Plots the ASCA scores with projected data for a selected factor or interaction.
● Data Source:
CranContrib
● Keywords: ASCA, PCA
● Alias: ASCA.PlotScoresPerLevel
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This function will write the input matrix to a file, always including row names and column names. If no row or column names are present in the input matrix, default names will be generated. In addition, the first data position in the output file is reserved for a description.
● Data Source:
CranContrib
● Keywords: File, Matrix
● Alias: MetStaT.CreateFileFromHeaderRowMatrix
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This is a verbatim copy of the 'mldivide' function contained in the 'pracma' package by Hans W. Borchers (under GPL>=3 license), as found on CRAN on November 27, 2012. Together with the 'mrdivide' method, these are the only two 'pracma' package methods used in the 'MetStaT' package, which is why they were copied instead of depending on the entire 'pracma' package. For details on the mldivide method, please see the help file in the 'pracma' package.
● Data Source:
CranContrib
● Keywords:
● Alias: MetStaT.mldivide
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This is the last step in obtaining a figure of merit. The binned data is fitted by iterating over all alpha values. For each alpha value the best additive fit left of the alpha value and the best multiplicative fit right of the alpha value is determined. The alpha value which then produces the best overall fit (smallest residual) is chosen for the overall fit.
● Data Source:
CranContrib
● Keywords: Metabolomics
● Alias: FoM.FitBinnedSampleRepeatErrors
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This function will accept metabolomics sample data for a single metabolite and includes sample identification. It will bin corresponding samples by intensity and calculate a measure for each bin's error. The intensity per bin versus this measure of error will be plotted, and a best fit will be found and plotted that consists of a additive and a multiplicative part. For more details see the references.
● Data Source:
CranContrib
● Keywords: Figures of Merit, Metabolomics
● Alias: FoM.Calculate
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Supplies a list that contains the power set of the input set, i.e. all the possible subsets of the input set.
● Data Source:
CranContrib
● Keywords:
● Alias: ASCA.GetPowerSet
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The example contains data for a two factor experimental design. The first factor has two treatment levels, the second has three treatment levels. The design is balanced with 10 observations per factor/treatment level.
● Data Source:
CranContrib
● Keywords: ASCA
● Alias: ASCAdata
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Performs a permutation test for the QStat.Calculate outcome, which is by default already done by that method.
● Data Source:
CranContrib
● Keywords:
● Alias: QStat.PermutationTest
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ASCA.Calculate
(Package: MetStaT) :
ASCA method (ANOVA-simultaneous component analysis)
ASCA does PCA on the averages of the treatment levels for an experimental design.
● Data Source:
CranContrib
● Keywords: ASCA, PCA
● Alias: ASCA.Calculate
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Supplied with the total number of principal components of interest, this method will return a list of each possible pairing between two principal components.
● Data Source:
CranContrib
● Keywords: File, Matrix
● Alias: MetStaT.GetPcTuples
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providing 
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