mpKPlus1STATIS: mpKPlus1STATIS: Function for (K+1) STATIS via MExPosition
In MExPosition: Multi-table ExPosition

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

All (K+1) STATIS steps are combined in this function. It enables preparation of the data, processing and graphing.

mpKPlus1STATIS(data, plus1data, column.design, make.columndesign.nominal = TRUE, 
  row.preprocess = 'None', column.preprocess = 'Center', table.preprocess = 'Sum_PCA', 
  optimization.option = 'STATIS', 
  DESIGN = NULL, make.design.nominal = TRUE, graphs = TRUE)

`data`	Data Matrix
`plus1data`	External table
`column.design`	Column Design for data - used to identifty the tables of the data matrix
`make.columndesign.nominal`	Boolean option. If TRUE (default), table is a vector that indicates groups (and will be dummy-coded). If FALSE, table is a dummy-coded matrix.
`row.preprocess`	String option: 'None' (default), 'Profile', 'Hellinger', 'Center' or 'Center_Hellinger'
`column.preprocess`	String option: 'None', 'Center' (default), '1Norm', 'Center_1Norm' or 'Z_Score'
`table.preprocess`	String option: 'None','Num_Columns','Tucker','Sum_PCA' (default), 'RV_Normalization' or 'MFA_Normalization'
`optimization.option`	String option of either 'None', 'Multiable', 'RV_Matrix', 'STATIS' (DEFAULT), or 'STATIS_Power1'
`DESIGN`	a design matrix to indicate if rows belong to groups.
`make.design.nominal`	Boolean option. If TRUE (default), table is a vector that indicates groups (and will be dummy-coded). If FALSE, table is a dummy-coded matrix.
`graphs`	Boolean option. If TRUE (default), graphs are displayed

Computation of (K+1) STATIS.

Returns a large list of items which are divided into four categories:

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

The results for Overview are bundled inside of $Overview.

`$Overview$data`	Data Matrix
`$Overview$plus1data`	Preprocessed external table
`$Overview$column.design`	Column Design for dataset
`$Overview$row.preprocess`	Row Preprocess Option used
`$Overview$column.preprocess`	Column Preprocess Option used
`$Overview$Table.preprocess`	Table Preprocess Option used
`$Overview$num.groups`	Number of Groups in dataset

The results for InnerProduct are bundled inside of $InnerProduct

`$InnerProduct$S`	Inner Product: Scalar Product Matrices of dataset
`$InnerProduct$S.star`	Inner Product: Scalar Product Matrices * of dataset
`$InnerProduct$rvMatrix.star`	Inner Product: RV Matrix *
`$InnerProduct$C`	Inner Product: C Matrix of S*
`$InnerProduct$ci`	Inner Product: Contribution of the rows of C*
`$InnerProduct$cj`	Inner Product: Contribuition of the columns of C*
`$InnerProduct$eigs`	Inner Product: Eigen Values of C*
`$InnerProduct$eigs.vector`	Inner Product: Eigen Vectors of C*
`$InnerProduct$eigs`	Inner Product: Eigen Value of C*
`$InnerProduct$fi`	Inner Product: Factor Scores of C*
`$InnerProduct$t`	Inner Product: Percent Variance Explained of C*
`$InnerProduct$alphaWeights`	Inner Product: Alpha Weights *

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.

`$Table$m`	Table: masses
`$Table$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: Arrray of Partial Factor Scores
`$Table$ci`	Table: Contribition of the Rows
`$Table$cj`	Table: Contribution of the Columns
`$Table$t`	Table: Percent 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, 124-167

mpKPlus1STATIS, mpSTATIS

  #(K+1) STATIS
  data('wines2012')
	data=wines2012$data
	chemical <- wines2012$supplementary
	design=c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
    
  demo.plus1 <- mpKPlus1STATIS(wines2012$data,chemical,wines2012$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:  Center"
[1] "Preprocessed the Tables of the data matrix using:  Sum_PCA"
[1] "Preprocessing Completed"
[1] "Preprocessed the Rows of the data matrix using:  None"
[1] "Preprocessed the Columns of the data matrix using:  Center"
[1] "Preprocessed the Tables of the data matrix using:  Sum_PCA"
[1] "Processing Complete"
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")