Package ‘MExPosition’ - The Comprehensive R … ‘MExPosition’ February 19, 2015 Type Package Title Multi-table ExPosition Version 2.0.3 Date 2013-06-10 Author Cherise R. Chin

Package ‘MExPosition’February 19, 2015

Type Package

Title Multi-table ExPosition

Version 2.0.3

Date 2013-06-10

Author Cherise R. Chin Fatt, Derek Beaton, Herve Abdi.

Maintainer Cherise R. Chin Fatt <[email protected]>

Description MExPosition is for descriptive (i.e., fixed-effects)multi-table multivariate analysis the singular valuedecomposition.

License GPL-2

Depends prettyGraphs (>= 2.0.0), ExPosition (>= 2.0.0)

NeedsCompilation no

Repository CRAN

Date/Publication 2013-06-15 18:39:41

R topics documented:MExPosition-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3mpANISOSTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4mpANISOSTATIS.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7mpCANOSTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9mpCANOSTATIS.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12mpCOVSTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14mpCOVSTATIS.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17mpDISTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19mpDISTATIS.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22mpDOACT.STATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24mpDOACT.STATIS.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28mpGraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31mpKPlus1STATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32mpKPlus1STATIS.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35mpMahalanobis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

1

2 R topics documented:

mpMFA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38mpMultitable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40mpPTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43mpPTA.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46mpSTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48mpSTATIS.columnPreproc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51mpSTATIS.core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52mpSTATIS.optimize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54mpSTATIS.preprocess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55mpSTATIS.rowPreproc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56mpSTATIS.tablePreproc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57mpSumPCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59mpTableCheck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61print.covstatis.compromise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62print.covstatis.innerproduct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63print.covstatis.overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63print.covstatis.table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64print.distatis.compromise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64print.distatis.innerproduct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65print.distatis.overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65print.distatis.table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66print.doact.statis.compromise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66print.doact.statis.innerproduct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67print.doact.statis.overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67print.doact.statis.table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68print.KPlus1.statis.compromise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68print.KPlus1.statis.innerproduct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69print.KPlus1.statis.overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69print.KPlus1.statis.table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70print.mexPosition.Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70print.mpANISOSTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71print.mpCOVSTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71print.mpDISTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72print.mpDOACT.STATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72print.mpGraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73print.mpKPlus1STATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73print.mpMFA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74print.mpSTATIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74print.statis.compromise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75print.statis.innerproduct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75print.statis.overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76print.statis.table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Index 77

MExPosition-package 3

MExPosition-package Multi-table Exploratory Analysis with the Singular Value De-comPosition with the STATIS family.

Description

MExPosition is multi-table ExPosition and includes the family of STATIS method, such as PlainSTATIS, DISTATIS, Dual STATIS and ANISOSTATIS. The core of MExPosition is ExPositionand the svd.

Details

Package: MExPositionType: PackageVersion: 2.0.3Date: 2013-06-10Depends: R (>=2.15.0), prettyGraphs (>= 2.0.0), ExPosition (>= 2.0.0)License: GPL-2

Author(s)

Questions, comments, compliments, and complaints go to Cherise R. Chin Fatt <[email protected]>.

The following people are contributors to MExPosition code, data, or examples:Derek Beaton and Hervé Abdi.

References

Abdi, H., Valentin, D., Chollet, S., & Chrea, C. (2007). Analyzing assessors and products in sortingtasks: 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.

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 Measurementand Statistics. Sage. pp. 955-962.

4 mpANISOSTATIS

See Also

mpSTATIS, mpDISTATIS

Examples

#For more examples, see each individual function (as noted above).

mpANISOSTATIS mpANISOSTATIS.core: ANISOSTATIS via MExPositio

Description

All ANISOSTATIS steps are combined in this function. It enables preparation of the data, process-ing 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 Matrixanisostatis.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 bedummy-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 (andwill be dummy-coded). If FALSE, table is a dummy-coded matrix.

graphs Boolean option. If TRUE (default), graphs are displayed

Details

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

mpANISOSTATIS 5

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

6 mpANISOSTATIS

$InnerProduct$alphaWeights

Alpha Weights

The results for the Compromise are bundled inside of $Compromise

compromise Compromise Matrixcompromise.eigs

Compromise: Eigen Valuescompromise.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

Author(s)

