Análisis de Componentes Principales en presencia de datos faltantes: el principio de datos disponibles.

In this paper we propose to use the principle of available data derived from the NIPALS (Nonlinear estimation by Iterative Partial Least Square) algorithm to work on the Principal Components Analysis (PCA) in the presence of missing data. This proposal is important since it does not perform data imputation, nor are individuals discarded from the database; the proposed method works with the available pairs to form the quasicorrelation matrices in 𝑹𝒑 and in 𝑹𝒏; the spectral decomposition of these matrices allows through the transition relations to realize a conventional PCA. From the simulation study carried out, it was found that as the percentage of missing data increases, the inertia explained in the first factorial plane decreases. The solution algorithm was developed under the R programming environment and the code is appended for free use. [ABSTRACT FROM AUTHOR]