mpMultitable: mpMFA: Multitable Analysis via MExPosition
In MExPosition: Multi-table ExPosition

Description Usage Arguments Details Value Author(s) References See Also Examples

Multitable Analysis via MExPosition

1 2	mpMultitable(data, column.design, make.columndesign.nominal = TRUE, DESIGN = NULL, make.design.nominal = TRUE, graphs = TRUE)

`data`	Matrix of raw data
`column.design`	Matrix which identifies the different tables.
`make.columndesign.nominal`	a boolean. If TRUE (default), table is a vector that indicates groups (and will be dummy-coded). If FALSE, table is a dummy-coded matrix.
`graphs`	a boolean. If TRUE (default), graphs are displayed
`DESIGN`	a design matrix to indicate if rows belong to groups.
`make.design.nominal`	a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, design is a dummy-coded matrix.

mpMultitable performs multitable analysis on a set of data matrices.

Returns a large list of items which are divided into three categories

`$Overview`	Overview of Results
`$InnerProduct`	Results for the Inner Product
`$Table`	Results for the Tables

The results for Overview are bundled inside of $Overview.

`$Overview$data`	Data Matrix
`$Overview$groupmatrix`	Table which indicates the tables
`$Overview$preprocess.data`	Preprocessed Data Matrix
`$Overview$num.groups`	Number of Groups
`$Overview$num.obs`	Number of Observations
`$Overview$row.preprocess`	Option of row preprocessing selected
`$Overview$column.preprocess`	Option of column preprocessing selected
`$Overview$table.preprocess`	Option of table preprocessing selected

The results for InnerProduct are bundled inside of $InnerProduct

`$InnerProduct$S`	Inner Product: Scalar Product Matrices
`$InnerProduct$RVMatrix`	Inner Product: RV Matrix
`$InnerProduct$C`	Inner Product: C Matrix
`$InnerProduct$eigs.vector`	Inner Product: Eigen Vectors
`$InnerProduct$eigs`	Inner Product: Eigen Values
`$InnerProduct$fi`	Inner Product: Factor Scores
`$InnerProduct$t`	Inner Product: Percent Variance Explained (tau)
`$InnerProduct$alphaWeights`	Alpha Weights (alpha)

The results for the Compromise are bundled inside of $Compromise

`$Compromise$compromise`	Compromise Matrix
`$Compromise$compromise.eigs`	Compromise: Eigen Values
`$Compromise$compromise.eigs.vector`	Compromise: Eigen Vector
`$Compromise$compromise.fi`	Compromise: Factor Scores
`$Compromise$compromise.t`	Compromise: Percent Variance Explained
`$Compromise$compromise.ci`	Compromise: Contributions of the rows
`$Compromise$compromise.cj`	Compromise: Contributions of the Columns

The results for the Tables are bundled inside of $Table.

`$Table$eigs`	Table: Eigen Values
`$Table$eigs.vector`	Table: Eigen Vectors
`$Table$Q`	Table: Loadings
`$Table$fi`	Table: Factor Scores
`$Table$partial.fi`	Table: Partial Factor Scores
`$Table$partial.fi.array`	Table: Array of Partial Factor Scores
`$Table$ci`	Table: Contribution of the rows
`$Tabl$cj`	Table: Contribution of the columns
`$Table$t`	Table: Percent of variance explained

Cherise R. Chin Fatt and Hervé Abdi.

Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multi-table principal component analysis and three way metric multidimensional scaling. Wiley Interdisciplinary Reviews: Computational Statistics, 4.

Abdi, H., & Valentin, D. (2007). Multiple factor analysis. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Sage. pp. 657-663.

mpDISTATIS

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  #Multitable
  data('wines2007')
  demo.multitable.2007 <- mpMultitable(wines2007$data, wines2007$table)

Loading required package: prettyGraphs
Loading required package: ExPosition
[1] "Preprocessed the Rows of the data matrix using:  None"
[1] "Preprocessed the Columns of the data matrix using:  None"
[1] "Preprocessed the Tables of the data matrix using:  None"
[1] "Preprocessing Completed"
[1] "Optimizing using:  None"
[1] "Processing Complete"
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")