Cherise R. Chin Fatt <[email protected]>

References

Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Op-timum multi-table principal component analysis and three way metric multidimensional scaling.Wiley Interdisciplinary Reviews: Computational Statistics, 4, 124-167.



mpANISOSTATIS.core 7

See Also

mpANISOSTATIS.core

Examples

# ANOISTATIS Type 1data('wines2012')

data = wines2012$datacolumn.design = wines2012$tablerow.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 2data('wines2012')

data = wines2012$datacolumn.design = wines2012$tablerow.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)

mpANISOSTATIS.core mpANISOSTATIS.core: Core Function for ANISOSTATIS via MExPo-sition

Description

Performs the core of ANISOSTATIS on the data

Usage

mpANISOSTATIS.core(data, num.obs, column.design, num.groups,optimization.option='ANISOSTATIS_Type1')

Arguments

data Matrix of preprocessed data

num.obs Number of observations

column.design Table Matrix- used to identifty the tables of the data matrix

num.groups Number of groupsoptimization.option

String option of either ’ANISOSTATIS_Type1’ (DEFAULT), or ’ANISOSTATIS_Type2’

Details

Computation of Anisotropic STATIS (ANISOSTATIS), where the one weight is assigned per vari-able.

8 mpANISOSTATIS.core

Value

S Inner Product: Scalar Product Matrices

RVMatrix Inner Product: RV Matrix

C Inner Product: C Matrix

ci Inner Product: Contribution of the rows of C

cj Inner Product: Contribuition of the columns of C

eigs Inner Product: Eigen Values of C

eigs.vector Inner Product: Eigen Vectors of S

eigenValue Inner Product: Eigen Value

fi Inner Product: Factor Scores

tau Inner Product: Percent Variance Explained

alphaWeights Inner Product: Alpha Weights





Compromise.tau Compromise: Percent Variance Explained



masses Table: masses

table.eigs Table: Eigen Valuestable.eigs.vector


table.loadings Table: Loadings

table.fi Table: Factor Scorestable.partial.fi

Table: Partial Factor Scorestable.partial.fi.array

Table: Array of Partial Factor Scores

table.tau 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

mpCANOSTATIS 9

See Also

mpDISTATIS, mpSTATIS, mpANISOSTATIS

mpCANOSTATIS mpCANOSTATIS: Canonical STATIS (CANOSTATIS) via MExPosi-tion

Description

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

Usage

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’


Boolean option. If TRUE (default), the matrix will be nominalizedmake.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



Details

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

10 mpCANOSTATIS

Value








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




Table Preprocess Option used


mahalanobis Mahalanobis distance matrices$InnerProduct$S








Inner Product: Contribution of the Rows

mpCANOSTATIS 11

$InnerProduct$cj

Inner Product: Contribution of the Columns$InnerProduct$alphaWeights

Alpha Weights


$Compromise$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


$Table$m Table: masses







$Table$ci Table: Contribition of the Rows



Author(s)


References


12 mpCANOSTATIS.core

See Also

mpCANOSTATIS.core, mpCANOSTATIS

Examples

# CANOSTATISdata('wines2012')row.design = c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')

column.design = wines2012$tabledemo.canostatis.2012 <- mpCANOSTATIS(wines2012$data,column.design, row.design,

DESIGN = row.design)

mpCANOSTATIS.core mpCANOSTATIS.core: Core Function for Canonical STATIS (CANO-STATIS) via MExPosition

Description

Performs the core of CANOSTATIS on the given dataset

Usage

mpCANOSTATIS.core(data, num.obs = num.obs, column.design, row.design,num.groups = num.groups, normalization = 'MFA', masses = NULL)

Arguments





num.groups Number of groups

normalization String option of either ’None’, ’MFA’ (DEFAULT), or ’Sum_PCA’

masses Masses

Details

Computation of Canonical STATIS (CANOSTATIS), where the observations come from predefinedgroups and tables.

mpCANOSTATIS.core 13

Value

mahalanobis Mahalanobis distance matrices

normalization Inner Product: Normalization option selected




rvMatrix Inner Product: RV Matrix




















table.Q Table: Loadings





Author(s)


14 mpCOVSTATIS

References


See Also

mpDISTATIS, mpSTATIS, mpCANOSTATIS

mpCOVSTATIS mpCOVSTATIS: Core Function for COVSTATIS via MExPosition

Description

All COVSTATIS steps are combined in this function. It enables preparation of the data, processingand graphing.

