El método Statis como alternativa para detectar "response shift" en estudios de calidad de vida relacionada con la salud

Multi-set multivariate data analysis methods provide a way to analyze a series of tables together. In particular, the STATIS-dual method is applied in data tables where individuals can vary from one table to another, but the variables that are analyzed remain fixed. However, when you have a large number of variables or indicators, interpretation through traditional multiple-set methods is complex. For this reason, in this paper, a new methodology is proposed, which we have called Sparse STATIS-dual. This implements the elastic net penalty technique which seeks to retain the most important variables of the model and obtain more precise and interpretable results. As a complement to the new methodology and to materialize its application to data tables with fixed variables, a package is created in the R programming language, under the name Sparse STATIS-dual. Finally, an application to real data is presented and a comparison of results is made between the STATIS-dual and the Sparse STATIS-dual. The proposed method improves the informative capacity of the data and offers more easily interpretable solutions.

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“…Each symmetric matrix of cross products S h is vectorized. The vectors obtained are stored in a new matrix W H×J 2 [46,47].…”

Section: Statis-dual Stepsmentioning

confidence: 99%

“…Each symmetric matrix of cross products is vectorized. The vectors obtained are stored in a new matrix × [46,47]. The matrix × is calculated from the matrix .…”

mentioning

confidence: 99%