MExPosition (version 2.0.3)

mpANISOSTATIS: mpANISOSTATIS.core: ANISOSTATIS via MExPositio

Description

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

Usage

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

Arguments

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

Value

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

Details

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

References

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.

See Also

mpANISOSTATIS.core

Examples

# 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)