Usage

mpCOVSTATIS(data, normalization = 'None', masses = NULL, table = NULL,make.table.nominal = TRUE, DESIGN = NULL, make.design.nominal = TRUE, graphs = TRUE)

Arguments



masses Masses

table Design Matrix - used to identifty the tables of the data matrixmake.table.nominal

a boolean. If TRUE (default), table is a vector that indicates tables (and will bedummy-coded). If FALSE, table is a dummy-coded matrix.




Details

COVSTATIS is used to analysis covariance matrices. It is an extension of three-way multidimen-sional scaling.

mpCOVSTATIS 15

Value







$Overview$data Data Matrix$Overview$normalization

Type of normalization used$Overview$table

Matrix used to identify the different tables of the data matrix$Overview$num.groups

Number of Tables


$InnerProduct$S


Inner Product: C Matrix$InnerProduct$rvMatrix








Alpha Weights





16 mpCOVSTATIS


Compromise.t Compromise: Percent Variance Explained




$m Table: masses







Table$ci Table: Contribition of the Rows



Author(s)


References


See Also

mpCANOSTATIS

Examples

#COVSTATISdata('faces2005')table = c('pixel','pixel','pixel','pixel','pixel','pixel','distance','distance','distance','distance','distance','distance','ratings','ratings','ratings','ratings','ratings','ratings','similarity','similarity','similarity','similarity','similarity','similarity')demo.covstatis.2005 <- mpCOVSTATIS(faces2005$data, table = table)

mpCOVSTATIS.core 17

mpCOVSTATIS.core mpCOVSTATIS.core: Core Function for COVSTATIS via MExPosition

Description

Performs the core of CANOSTATIS on the given dataset

Usage

mpCOVSTATIS.core(data, normalization = 'None', masses = NULL,table = NULL, make.table.nominal = TRUE)

Arguments



masses Masses

table Design Matrix - used to identifty the tables of the data matrixmake.table.nominal

a boolean. If TRUE (default), table is a vector that indicates tables (and will bedummy-coded). If FALSE, table is a dummy-coded matrix.

Details

COVSTATIS is used to analysis covariance matrices. It is an extension of three-way multidimen-sional scaling.

Value

data Data matrix

normalization Inner Product: Normalization option selected

table Design matrix used to identifty the tables of the data matrix


rvMatrix Inner Product: RV Matrix









18 mpCOVSTATIS.core


compromise Compromise Matrix

compromise.eigs

Compromise: Eigen Values

compromise.eigs.vector







table.eigs Table: Eigen Values

table.eigs.vector



table.fi Table: Factor Scores

table.partial.fi

Table: Partial Factor Scores

table.partial.fi.array



Author(s)


References


See Also

mpCANOSTATIS

mpDISTATIS 19

mpDISTATIS mpDISTATIS: DISTATIS via MExPosition

Description

All DISTATIS steps are combined in this function. It enables preparation of the data, processingand graphing.

Usage

mpDISTATIS(data, sorting = 'No', normalization = 'None', masses = 'NULL',table=NULL, make.table.nominal = TRUE, DESIGN = NULL, make.design.nominal = TRUE,graphs = TRUE)

Arguments

data Data Matrix

sorting a boolean. If YES, DISTATIS will by processed as a sorting task. Default is NO

normalization Normaliztion string option: ’None’ (default), ’Sum_PCA’, or ’MFA’

table Table which identifies the different tables.make.table.nominal


masses Masses: if NULL, 1/num.obs would be set by default. For customized masses,enter the matrix of customized masses

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


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

Details

mpDISTATIS performs DISTATIS on a set of data matrices measured on the same set of observations.

Value

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






20 mpDISTATIS

$Overview$data Data Matrix$Overview$normalization

Type of Normalization used.$Overview$sorting

Indicates if the task is a sorting task$Overview$table

Table which indicates the tables

$num.groups Number of groups


$InnerProduct$S

Inner Product: Scalar Product Matrices

$norm.S Normalized Scalar Product Matrices$InnerProduct$C

Inner Product: C Matrix$InnerProduct$eigs.vector




Inner Product: Percent Variance Explained (tau)$InnerProduct$alphaWeights

Alpha Weights











$Table$m Table: Masses

$Table$eigs Table: Eigen Values

mpDISTATIS 21

$Table$eigs.vector






$Table$cj Table:Contribution for the rows

$Table$cj Table: Contribution for the columns

$Table$t Table:Percent Variance Explained

Author(s)


