How do you explain varimax rotation?
Varimax rotation (also called Kaiser-Varimax rotation) maximizes the sum of the variance of the squared loadings, where ‘loadings’ means correlations between variables and factors. This usually results in high factor loadings for a smaller number of variables and low factor loadings for the rest.
What does PCA rotation do?
The rotated PCA (RPCA) methods rotate the PCA eigenvectors, so they point closer to the local clusters of data points. Thus the rotated eigenvectors may bear greater resemblance to actual physical states (though they account for less variance) than the unrotated eigenvectors.
How is varimax rotation used in principal component analysis?
A VARIMAX rotation is a change of coordinates used in principal component analysis (PCA) that maximizes the sum of the variances of the squared loadings. Thus, all the coefficients (squared correlation with factors) will be either large or near zero, with few intermediate values. The goal is to associate each variable to at most one factor.
How does the Varimax factor transformation matrix work?
Varimax: orthogonal rotation maximizes variances of the loadings within the factors while maximizing differences between high and low loadings on a particular factor Orthogonal means the factors are uncorrelated The factor transformation matrix turns the regular factor matrix into the rotated factor matrix
What should the numbers be in each column of varimax?
Ideally, we should find that the numbers in each column are either far away from zero or close to zero. Numbers close to +1 or -1 or 0 in each column give the ideal or cleanest interpretation. If a rotation can achieve this goal, then that is wonderful.
What should the coefficients of a PCA be?
Thus, all the coefficients (squared correlation with factors) will be either large or near zero, with few intermediate values. The goal is to associate each variable to at most one factor. The interpretation of the results of the PCA will be simplified.