What's a meaningful "correlation" measure to study the relation between the such two types of variables?
In R, how to do it?
Thanks a lot!
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For a moment, let's ignore the continuous/discrete issue. Basically correlation measures the strength of the linear relationship between variables, and you seem to be asking for an alternative way to measure the strength of the relationship. You might be interested in looking at some ideas from information theory. Specifically I think you might want to look at mutual information. Mutual information essentially gives you a way to quantify how much knowing the state of one variable tells you about the other variable. I actually think this definition is closer to what most people mean when they think about correlation. For two discrete variables X and Y, the calculation is as follows: $$I(X;Y) = \sum_{y \in Y} \sum_{x \in X} p(x,y) \log{ \left(\frac{p(x,y)}{p(x)\,p(y)} \right) }$$ For two continuous variables we integrate rather than taking the sum: $$I(X;Y) = \int_Y \int_X p(x,y) \log{ \left(\frac{p(x,y)}{p(x)\,p(y)} \right) } \; dx \,dy$$ Your particular use-case is for one discrete and one continuous. Rather than integrating over a sum or summing over an integral, I imagine it would be easier to convert one of the variables into the other type. A typical way to do that would be to discretize your continuous variable into discrete bins. There are a number of ways to discretzie data (e.g. equal intervals), and I believe the entropy package should be helpful for the MI calculations if you want to use R. |
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If the categorical variable is ordinal and you bin the continuous variable into a few frequency intervals you can use Gamma. Also available for paired data put into ordinal form are Kendal's tau, Stuart's tau and Somers D. These are all available in SAS using Proc Freq. I don't know how they are computed using R routines. Here is a link to a presentation that gives detailed information: http://faculty.unlv.edu/cstream/ppts/QM722/measuresofassociation.ppt#260,5,Measures of Association for Nominal and Ordinal Variables |
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