mpDOACT.STATIS: mpDOACT.STATIS: Function for Dual STATIS (DO-ACT) via...
In MExPosition: Multi-table ExPosition

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

All DO-ACT steps are combined in this function. It enables preparation of the data, processing and graphing.

mpDOACT.STATIS(data1, column.design.1, make.columndesign.1.nominal = TRUE, 
  data2, column.design.2, make.columndesign.2.nominal = TRUE, 
  row.preprocess.data1 = 'None', column.preprocess.data1 = 'Center', 
  table.preprocess.data1 = 'Sum_PCA', 
  row.preprocess.data2 = 'None', column.preprocess.data2 = 'Center', 
  table.preprocess.data2 = 'Sum_PCA', 
  DESIGN = NULL, make.design.nominal = TRUE, 
  graphs = TRUE)

`data1`	Matrix of dataset 1
`column.design.1`	Column Design for dataset 1 - used to identifty the tables of the data matrix
`make.columndesign.1.nominal`	Boolean option. If TRUE (default), the matrix will be nominalized
`data2`	Matrix of dataset 2
`column.design.2`	Column Design for dataset 2 - used to identifty the tables of the data matrix
`make.columndesign.2.nominal`	Boolean option. If TRUE (default), the matrix will be nominalized
`row.preprocess.data1`	String option: 'None' (default), 'Profile', 'Hellinger', 'Center' or 'Center_Hellinger'
`column.preprocess.data1`	String option: 'None', 'Center', '1Norm', 'Center_1Norm' (default) or 'Z_Score'
`table.preprocess.data1`	String option: 'None','Num_Columns','Tucker','Sum_PCA' (default), 'RV_Normalization' or 'MFA_Normalization'
`row.preprocess.data2`	String option: 'None' (default), 'Profile', 'Hellinger', 'Center' or 'Center_Hellinger'
`column.preprocess.data2`	String option: 'None', 'Center', '1Norm', 'Center_1Norm' (default) or 'Z_Score'
`table.preprocess.data2`	String option: 'None','Num_Columns','Tucker','Sum_PCA' (default), 'RV_Normalization' or 'MFA_Normalization'
`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 DualSTATIS (DOSTATIS).

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.

`$Overview$data1`	Data Matrix for dataset 1
`$Overview$column.design.1`	Column Design for dataset1
`$Overview$row.preprocess.data1`	Row Preprocess Option used for dataset1
`$Overview$column.preprocess.data1`	Column Preprocess Option used for dataset1
`$Overview$Table.preprocess.data1`	Table Preprocess Option used for dataset1
`$Overview$num.groups.1`	Number of Groups in dataset1
`$Overview$data2`	Data Matrix for dataset 2
`$Overview$column.design.2`	Column Design for dataset2
`$Overview$row.preprocess.data2`	Row Preprocess Option used for dataset2
`$Overview$column.preprocess.data2`	Column Preprocess Option used for dataset2
`$Overview$Table.preprocess.data2`	Table Preprocess Option used for dataset2
`$Overview$num.groups.2`	Number of Groups in dataset 2

The results for InnerProduct are bundled inside of $InnerProduct

`$InnerProduct$S.1`	Inner Product: Scalar Product Matrices for dataset 1
`$InnerProduct$S.2`	Inner Product: Scalar Product Matrices for dataset 2
`$InnerProduct$C`	Inner Product: C Matrix
`$InnerProduct$RVMatrix`	Inner Product: RV 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
`$InnerProduct$ci`	Inner Product: Contribution of the Rows
`$InnerProduct$cj`	Inner Product: Contribution of the Columns
`$InnerProduct$alphaWeights`	Inner Product: Alpha Weights
`$InnerProduct$betaWeights`	Inner Product: Beta Weights

The results for the Compromise are bundled inside of $Compromise

`$Compromise$compromiseMatrix.1`	Compromise Matrix for dataset 1
`$Compromise$compromise.eigs.1`	Compromise: Eigen Values for dataset 1
`$Compromise$compromise.eigs.vector.1`	Compromise: Eigen Vector for dataset 1
`$Compromise$compromise.fi.1`	Compromise: Factor Scores for dataset 1
`$Compromise$compromise.t.1`	Compromise: Percent Variance Explained for dataset 1
`$Compromise$compromise.ci.1`	Compromise: Contributions of the rows for dataset 1
`$Compromise$compromise.cj.1`	Compromise: Contributions of the Columns for dataset 1
`$Compromise$compromiseMatrix.2`	Compromise Matrix for dataset 2
`$Compromise$compromise.eigs.2`	Compromise: Eigen Values for dataset 2
`$Compromise$compromise.eigs.vector.2`	Compromise: Eigen Vector for dataset 2
`$Compromise$compromise.fi.2`	Compromise: Factor Scores for dataset 2
`$Compromise$compromise.t.2`	Compromise: Percent Variance Explained for dataset 2
`$Compromise$compromise.ci.2`	Compromise: Contributions of the rows for dataset 2
`$Compromise$compromise.cj.2`	Compromise: Contributions of the Columns for dataset 2

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

`$Table$m.1`	Table: masses for dataset 1
`$Table$eigs.1`	Table: Eigen Values for dataset 1
`$Table$eigs.vector.1`	Table: Eigen Vectors for dataset 1
`$Table$Q.1`	Table: Loadings for dataset 1
`$Table$fi.1`	Table: Factor Scores for dataset 1
`$Table$partial.fi.1`	Table: Partial Factor Scores for dataset 1
`$Table$partial.fi.array.1`	Table: Arrray of Partial Factor Scores for dataset 1
`$Table$ci.1`	Table: Contribition of the Rows for dataset 1
`$Table$cj.1`	Table: Contribution of the Columns for dataset 1
`$Table$t.1`	Table: Percent Variance Explained for dataset 1
`$Table$m.2`	Table: masses for dataset 2
`$Table$eigs.2`	Table: Eigen Values for dataset 2
`$Table$eigs.vector.2`	Table: Eigen Vectors for dataset 2
`$Table$Q.2`	Table: Loadings for dataset 2
`$Table$fi.2`	Table: Factor Scores for dataset 2
`$Table$partial.fi.2`	Table: Partial Factor Scores for dataset 2
`$Table$partial.fi.array.2`	Table: Arrray of Partial Factor Scores for dataset 2
`$Table$ci.2`	Table: Contribition of the Rows for dataset 2
`$Table$cj.2`	Table: Contribution of the Columns for dataset 2
`$Table$t.2`	Table: Percent Variance Explained for dataset 2

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

mpSTATIS, mpDOACT.STATIS

   #DO-ACT
   data('wines2012')
   design=c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
   data1 <- wines2012$data
   data2 <- wines2012$data
   design.1 <- wines2012$table
   design.2 <- wines2012$table

   demo.double <- mpDOACT.STATIS(data1=data1,column.design.1=design.1, data2=data2, 
   column.design.2=design.2, DESIGN=design)