LMWiRe: Linear Models for Wide Responses
In bgovaerts/LMWiRe: Linear Models for Wide Responses
LMWiRe R Documentation
Linear Models for Wide Responses
Description
This package has for objectives to provide a method to make Linear Models for Wide Responses 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:
lmwModelMatrixCreates a model matrix from the design and the model formula using sum coding
lmwEffectMatricesEstimates the GLM and derives the effect matrix for each model effect
lmwBootstrapTestsTests the significance of the effects using a parametric bootstrap
lmwPcaEffectsPerforms a PCA on the effect matrices and adapts the results according to the method chosen between ASCA, APCA or ASCA-E
The functions allowing the visualisation of the results are:
lmwLoading1dPlot or lmwLoading2dPlotPlots the loadings as a line plot (1D) or in 2D as a scatterplot
lmwScorePlotPlots scores of an effect for two components at a time
lmwScoreScatterPlotMPlots the scores of several effects simultaneously through a scores plot matrix
lmwEffectPlotPlots the ASCA scores of an effect for one Principal Component at a time and allows the scores to be decomposed by the levels of the factors found in the effect being studied.
lmwContributionsProduces plots and tables listing the contribution of the different effects to the total variance as well as the contribution of the Principal Components calculated on each model matrix
lmwScreePlotProduces bar plots of the variance percentage explained by each Principal Component fo a given effect
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
bgovaerts/LMWiRe documentation built on Sept. 17, 2022, 12:32 a.m.
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| LMWiRe | R Documentation |
Linear Models for Wide Responses
Description
This package has for objectives to provide a method to make Linear Models for Wide Responses 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:
lmwModelMatrixCreates a model matrix from the design and the model formula using sum coding
lmwEffectMatricesEstimates the GLM and derives the effect matrix for each model effect
lmwBootstrapTestsTests the significance of the effects using a parametric bootstrap
lmwPcaEffectsPerforms a PCA on the effect matrices and adapts the results according to the method chosen between ASCA, APCA or ASCA-E
The functions allowing the visualisation of the results are:
lmwLoading1dPlotorlmwLoading2dPlotPlots the loadings as a line plot (1D) or in 2D as a scatterplot
lmwScorePlotPlots scores of an effect for two components at a time
lmwScoreScatterPlotMPlots the scores of several effects simultaneously through a scores plot matrix
lmwEffectPlotPlots the ASCA scores of an effect for one Principal Component at a time and allows the scores to be decomposed by the levels of the factors found in the effect being studied.
lmwContributionsProduces plots and tables listing the contribution of the different effects to the total variance as well as the contribution of the Principal Components calculated on each model matrix
lmwScreePlotProduces bar plots of the variance percentage explained by each Principal Component fo a given effect
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
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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