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    <p>The general solutions suggested in the comments by @Chase and @bill_080 need a little bit of enhancement to (partially) fulfil the needs of the OP.</p> <p>A reproducible example:</p> <pre><code>require(MASS) set.seed(1) dat &lt;- data.frame(mvrnorm(100, mu = c(2,6,3), Sigma = matrix(c(10, 2, 4, 2, 3, 0.5, 4, 0.5, 2), ncol = 3))) </code></pre> <p>Compute the dissimilarity matrix of the standardised data using Eucildean distances</p> <pre><code>dij &lt;- dist(scale(dat, center = TRUE, scale = TRUE)) </code></pre> <p>and then calculate a hierarchical clustering of these data using the group average method</p> <pre><code>clust &lt;- hclust(dij, method = "average") </code></pre> <p>Next we compute the ordering of the samples on basis of forming 3 ('k') groups from the dendrogram, but we could have chosen something else here.</p> <pre><code>ord &lt;- order(cutree(clust, k = 3)) </code></pre> <p>Next compute the dissimilarities between samples based on dendrogram, the cophenetic distances:</p> <pre><code>coph &lt;- cophenetic(clust) </code></pre> <p>Here are 3 image plots of:</p> <ol> <li>The original dissimilarity matrix, sorted on basis of cluster analysis groupings,</li> <li>The cophenetic distances, again sorted as above</li> <li>The difference between the original dissimilarities and the cophenetic distances</li> <li>A Shepard-like plot comparing the original and cophenetic distances; the better the clustering at capturing the original distances the closer to the 1:1 line the points will lie</li> </ol> <p>Here is the code that produces the above plots</p> <pre><code>layout(matrix(1:4, ncol = 2)) image(as.matrix(dij)[ord, ord], main = "Original distances") image(as.matrix(coph)[ord, ord], main = "Cophenetic distances") image((as.matrix(coph) - as.matrix(dij))[ord, ord], main = "Cophenetic - Original") plot(coph ~ dij, ylab = "Cophenetic distances", xlab = "Original distances", main = "Shepard Plot") abline(0,1, col = "red") box() layout(1) </code></pre> <p>Which produces this on the active device:</p> <p><img src="https://i.stack.imgur.com/9AvaA.png" alt="plots of original and cophenetic distances"></p> <p>Having said all that, however, only the Shepard plot shows the "correlation between clustered data and [dis]similarity matrix", and that is not an image plot (levelplot). How would you propose to compute the correlation between two numbers for all pairwise comparisons of cophenetic and original [dis]similarities?</p>
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    1. POIn R, how can I plot a similarity matrix (like a block graph) after clustering data?
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    1. USGavin Simpson
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