Categorical PCA and Multiple Correlation in the Study of the Incidence of Dengue Fever in Communities of Paraguay

Emilio Gerardo Sotto Riveros, Santiago Gómez Guerrero, Christian Schaerer


The Principal Component Analysis (PCA) belongs to a group of multivariate statistical methods, and it is widely used as a descriptive technique in several fields to reduce the inherent complexity of having multiple variables. In public health observational studies, data of a mixed nature (discrete and continuous) are often found, as in [1]. Since the factors responsible for the epidemic and hemorrhagic varieties of dengue are complex and not yet fully understood, the Categorical Principal Components Analysis (CatPCA) emerges as an optimal tool to analyze the data collected. MSU, a novel measure of multiple correlation between variables, is also computed on the resulting PCA components to obtain a greater insight regarding the relevance of each variable. [...]

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