References




See Also

mpSTATIS

Examples

data('faces2005')table = c('pixel','pixel','pixel','pixel','pixel','pixel','distance','distance','distance','distance','distance','distance','ratings','ratings','ratings','ratings','ratings','ratings','similarity','similarity','similarity','similarity','similarity','similarity')face.data <- faces2005$datademo.distatis <- mpDISTATIS(face.data, sorting = 'No', normalization = 'MFA', table = table)

22 mpDISTATIS.core

mpDISTATIS.core mpDISTATIS.core

Description

mpDISTATIS.core performs the core functions of DISTATIS.

Usage

mpDISTATIS.core(data, table, sorting = 'No', normalization = 'None',masses = NULL, make.table.nominal=TRUE)

Arguments


table Table which identifies the different tables.

sorting a boolean. If YES, DISTATIS will by processed as a sorting task. Default is NO

normalization Normaliztion string option: ’None’ (default), ’Sum_PCA’, or ’MFA’

masses Masses: if NULL, 1/num.obs would be set by default. For customized masses,enter the vector of customized masses

make.table.nominal


Details

This function should not be used directly. Please use mpDISTATIS

Value

Returns a large list of items which are also returned in mpDISTATIS.

data Data Matrix

table Design Matrix

normalization Type of Normalization used.

sorting Indicates if the task is a sorting task





eigs.vector Inner Product: Eigen Vectors

eigs Inner Product: Eigen Values


mpDISTATIS.core 23


alphaWeights Alpha Weights
















Author(s)


References

Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Op-timum multi-table principal component analysis and three way metric multidimensional scaling.Wiley Interdisciplinary Reviews: Computational Statistics, 4, 124-167



See Also

mpSTATIS, mpSTATIS.core, mpDISTATIS

24 mpDOACT.STATIS

mpDOACT.STATIS mpDOACT.STATIS: Function for Dual STATIS (DO-ACT) via MExPo-sition

Description

All DO-ACT steps are combined in this function. It enables preparation of the data, processing andgraphing.

Usage

mpDOACT.STATIS(data1, column.design.1, make.columndesign.1.nominal = TRUE,data2, column.design.2, make.columndesign.2.nominal = TRUE,row.preprocess.data1 = 'None', column.preprocess.data1 = 'Center',table.preprocess.data1 = 'Sum_PCA',row.preprocess.data2 = 'None', column.preprocess.data2 = 'Center',table.preprocess.data2 = 'Sum_PCA',DESIGN = NULL, make.design.nominal = TRUE,graphs = TRUE)

Arguments

data1 Matrix of dataset 1column.design.1

Column Design for dataset 1 - used to identifty the tables of the data matrixmake.columndesign.1.nominal

Boolean option. If TRUE (default), the matrix will be nominalized

data2 Matrix of dataset 2column.design.2

Column Design for dataset 2 - used to identifty the tables of the data matrixmake.columndesign.2.nominal

Boolean option. If TRUE (default), the matrix will be nominalizedrow.preprocess.data1

String option: ’None’ (default), ’Profile’, ’Hellinger’, ’Center’ or ’Center_Hellinger’column.preprocess.data1

String option: ’None’, ’Center’, ’1Norm’, ’Center_1Norm’ (default) or ’Z_Score’table.preprocess.data1


row.preprocess.data2

String option: ’None’ (default), ’Profile’, ’Hellinger’, ’Center’ or ’Center_Hellinger’column.preprocess.data2

String option: ’None’, ’Center’, ’1Norm’, ’Center_1Norm’ (default) or ’Z_Score’table.preprocess.data2


mpDOACT.STATIS 25




Details

Computation of DualSTATIS (DOSTATIS).

