"Arthur J. Kendall" schrieb: ... > If I recall correctly, Q and R factoring the same data matrix have equivalences. > I just cannot remember what they are right now.
Possibly a much more simple solution than mine. I am not fully aware of the special properties of a q-factorization, and remember the results of P and Q factoring should be roughly equivalent according to readings some years ago. Since in P-factor analysis we weight the measures for each variable with a different coefficient: accordig to the variables stddev, in Q-factor-analysis we weight the measures for each person with a different weight: the persons stddev in answering his questions. Maybe comparing the *centroids* (being the mean-vectors) of two methods may lead to identical results, I doubt, whether this is the same with principal components. But - if I remember right, some authors said, they are similar. Much more simple is the simple proceeding in the following, which I remembered after reading the link provided by Hiu Chung Law in this thread; a quick&dirty examination on data produced the same result as a classic PCA, even without the need of explicitely computing a correlation and a loadings-matrix: center each variable over the 150 measures along the row, so that the mean is 0 normalize data so that the sqsum along a row is 1 just use this data as a factor-loadings-matrix rotate for pca (iterative jacobi rotation between all 150 columns) The result is the PCA-Loadingsmatrix The pca-rotation is a simple task, which can even be programmed in a Excel-macro. Gottfried -- Gottfried Helms Univ. Kassel . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
