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Artículo

lmdme: Linear Models on Designed Multivariate Experiments in R

Fresno Rodríguez, CristóbalIcon ; Balzarini, Monica GracielaIcon ; Fernandez, Elmer AndresIcon
Fecha de publicación: 04/2014
Editorial: Journal Statistical Software
Revista: Journal Of Statistical Software
ISSN: 1548-7660
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Matemática Pura

Resumen

Thelmdmepackage decomposes analysis of variance (ANOVA) through linear mod-els on designed multivariate experiments, allowing ANOVA-principal component analysis(APCA) and ANOVA-simultaneous component analysis (ASCA) inR. It also extends bothmethods with the application of partial least squares (PLS) through the specification ofa desired output matrix. The package is freely available fromBioconductorand licensedunder the GNU General Public License.ANOVA decomposition methods for designed multivariate experiments are becomingpopular in “omics” experiments (transcriptomics, metabolomics, etc.), where measure-ments are performed according to a predefined experimental design, with several exper-imental factors or including subject-specific clinical covariates, such as those present incurrent clinical genomic studies. ANOVA-PCA and ASCA are well-suited methods forstudying interaction patterns on multidimensional datasets. However, currently anRimplementation of APCA is only available forSpectradata in theChemoSpecpackage,whereas ASCA is based on average calculations on the indices of up to three design ma-trices. Thus, no statistical inference on estimated effects is provided. Moreover, ASCA isnot available in anRpackage.Here, we present anRimplementation for ANOVA decomposition with PCA/PLSanalysis that allows the user to specify (through a flexibleformulainterface), almostany linear model with the associated inference on the estimated effects, as well as todisplay functions to explore results both of PCA and PLS. We describe the model, itsimplementation and two high-throughputmicroarrayexamples: one applied to interactionpattern analysis and the other to quality assessment.
Palabras clave: Linear Model , Anova Descomposition , Pca , Pls , Designed Experiments , R
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/33951
URL: https://www.jstatsoft.org/article/view/v056i07
Colecciones
Articulos(CCT - CORDOBA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - CORDOBA
Citación
Fresno Rodríguez, Cristóbal; Balzarini, Monica Graciela; Fernandez, Elmer Andres; lmdme: Linear Models on Designed Multivariate Experiments in R; Journal Statistical Software; Journal Of Statistical Software; 56; 7; 4-2014; 1-16
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