Value







$Overview$data1

Data Matrix for dataset 1$Overview$column.design.1

Column Design for dataset1$Overview$row.preprocess.data1

Row Preprocess Option used for dataset1$Overview$column.preprocess.data1

Column Preprocess Option used for dataset1$Overview$Table.preprocess.data1

Table Preprocess Option used for dataset1$Overview$num.groups.1

Number of Groups in dataset1$Overview$data2

Data Matrix for dataset 2$Overview$column.design.2

Column Design for dataset2$Overview$row.preprocess.data2

Row Preprocess Option used for dataset2$Overview$column.preprocess.data2

Column Preprocess Option used for dataset2$Overview$Table.preprocess.data2

Table Preprocess Option used for dataset2$Overview$num.groups.2

Number of Groups in dataset 2


26 mpDOACT.STATIS

$InnerProduct$S.1

Inner Product: Scalar Product Matrices for dataset 1$InnerProduct$S.2

Inner Product: Scalar Product Matrices for dataset 2$InnerProduct$C









Inner Product: Alpha Weights$InnerProduct$betaWeights

Inner Product: Beta Weights


$Compromise$compromiseMatrix.1

Compromise Matrix for dataset 1$Compromise$compromise.eigs.1

Compromise: Eigen Values for dataset 1$Compromise$compromise.eigs.vector.1

Compromise: Eigen Vector for dataset 1$Compromise$compromise.fi.1

Compromise: Factor Scores for dataset 1$Compromise$compromise.t.1

Compromise: Percent Variance Explained for dataset 1$Compromise$compromise.ci.1

Compromise: Contributions of the rows for dataset 1$Compromise$compromise.cj.1

Compromise: Contributions of the Columns for dataset 1$Compromise$compromiseMatrix.2

Compromise Matrix for dataset 2$Compromise$compromise.eigs.2

Compromise: Eigen Values for dataset 2$Compromise$compromise.eigs.vector.2

Compromise: Eigen Vector for dataset 2

mpDOACT.STATIS 27

$Compromise$compromise.fi.2

Compromise: Factor Scores for dataset 2$Compromise$compromise.t.2

Compromise: Percent Variance Explained for dataset 2$Compromise$compromise.ci.2

Compromise: Contributions of the rows for dataset 2$Compromise$compromise.cj.2

Compromise: Contributions of the Columns for dataset 2


$Table$m.1 Table: masses for dataset 1

$Table$eigs.1 Table: Eigen Values for dataset 1$Table$eigs.vector.1

Table: Eigen Vectors for dataset 1

$Table$Q.1 Table: Loadings for dataset 1

$Table$fi.1 Table: Factor Scores for dataset 1$Table$partial.fi.1

Table: Partial Factor Scores for dataset 1$Table$partial.fi.array.1

Table: Arrray of Partial Factor Scores for dataset 1

$Table$ci.1 Table: Contribition of the Rows for dataset 1

$Table$cj.1 Table: Contribution of the Columns for dataset 1

$Table$t.1 Table: Percent Variance Explained for dataset 1

$Table$m.2 Table: masses for dataset 2

$Table$eigs.2 Table: Eigen Values for dataset 2$Table$eigs.vector.2


$Table$Q.2 Table: Loadings for dataset 2

$Table$fi.2 Table: Factor Scores for dataset 2$Table$partial.fi.2

Table: Partial Factor Scores for dataset 2$Table$partial.fi.array.2

Table: Arrray of Partial Factor Scores for dataset 2

$Table$ci.2 Table: Contribition of the Rows for dataset 2

$Table$cj.2 Table: Contribution of the Columns for dataset 2

$Table$t.2 Table: Percent Variance Explained for dataset 2

Author(s)


28 mpDOACT.STATIS.core

References


See Also

mpSTATIS, mpDOACT.STATIS

Examples

#DO-ACTdata('wines2012')design=c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')data1 <- wines2012$datadata2 <- wines2012$datadesign.1 <- wines2012$tabledesign.2 <- wines2012$table

demo.double <- mpDOACT.STATIS(data1=data1,column.design.1=design.1, data2=data2,column.design.2=design.2, DESIGN=design)

mpDOACT.STATIS.core mpDOACT.STATIS.core: Core Function for Dual STATIS (DO-ACT)via MExPosition

Description

Performs the core of Dual STATIS on two given dataset

Usage

mpDOACT.STATIS.core(dataset1, column.design.1, dataset2, column.design.2)

