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    <p>Multiplying the x data by some factor won't really change the Spearman rank-order correlation coefficient, though.</p> <pre><code>&gt;&gt;&gt; x = numpy.random.uniform(-10,10,size=(20)) &gt;&gt;&gt; y = numpy.random.uniform(-10,10,size=(20)) &gt;&gt;&gt; scipy.stats.spearmanr(x,y) (-0.24661654135338346, 0.29455199407204263) &gt;&gt;&gt; scipy.stats.spearmanr(x*10,y) (-0.24661654135338346, 0.29455199407204263) &gt;&gt;&gt; scipy.stats.spearmanr(x*1e6,y) (-0.24661654135338346, 0.29455199407204263) &gt;&gt;&gt; scipy.stats.spearmanr(x*1e-16,y) (-0.24661654135338346, 0.29455199407204263) &gt;&gt;&gt; scipy.stats.spearmanr(x*(-2),y) (0.24661654135338346, 0.29455199407204263) &gt;&gt;&gt; scipy.stats.spearmanr(x*(-2e6),y) (0.24661654135338346, 0.29455199407204263) </code></pre> <p>(The second term in the tuple is the p value.)</p> <p>You can change its sign, if you flip the signs of the terms, but the whole point of Spearman correlation is that it tells you the degree to which <em>any</em> monotonic relationship would capture the association. Probably that explains why fmin isn't changing the multiplier much: it's not getting any feedback on direction, because the returned value is constant.</p> <p>So I don't see how what you're trying to do can work.</p> <p>I'm also not sure why you've chosen the sum of all the the Spearman coefficients and the p values as what you're trying to maximize: the Spearman coefficients can be negative, so you probably want to square them, and you haven't mentioned the p values, so I'm not sure why you're throwing them in.</p> <p>[It's possible I guess that we're working with different scipy versions and our spearmanr functions return different things. I've got 0.9.0.]</p>
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    1. POHow do I use scipy.optimize to minimize a set of functions?
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    1. COThanks. Your right, my assumption that changing the x values changes the correlation coefficient was incorrect. However, my y axis is actually a total objective function (sum) of all the values. So, if I change the x values then I've changed the y values, but by a different factor. I want to choose multipliers that make the largest sum of the correlation coefficients. I was assuming that this would give me the set of multipliers that is most beneficial for the group of x values. I'm sure my logic and math are skewed somehow.
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