Principal components analysis in mixed epidemiological data

Resumo

The principal components analysis is a dimension reduction technique from multivariate data analysis, whose objective is to synthesize information from a dataset, reducing the number of variables to a lower number of components or factors that explain most of the variance found in the data. These components or factors constitute a linear combination of the original variables and are linearly independent [1]. The technique has a double purpose: to optimally represent in a space of small dimension, observations of a larger p-dimensional general space; and to identify possible “latent” variables that generate the variability of the data. The components are independent and therefore facilitate the interpretation of the data [2].[...]