Arguments

dataset1 Matrix of dataset 1column.design.1

Column Design for dataset 1 - used to identifty the tables of the data matrix

dataset2 Matrix of dataset 2column.design.2

Column Design for dataset 2 - used to identifty the tables of the data matrix

Details

Computation of DualSTATIS (DOSTATIS). This function should not be used independently. Itshould be used with mpDOACT.STATIS

mpDOACT.STATIS.core 29

Value

S.1 Inner Product: Scalar Product Matrices of dataset1

S.2 Inner Product: Scalar Product Matrices of dataset2










betaWeights Inner Product: Beta WeightscompromiseMatrix.1

Compromise Matrix for dataset 1compromise.eigs.1

Compromise: Eigen Values for dataset 1compromise.eigs.vector.1

Compromise: Eigen Vector for dataset 1compromise.fi.1

Compromise: Factor Scores for dataset 1Compromise.tau.1

Compromise: Percent Variance Explained for dataset 1compromise.ci.1

Compromise: Contributions of the rows for dataset 1compromise.cj.1

Compromise: Contributions of the Columns for dataset 1compromiseMatrix.2

Compromise Matrix for dataset 2compromise.eigs.2

Compromise: Eigen Values for dataset 2compromise.eigs.vector.2

Compromise: Eigen Vector for dataset 2compromise.fi.2

Compromise: Factor Scores for dataset 2Compromise.tau.2

Compromise: Percent Variance Explained for dataset 2compromise.ci.2

Compromise: Contributions of the rows for dataset 2compromise.cj.2

Compromise: Contributions of the Columns for dataset 2

30 mpDOACT.STATIS.core

masses.1 Table: masses for dataset 1

table.eigs.1 Table: Eigen Values for dataset 1

table.eigs.vector.1


table.loadings.1

Table: Loadings for dataset 1

table.fi.1 Table: Factor Scores for dataset 1table.partial.fi.1

Table: Partial Factor Scores for dataset 1table.partial.fi.array.1

Table: Array of Partial Factor Scores for dataset 1

table.tau.1 Table: Percent Variance Explained for dataset 1

masses.2 Table: masses for dataset 2

table.eigs.2 Table: Eigen Values for dataset 2

table.eigs.vector.2


table.loadings.2

Table: Loadings for dataset 2

table.fi.2 Table: Factor Scores for dataset 2table.partial.fi.2

Table: Partial Factor Scores for dataset 2table.partial.fi.array.2

Table: Array of Partial Factor Scores for dataset 2

table.tau.2 Table: Percent Variance Explained for dataset 2

Author(s)


References


See Also

mpSTATIS, mpDOACT.STATIS

mpGraphs 31

mpGraphs mpGraphs: MExPosition plotting function

Description

MExPosition plotting function which is an interface to prettyGraphs.

Usage

mpGraphs(res, table, DESIGN = NULL, x_axis = 1, y_axis = 2,fi.col = NULL, fj.col = NULL, table.col = NULL, col.offset = NULL,constraints = NULL, xlab = NULL, ylab = NULL, main = NULL, graphs = TRUE)

Arguments

res results from MExPosition (i.e., $mexPosition.Data)

table results from mpGraphs (i.e., $Plotting.Data)

DESIGN Design Matrix which differentiates the tables

x_axis which component should be on the x axis?

y_axis which component should be on the y axis?

fi.col Colors for the rows

fj.col Colors for the columns

table.col Colors for the tables

col.offset Color Offset

constraints Plotting Constraints

xlab x axis label

ylab y axis label

main main label for the graph window

graphs Boolean option. If TRUE (default), graphs will be plotted else, there will begraphical output

Details

mpGraphs is an interface between MExPosition and prettyGraphs.

Value

The following items are bundled inside of $Plotting.Data:

$fi.col the colors that are associated to the groups.

$fj.col the colors that are associated to the column items.

32 mpKPlus1STATIS

Author(s)

Cherise R. Chin Fatt and Derek Beaton

See Also

prettyGraphs

mpKPlus1STATIS mpKPlus1STATIS: Function for (K+1) STATIS via MExPosition

Description

All (K+1) STATIS steps are combined in this function. It enables preparation of the data, processingand graphing.

Usage

mpKPlus1STATIS(data, plus1data, column.design, make.columndesign.nominal = TRUE,row.preprocess = 'None', column.preprocess = 'Center', table.preprocess = 'Sum_PCA',optimization.option = 'STATIS',DESIGN = NULL, make.design.nominal = TRUE, graphs = TRUE)

Arguments

data Data Matrix

plus1data External table

column.design Column Design for data - used to identifty the tables of the data matrixmake.columndesign.nominal


row.preprocess String option: ’None’ (default), ’Profile’, ’Hellinger’, ’Center’ or ’Center_Hellinger’column.preprocess

String option: ’None’, ’Center’ (default), ’1Norm’, ’Center_1Norm’ or ’Z_Score’table.preprocess


optimization.option

String option of either ’None’, ’Multiable’, ’RV_Matrix’, ’STATIS’ (DEFAULT),or ’STATIS_Power1’




mpKPlus1STATIS 33

Details

Computation of (K+1) STATIS.

