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Sorry, I am not enough educated to catch up your goals about checking the correlation efficient between two covariance matrices, but I tried to apprehend your code per se. If I am right, you are making up 10.000 different matrices from the same matrix (<code>mat2</code>) by reordering the cells all round, and recomputing the correlation between the covariance matrix of <code>mat1</code> and the covariance matrix of the resampled array. Those are stored in the <code>statSim</code> variable. You said the original correaltion efficient was high (<code>statObs</code>), but the maximum of <code>statSim</code> was low, which is strange. I think the problem is with your result list: <pre><code>list( obs = statObs , sumFit = sum(statSim > statObs)/numR) </code></pre> Where you return the original correaltion coefficient (<code>obs</code>), but not the written maximum with <code>sumFit</code>. There you might use eg. <code>max(statSim)</code>. I see the point in returning <code>sumFit</code> for checking if the resampling did any improvement to the correlation efficient, but based on your code, I see no problem about the theory. Updated function with <code>max</code> of simulated correlation coefficients: <pre><code>resamplerSimAlt <- function(mat1, mat2, numR, graph = FALSE) { statSim <- numeric(numR) mat1vcv <- cov(mat1) mat2vcvT <- cov(mat2) ltM1 <- mat1vcv[col(mat1vcv) <= row(mat1vcv)] ltM2T <- mat2vcvT[col(mat2vcvT) <= row(mat2vcvT)] statObs <- cor(ltM1, ltM2T) indice <- c(1:length(mat2)) resamplesIndices <- lapply(1:numR, function(i) sample(indice, replace = F)) for (i in 1:numR) { ss <- mat2[sample(resamplesIndices[[i]])] ss <- matrix(ss, nrow = dim(mat2)[[1]], ncol = dim(mat2)[[2]]) mat2ss <- cov(ss) ltM2ss <- mat2ss[col(mat2ss) <= row(mat2ss)] statSim[i] <- cor(ltM1, ltM2ss) } if (graph == TRUE) { plot(1, main = "resampled data density distribution", xlim = c(0, statObs+0.1), ylim = c(0,14)) points(density(statSim), type="l", lwd=2) abline(v = statObs) text(10, 10, "observed corelation = ") } list( obs = statObs , sumFit = sum(statSim > statObs)/numR, max=max(statSim)) } </code></pre> What I had run: <pre><code>> mat1 <- matrix(runif(25),5,5) > mat2 <- mat1+0.2 > resamplerSimAlt(mat1, mat2, 10000) $obs [1] 1 $sumFit [1] 0 $max [1] 0.94463 </code></pre> And with random <code>mat2</code>: <pre><code>> mat2 <- matrix(runif(25),5,5) > resamplerSimAlt(mat1, mat2, 10000) $obs [1] 0.31144 $sumFit [1] 0.9124 $max [1] 0.9231 </code></pre> My answer might not be a real answer. If that would be the case, please give more details about the problem :)
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1. COIn fact the sumFit part of the function was meant to provide some sort of P value but I overlooked the sum, so it just should go like this `sumFit = length(statSim > statObs)/numR`. Thanks for the support.
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