I have researched some of the other articles available online regarding how to chose the components we want to retain. Below are the suggested ways: - 1. The eigenvalue-one criterion :: In principal component analysis, one of the most commonly used criteria for solving the number-of-components problem is the eigenvalue-one criterion, also known as the Kaiser criterion (Kaiser, 1960). With this approach, you retain and interpret any component with an eigenvalue greater than 1.00 2. The scree test. With the scree test (Cattell, 1966), you plot the eigenvalues associated with each component and look for a “break” between the components with relatively large eigenvalues and those with small eigenvalues. The components that appear before the break are assumed to be meaningful and are retained for rotation; those apppearing after the break are assumed to be unimportant and are not retained. This method was mentioned by kevin.c.anderson in the first reply to this post. 3. Proportion of variance accounted for, the criterion in solving the number of factors problem involves retaining a component if it accounts for a specified proportion (or percentage) of variance in the data set. For example, you may decide to retain any component that accounts for at least 5% or 10% of the total variance. This proportion can be calculated with a simple formula: Proportion = Eigenvalue for the component of interest / Total eigenvalues of the correlation matrix. What I am confused about here is that all the methods are focused around eigenvalues and not about the cumulative percentage of variance. In my current class, my professor has asked to focus on percentage. Please speak on the best way for selection of principal components. Thank you in advance!! P.S - the above information can be found at http://support.sas.com/publishing/pubcat/chaps/55129.pdf
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