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RV coefficient
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<p>In statistics, the RV coefficient is a <a href="/facts/Multivariate_statistics/tDuNcWrL">multivariate</a> generalization of the <i>squared</i> <a href="/facts/Pearson_correlation_coefficient/Igf0xDPc">Pearson correlation coefficient</a> (because the RV coefficient takes values between 0 and 1). It measures the closeness of two set of points that may each be represented in a <a href="/facts/Matrix_(mathematics)/qa8FI4ko">matrix</a>. </p><p>The major approaches within <a href="/facts/Multivariate_statistics/tDuNcWrL">statistical multivariate data analysis</a> can all be brought into a common framework in which the RV coefficient is maximised subject to relevant constraints. Specifically, these statistical methodologies include: </p> <ul><li><a href="/facts/Principal_component_analysis/pCbVTqdR">principal component analysis</a></li> <li><a href="/facts/Canonical_correlation_analysis/LuX8FltG">canonical correlation analysis</a></li> <li>multivariate <a href="/facts/Regression_analysis/n6z5Tf7K">regression</a></li> <li><a href="/facts/Statistical_classification/jXXHRkXR">statistical classification</a> (<a href="/facts/Linear_discriminant_analysis/Mj6KQhRN">linear discrimination</a>).</li></ul> <p>One application of the RV coefficient is in <a href="/facts/Functional_neuroimaging/r0k8tKGR">functional neuroimaging</a> where it can measure the similarity between two subjects' series of brain scans or between different scans of a same subject. </p>