Re: [amelia] tscs + PCA

Dear Matt, I do not answer your question directly but I can suggest another way to deal with missing values in the framework of PCA. Indeed, it is possible to modify PCA algorithms such that it can handle missing values. In my lab, we work on this topic and we have developed an R package named missMDA. This package is dedicated to handle missing values in principal components method such as principal component analysis. The rationale of the proposed method is the following: First, an EM algorithm (named EM-PCA) is implemented to obtain estimate of the scores and of the loadings despite the missing values. The algorithm consists in alternating two steps: one step to estimate the parameters via PCA and one step to impute the missing values using the PCA model (named also the reconstruction formulae). Consequently, at the end of the algorithm, a completed data set is obtained as well as the scores and loadings (if you achieve your PCA on the completed data set, you find the same loadings and scores). Then we have proposed a multiple imputation procedure using the PCA model. To visualize the different plausible imputations on the PCA maps, we have proposed confidences areas around the position of the individuals and the variables. References can be found in the following article: Josse, Julie; Pagès, Jérôme; Husson, François (2011). Multiple imputation in principal component analysis./Advances in Data Analysis and Classification/: 1-16, March 06, 2011. J. Josse Le 07/04/2011 02:03, matthew-c.johnson(a)ubs.com a écrit :