In some cases, we may want to use principal component analysis on several time-series variables, or some regionalized random variables (i.e., spatial varying variables). The variability of these variables usually contain large-scale trend, which might result in the variables being correlated. Therefore, the correlation obtained from conventional multivariate statistics might be overestimated. I want to know what is the effect of the overestimated correlation for PCA scores? Whether should I detrend the variables before performing PCA?
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Overestimated relative what? PCA itself is indifferent whether correlations in the input variables are "true" or "twisted". Sure, the magnitude of correlations affect PCA results. One should consider carefully potentially useful transformations before PCA. – ttnphns Aug 28 '21 at 08:22
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@ttnphns Thank you for your answer. "Overestimated" here I mean the absolute correlation coefficient is larger than that for the reference variables (without simulated trend term added). I understand you agree with that I should detrend variables before calculating their correlation coeffcient. – tunar Aug 28 '21 at 09:00