mpANISOSTATIS: mpANISOSTATIS.core: ANISOSTATIS via MExPositio
In MExPosition: Multi-table ExPosition

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

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

1 2	mpANISOSTATIS(data, anisostatis.option = 'ANISOSTATIS_Type1', column.design, make.columndesign.nominal = TRUE, DESIGN =NULL, make.design.nominal = TRUE, graphs = TRUE)

`data`	Data Matrix
`anisostatis.option`	ANISOSTATIS string ptions: 'ANISOSTATIS_Type1' or 'ANISOSTATIS_Type2'
`column.design`	Matrix used to identify tables of data matrix
`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.
`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

mpANISOSTATIS computes Anisotropic STATIS, where the one weight is assigned per variable.

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
`$Overview$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

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

The results for the Tables are bundled inside of $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 cherise.chinfatt@utdallas.edu

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.

Abdi, H., Valentin, D., Chollet, S., & Chrea, C. (2007). Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications. Food Quality and Preference, 18, 627-640.

Abdi, H., & Valentin, D. (2005). DISTATIS: the analysis of multiple distance matrices. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 284-290.

mpANISOSTATIS.core

# ANOISTATIS Type 1
  data('wines2012')
	data = wines2012$data
	column.design = wines2012$table
	row.design= c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
	demo.anisostatis1 <- mpANISOSTATIS(data,anisostatis.option='ANISOSTATIS_Type1',
  column.design = column.design)

# ANISOSTATISType 2
  data('wines2012')
	data = wines2012$data
	column.design = wines2012$table
	row.design = c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
	demo.anisostatis2 <- mpANISOSTATIS(data,anisostatis.option='ANISOSTATIS_Type2',
  column.design = column.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] "Optimizing using:  ANISOSTATIS_Type1"
[1] "Processing Complete"
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
[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] "Optimizing using:  ANISOSTATIS_Type2"
[1] "Processing Complete"
dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")
dev.new(): using pdf(file="Rplots7.pdf")