mpSumPCA: Sum PCA via MExPosition

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Description

Sum PCA via MExPosition

Usage

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mpSumPCA(data, column.design, make.columndesign.nominal = TRUE, 
  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.

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.

Details

mpSumPCA performs SumPCA via STATIS on a set of data matrices.

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

Author(s)

Cherise R. Chin Fatt and Hervé Abdi.

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). Multiple factor analysis. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Sage. pp. 657-663.

See Also

mpDISTATIS

Examples

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  #Sum PCA
  data('wines2007')
  demo.sumpca.2007 <- mpSumPCA(wines2007$data, wines2007$table)

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