal acceptance has been reached on one measure over another.&nbsp;&nbsp;</P> Fri, 08 Feb 2019 14:36:50 GMT StatsMan 2019-02-08T14:36:50Z https://communities.sas.com/t5/Statistical-Procedures/interpreting-variability-explained-by-exposure-in-multilevel/m-p/532144#M26833 <P>Hi Sas community! I am analyzing whether a census-tract level variable (exposure), which is ordinal and has 5 levels, is associated with a binary outcome (outcome), after adjusting for race (racecat), age (agegrp) and clustering by census tract (tract10). My reference category for exposure is 5 and I found significant associations between each lower level of the variable and the reference category of 5. This is the code I used:</P><P>&nbsp;</P><P>proc glimmix data=dat;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</P><P>class tract10 exposure racecat agegrp;</P><P>model outcome (event='1') = exposure racecat agegrp/ dist=binary link=logit ddfm=bw solution oddsratio cl;</P><P>random intercept / subject=tract10 ;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; run;&nbsp;</P><P>&nbsp;</P><P>I have been asked to look into what % variability can be explained by the exposure variable or by the model, but because I can’t use R^2 for this kind of model, I am at a loss. Can anyone provide some guidance on how to answer this question??</P> Fri, 01 Feb 2019 18:26:32 GMT https://communities.sas.com/t5/Statistical-Procedures/interpreting-variability-explained-by-exposure-in-multilevel/m-p/532144#M26833 kb4 2019-02-01T18:26:32Z https://communities.sas.com/t5/Statistical-Procedures/interpreting-variability-explained-by-exposure-in-multilevel/m-p/533931#M26890 <P>Under a linear regression, the parameter estimates are chosen in such a way to minimize the residual variance.&nbsp; That minimization gives you the concept of an R**2 statistic, and you can talk about the percentage of variance that a model explains.</P> <P>&nbsp;</P> <P>With a logistic regression, you are calculating the parameter estimates using maximum likelihood.&nbsp; You are not minimizing a residual variance, hence you don't have a traditional R**2 statistic to fall back on.&nbsp; Several psuedo-R**2 measures have been proposed in the statistical literature, but I don't know that any universal acceptance has been reached on one measure over another.&nbsp;&nbsp;</P> Fri, 08 Feb 2019 14:36:50 GMT https://communities.sas.com/t5/Statistical-Procedures/interpreting-variability-explained-by-exposure-in-multilevel/m-p/533931#M26890 StatsMan 2019-02-08T14:36:50Z