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In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the(More)
Description Exploratory data analysis methods such as principal component methods and clustering. Description The method proposed in this package are exploratory mutlivariate methods such as principal component analysis, correspondence analysis or clustering. AovSum Analysis of variance with the contrasts sum (the sum of the coefficients is 0) Description(More)
This paper combines three exploratory data analysis methods, principal component methods, hierarchical clustering and partitioning, to enrich the description of the data. Principal component methods are used as preprocessing step for the clustering in order to denoise the data, transform categorical data in continuous ones or balanced groups of variables.(More)
Cross-validation is a tried and tested approach to select the number of components in principal component analysis (PCA), however, its main drawback is its computational cost. In a regression (or in a non parametric regression) setting, criteria such as the general cross-validation one (GCV) provide convenient approximations to leave-one-out(More)
The aim of this study is to introduce tools to improve the security of each IMRT patient treatment by determining action levels for the dose delivery process. To achieve this, the patient-specific quality control results performed with an ionization chamber--and which characterize the dose delivery process--have been retrospectively analyzed using a method(More)
A common approach to deal with missing values in multivariate exploratory data analysis consists in minimizing the loss function over all non-missing elements. This can be achieved by EM-type algorithms where an iterative imputation of the missing values is performed during the estimation of the axes and components. This paper proposes such an algorithm ,(More)
BACKGROUND Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to ease the interpretation of microarray data. RESULTS Here, we introduce a data-mining approach, Multiple Factor(More)