mpCANOSTATIS: mpCANOSTATIS: Canonical STATIS (CANOSTATIS) via MExPosition
In MExPosition: Multi-table ExPosition

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

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

mpCANOSTATIS(data, column.design, row.design, normalization = 'MFA', 
row.preprocess = 'None', column.preprocess = 'Center_1Norm', table.preprocess ='Sum_PCA', 
make.columndesign.nominal = TRUE, make.rowdesign.nominal = TRUE, DESIGN = NULL, 
make.design.nominal = TRUE, graphs = TRUE)

`data`	Matrix of data
`column.design`	Column Design- used to identifty the tables of the data matrix
`row.design`	Row Design - used to identify the groups of the data matrix
`normalization`	String option: 'None', 'MFA' (default), or 'Sum_PCA'
`row.preprocess`	String option: 'None' (default), 'Profile', 'Hellinger', 'Center' or 'Center_Hellinger'
`column.preprocess`	String option: 'None', 'Center', '1Norm', 'Center_1Norm' (default) or 'Z_Score'
`table.preprocess`	String option: 'None','Num_Columns','Tucker','Sum_PCA' (default), 'RV_Normalization' or 'MFA_Normalization'
`make.columndesign.nominal`	Boolean option. If TRUE (default), the matrix will be nominalized
`make.rowdesign.nominal`	Boolean option. If TRUE (default), the matrix will be nominalized
`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 Canonical STATIS (CANOOSTATIS), where the observations come from predefined groups and tables.

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
`$Overview$groupmatrix`	Matrix used to identify the different tables of the data matrix
`$Overview$row.design`	Matrix used to identify the groups of the data matrix
`$Overview$preprocess.data`	Preprocessed data matrix
`$Overview$num.groups`	Number of Tables
`$Overview$num.obs`	Number of Observations
`$Overview$row.preprocess`	Row Preprocess Option used
`$Overview$column.preprocess`	Column Preprocess Option used
`$Overview$Table.preprocess`	Table Preprocess Option used

The results for InnerProduct are bundled inside of $InnerProduct

`mahalanobis`	Mahalanobis distance matrices
`$InnerProduct$S`	Inner Product: Scalar Product Matrices
`$InnerProduct$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`	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

mpCANOSTATIS.core, mpCANOSTATIS

# CANOSTATIS
  data('wines2012')
  row.design = c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
	column.design = wines2012$table
	demo.canostatis.2012 <- mpCANOSTATIS(wines2012$data,column.design, row.design, 
  DESIGN = row.design)

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_1Norm"
[1] "Preprocessed the Tables of the data matrix using:  Sum_PCA"
[1] "Preprocessing Completed"
[1] "Processing Completed"
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
[1] "Contributions dimension mismatch. Defaulting to cex only."