Value







$Overview$data Data Matrix$Overview$plus1data

Preprocessed external table$Overview$column.design

Column Design for dataset$Overview$row.preprocess



Table Preprocess Option used$Overview$num.groups

Number of Groups in dataset


$InnerProduct$S

Inner Product: Scalar Product Matrices of dataset$InnerProduct$S.star

Inner Product: Scalar Product Matrices * of dataset$InnerProduct$rvMatrix.star

Inner Product: RV Matrix *$InnerProduct$C

Inner Product: C Matrix of S*$InnerProduct$ci

Inner Product: Contribution of the rows of C*$InnerProduct$cj

Inner Product: Contribuition of the columns of C*$InnerProduct$eigs

Inner Product: Eigen Values of C*$InnerProduct$eigs.vector

Inner Product: Eigen Vectors of C*$InnerProduct$eigs

Inner Product: Eigen Value of C*

34 mpKPlus1STATIS

$InnerProduct$fi

Inner Product: Factor Scores of C*$InnerProduct$t

Inner Product: Percent Variance Explained of C*$InnerProduct$alphaWeights

Inner Product: Alpha Weights *











$Table$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: 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)


References


mpKPlus1STATIS.core 35

See Also

mpKPlus1STATIS, mpSTATIS

Examples

#(K+1) STATISdata('wines2012')

data=wines2012$datachemical <- wines2012$supplementarydesign=c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')

demo.plus1 <- mpKPlus1STATIS(wines2012$data,chemical,wines2012$table)

mpKPlus1STATIS.core mpKPlus1STATIS.core: Core Function for (K+1) STATIS via MExPo-sition

Description

Performs the core of (K+1) STATIS

Usage

mpKPlus1STATIS.core(data, plus1data, num.obs, column.design, num.groups,optimization.option = 'STATIS')

Arguments


plus1data Matrix of preprocessed external table


column.design Column Design for data - used to identifty the tables of the data matrix

num.groups Number of groups

optimization.option


Details

Computation of (K+1) STATIS. This function should not be used independently. It should be usedwith mpKPlus1STATIS

36 mpKPlus1STATIS.core

Value

S Inner Product: Scalar Product Matrices of datasetS.star Inner Product: Scalar Product Matrices * of datasetrvMatrix.star Inner Product: RV Matrix *C Inner Product: C Matrix of S*ci Inner Product: Contribution of the rows of C*cj Inner Product: Contribuition of the columns of C*eigs Inner Product: Eigen Values of C*eigs.vector Inner Product: Eigen Vectors of C*eigenValue Inner Product: Eigen Value of C*fi Inner Product: Factor Scores of C*tau Inner Product: Percent Variance Explained of C*alphaWeights Inner Product: Alpha Weights *compromise Compromise Matrixcompromise.eigs


Compromise: Eigen Vectorcompromise.fi Compromise: Factor ScoresCompromise.tau Compromise: Percent Variance Explainedcompromise.ci Compromise: Contributions of the rowscompromise.cj Compromise: Contributions of the Columnsmasses Table: massestable.eigs Table: Eigen Valuestable.eigs.vector

Table: Eigen Vectorstable.loadings Table: Loadingstable.fi Table: Factor Scorestable.partial.fi


Table: Array of Partial Factor Scorestable.tau Table: Percent Variance Explained

Author(s)


References


mpMahalanobis 37

See Also

mpKPlus1STATIS, mpSTATIS

mpMahalanobis mpMahalanobis: Mahalanobis Distance

Description

Computation of Mahalanobis Distance

Usage

mpMahalanobis(data, row.design)

Arguments

data Data Matrixrow.design Design Matrix which identifies the groups of the data matrix

Details

Computation of Mahalanobis Distance which is used in mpCANOSTATIS.

