MExPosition (version 2.0.3)

mpSTATIS: mpSTATIS: STATIS via MExPosition

Description

All STATIS steps are combined in this function. It enables preprocessing, processing, optimization and supplementary projections which is computed using the STATIS method of analysis.

Usage

mpSTATIS(data, column.design, make.columndesign.nominal = TRUE,  row.design = NULL, make.rowdesign.nominal = FALSE,  statis.prepro.option = 'Plain_STATIS', DESIGN = NULL,  make.design.nominal = TRUE, graphs = TRUE)

Arguments

data

Matrix of raw data

column.design

Matrix which identifies the different tables.

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.

row.design

Matrix which identifes the different groups.

make.rowdesign.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.

statis.prepro.option

String option for the STATIS presets. The following options are available: 'Plain_STATIS', 'MFA', 'Sum_PCA', 'Plain_Multitable', 'Plain_ANISOSTATIS' and 'Customization.'

graphs

a boolean. If TRUE (default), graphs are displayed

DESIGN

a design matrix to indicate if rows belong to groups.

make.design.nominal

a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, design is a dummy-coded matrix.

Value

Returns a large list of items which are divided into three categories

$Overview

Overview of Results

$InnerProduct

Results for the Inner Product

$Table

Results for the Tables

The results for Overview are bundled inside of $Overview.

$Overview$data

Data Matrix

$Overview$groupmatrix

Table which indicates the tables

$Overview$preprocess.data

Preprocessed Data Matrix

$Overview$num.groups

Number of Groups

$Overview$num.obs

Number of Observations

$Overview$row.preprocess

Option of row preprocessing selected

$Overview$column.preprocess

Option of column preprocessing selected

$Overview$table.preprocess

Option of table preprocessing selected

The results for InnerProduct are bundled inside of $InnerProduct

$InnerProduct$S

Inner Product: Scalar Product Matrices

$InnerProduct$RVMatrix

Inner Product: RV Matrix

$InnerProduct$C

Inner Product: C 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 (tau)

$InnerProduct$alphaWeights

Alpha Weights (alpha)

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.

$Table$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: Array of Partial Factor Scores

$Table$ci

Table: Contribution of the rows

$Tabl$cj

Table: Contribution of the columns

$Table$t

Table: Percent of variance explained

Details

mpSTATIS performs STATIS on a set of data matrices measured on the same set of observations. If statis.prepro.option is set to 'Customization,' the options for row, column, table prepreprocessing and optimization will be selected via the R console.

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. Abdi, H., & Valentin, D. (2007). STATIS. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Sage. pp. 955-962.

See Also

mpDISTATIS

Examples

data('wines2012')
design=c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')
demo.statis.2012 <- mpSTATIS(wines2012$data, column.design = wines2012$table, 
statis.prepro.option = 'Plain_STATIS', DESIGN = design, graphs = TRUE )