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Non iterative Principal Factor Analysis (PFA) . This is an approach which tries to detect underlying structures in the relationships between the variables of interest. Unlike PCA, the PFA is focused only on the shared variances of the set of variables. It is suited when the goal is to uncover the latent structure of the variables. It works on a slightly modified version of the correlation matrix where the diagonal, the prior communality estimate of each variable, is replaced by its squared multiple correlation with all others. Harris Component Analysis . This is a non-iterative factor analysis approach. It tries to detect underlying structures in the relationships between the variable of interest. Like Principal Factor Analysis, it focuses on the shared variances of the set of variables. It works on a modified version of the correlation matrix. Principal Component Analysis . Two functionalities are added: the reproduced and residual correlation matrices can be computed, the variables c

AFDM (Factor analysis for mixed data) . It extends the principal component analysis (PCA) to data containing a mixture of quantitative and qualitative variables. The method is developed by Pagès (2004). A tutorial will come to describe the use of the method and the reading of the results. Download page : setup