LMWiRe: R Routines for Linear Model With Wide Response

In FranceschiniS/LMWiRe: R Routines for Linear Model with Wide Response

Description

This package has for objectives to provide a method to make Linear Model for Wide Response data. This method handles unbalanced design. At this development stage, the package can only perform one or two-way ANOVA of class I. More possibilities should be included in the future. The principal functions of the package are:

LMModelMatrix

Create a model matrix from the design matrix

LMEffectMatrices

Compute an ANOVA-GLM model

PCALMEffects

Perform a PCA on the effect matrices

LMModelMatrix

Create a model matrix from the design matrix

Other functions to observe the results are:

PlotScoresMatrix

Plot multiple scores of different effects in one graph

PlotScoresXY

Plot scores of an effect on two components by graph

PlotLoadings

Plot the loadings

PrintContributions

Print a table with the contribution of each effect's component to the total variance

PlotScree

Plot a barplot of the variance percentage of each component

Details

Package:	LMWiRe
Type:	Package
License:	GPL-2

See the vignette for an example.

Author(s)

Sébastien Franceschini

References

Rousseau, R. (2011). Statistical contribution to the analysis of metabonomics data in 1H NMR spectroscopy (Doctoral dissertation, PhD thesis. Institut de statistique, biostatistique et sciences actuarielles, Université catholique de Louvain, Belgium).

Thiel M.,Feraud B. and Govaerts B. (2017) ASCA+ and APCA+: Extensions of ASCA and APCA in the analysis of unbalanced multifactorial designs, Journal of Chemometrics

Guisset S.,Martin M. and Govaerts B. (2019) Comparison of PARAFASCA, AComDim, and AMOPLS approaches in the multivariate GLM modelling of multi-factorial designs, Chemometrics and Intelligent Laboratory Systems

FranceschiniS/LMWiRe documentation built on Oct. 30, 2019, 6:20 p.m.