Categorización de variables continuas en modelos predictivos

El paquete de R CatPredi permite seleccionar puntos de corte óptimos tanto en un modelo logístico como en un modelo de cox de riesgos proporcionales. Permite categorizar la variable continua en k (a elección del usuario) categorias, considerando un modelo univariante o múltiple.

Investigadores: Irantzu Barrio Beraza (País Vasco)María Xosé Rodríguez Álvarez (Galicia)Inmaculada Arostegui Madariaga (País Vasco)

Análisis de datos funcionales

Análisis de datos funcionales en R: análisis descriptivo, modelos de regresión, clasificación y selección de variables.

Investigadores: Manuel Oviedo de la Fuente (Galicia)Manuel Febrero Bande

Métodos de estimación para curvas ROC

Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.

Investigadores: Mónica López Ratón (Galicia)Carmen María Cadarso Suárez (Galicia)Elisa María Molanes López (Madrid)Emilio Letón Molina (Madrid)

Multivariate analysis with application to biomedicine

ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use.

Investigadores: Concepción Arenas Sola (Catalunya – BIO)Itziar IrigoienBasilio Sierra

Multivariate analysis with application to biomedicine

ORdensity gives the user the list of genes identified as differentially expressed genes in an easy and comprehensible way. The experimentation carried out in an off-the-self computer with the parallel execution enabled shows an improvement in run-time. This implementation may also lead to an important use of memory load. Results previously obtained with simulated and real data indicated that the procedure implemented in the package is robust and suitable for differentially expressed genes identification.

Investigadores: Concepción Arenas Sola (Catalunya – BIO)José María Martínez-OtzetaItziar IrigoienBasilio Sierra

Multivariate regression models

refreg R package is a software implementation of a new statistical model for estimating reference regions conditioned to a set of covariates. This statistical methodology is based on a multivariate location-scale model which provides probabilistic regions covering a specific percentage of the data conditionally on covariates.

Investigadores: Óscar Lado Baleato (Galicia)Javier Roca Pardiñas (Galicia)Carmen María Cadarso Suárez (Galicia)Francisco Gude Sampedro (Galicia)

Multivariate analysis with application to Biology and Biomedicine

The WeDiBaDis package provides a user friendly environment to perform discriminant analysis (supervised classification). WeDiBaDis is an easy to use package addressed to the biological and medical communities, and in general, to researchers interested in applied studies. It can be suitable when the user is interested in the problem of constructing a discriminant rule on the basis of distances between a relatively small number of instances or units of known unbalanced-class membership measured on many (possibly thousands) features of any type. This is a current situation when analyzing genetic biomedical data. This discriminant rule can then be used both, as a means of explaining differences among classes, but also in the important task of assigning the class membership for new unlabeled units. Our package implements two discriminant analysis procedures in an R environment: the well-known distance-based discriminant analysis (DB-discriminant) and a weighteddistance- based discriminant (WDB-discriminant), a novel classifier rule that we introduce.

Investigadores: Concepción Arenas Sola (Catalunya – BIO)Itziar IrigoienFrancisco Mestres