mpCANOSTATIS: mpCANOSTATIS: Canonical STATIS (CANOSTATIS) via MExPosition

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

View source: R/mpCANOSTATIS.R

Description

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

Usage

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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)

Arguments

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

Details

Computation of Canonical STATIS (CANOOSTATIS), where the observations come from predefined groups and tables.

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

$Overview$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

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, 124-167

See Also

mpCANOSTATIS.core, mpCANOSTATIS

Examples

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# 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)

MExPosition documentation built on May 29, 2017, 2:27 p.m.