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copied!<p>For numerical data you have the solution. But it is categorical data, you said. Then life gets a bit more complicated...</p> <p>Well, first : The amount of association between two categorical variables is not measured with a Spearman rank correlation, but with a Chi-square test for example. Which is logic actually. Ranking means there is some order in your data. Now tell me which is larger, yellow or red? I know, sometimes R does perform a spearman rank correlation on categorical data. If I code yellow 1 and red 2, R would consider red larger than yellow.</p> <p>So, forget about Spearman for categorical data. I'll demonstrate the chisq-test and how to choose columns using combn(). But you would benefit from a bit more time with Agresti's book : <a href="http://rads.stackoverflow.com/amzn/click/0471360937" rel="noreferrer">http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937</a></p> <pre><code>set.seed(1234) X <- rep(c("A","B"),20) Y <- sample(c("C","D"),40,replace=T) table(X,Y) chisq.test(table(X,Y),correct=F) # I don't use Yates continuity correction #Let's make a matrix with tons of columns Data <- as.data.frame( matrix( sample(letters[1:3],2000,replace=T), ncol=25 ) ) # You want to select which columns to use columns <- c(3,7,11,24) vars <- names(Data)[columns] # say you need to know which ones are associated with each other. out <- apply( combn(columns,2),2,function(x){ chisq.test(table(Data[,x[1]],Data[,x[2]]),correct=F)$p.value }) out <- cbind(as.data.frame(t(combn(vars,2))),out) </code></pre> <p>Then you should get :</p> <pre><code>> out V1 V2 out 1 V3 V7 0.8116733 2 V3 V11 0.1096903 3 V3 V24 0.1653670 4 V7 V11 0.3629871 5 V7 V24 0.4947797 6 V11 V24 0.7259321 </code></pre> <p>Where V1 and V2 indicate between which variables it goes, and "out" gives the p-value for association. Here all variables are independent. Which you would expect, as I created the data at random.</p>

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