Value

D Matrix of Mahalanobis Distances

Author(s)


References


See Also

mpCANOSTATIS

Examples

#Mahalanobis Exampledata('wines2012')data <- as.matrix(wines2012$data[,1:6])design <- makeNominalData(as.matrix(c('NZ','NZ','NZ','NZ','FR','FR','FR','FR','CA','CA','CA','CA')))demo <- mpMahalanobis(data,design)

38 mpMFA

mpMFA mpMFA: Multiple Factor Analysis via MExPosition

Description

Multiple Factor Analysis via MExPosition

Usage

mpMFA(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), DESIGN is a vector that indicates groups (andwill be dummy-coded). If FALSE, design is a dummy-coded matrix.

Details

mpMFA performs multiple factor analysis on a set of data matrices.

Value

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






Table which indicates the tables$Overview$preprocess.data

Preprocessed Data Matrix$Overview$num.groups

Number of Groups

mpMFA 39

$Overview$num.obs


Option of row preprocessing selected$Overview$column.preprocess

Option of column preprocessing selected$Overview$table.preprocess

Option of table preprocessing selected


$InnerProduct$S

Inner Product: Scalar Product Matrices$InnerProduct$RVMatrix

Inner Product: RV Matrix$InnerProduct$C




Inner Product: Factor Scores$InnerProduct$tau


Alpha Weights (alpha)












40 mpMultitable

$Table$eigs.vector






$Table$ci Table: Contribution of the rows

$Tabl$cj Table: Contribution of the columns

$Table$t Table: Percent of variance explained

Author(s)


References

Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Op-timum 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.): Encyclopediaof Measurement and Statistics. Sage. pp. 657-663.

See Also

mpDISTATIS

Examples

# MFAdata('wines2007')demo.mfa.2007 <- mpMFA(wines2007$data, wines2007$table)

mpMultitable mpMFA: Multitable Analysis via MExPosition

Description

Multitable Analysis via MExPosition

Usage

mpMultitable(data, column.design, make.columndesign.nominal = TRUE,DESIGN = NULL, make.design.nominal = TRUE, graphs = TRUE)

mpMultitable 41

Arguments







Details

mpMultitable performs multitable analysis on a set of data matrices.

Value









Number of Groups$Overview$num.obs






$InnerProduct$S


Inner Product: RV Matrix

42 mpMultitable

$InnerProduct$C


























Author(s)


mpPTA 43

References



See Also

mpDISTATIS

Examples

#Multitabledata('wines2007')demo.multitable.2007 <- mpMultitable(wines2007$data, wines2007$table)

mpPTA mpPTA: Core Function for Partial Triadic Analysis (PTA) via MExPo-sition

Description

All PTA steps are combined in this function. It enables preparation of the data, processing andgraphing.

Usage

mpPTA(data, column.design, make.columndesign.nominal = TRUE,DESIGN = NULL, make.design.nominal = TRUE, graphs = TRUE)

Arguments







44 mpPTA

Details

mpPTA performs Partial Triadic Analysis (PTA) on a set of data matrices.

Value















$InnerProduct$S









mpPTA 45




















Author(s)


References



See Also

mpDISTATIS

46 mpPTA.core

Examples

#Multitabledata('wines2007')demo.multitable.2007 <- mpMultitable(wines2007$data, wines2007$table)

mpPTA.core mpPTA.core: Core Function for Partial Triadic Analysis (PTA) viaMExPosition

Description

Performs the core of PTA

Usage

mpPTA.core(data, num.obs, column.design, num.groups, optimization.option = 'STATIS')

Arguments

data Matrix of dataset

num.obs Number of observations in dataset

column.design Column Design for dataset

num.groups Number of groups in datasetoptimization.option


Details

Computation of Partial Triadic Analyis (PTA). This function should not be used independently. Itshould be used with mpPTA.

Value


RVMatrix Inner Product:RV Matrix









mpPTA.core 47


compromise Compromise Matrix

compromise.eigs


compromise.eigs.vector







table.eigs Table: Eigen Values

table.eigs.vector




table.partial.fi





Author(s)


References


See Also

mpPTA

48 mpSTATIS

mpSTATIS mpSTATIS: STATIS via MExPosition

Description

All STATIS steps are combined in this function. It enables preprocessing, processing, optimizationand 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




row.design Matrix which identifes the different groups.make.rowdesign.nominal


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.’




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 prepreprocessingand optimization will be selected via the R console.

mpSTATIS 49

Value















$InnerProduct$S











Compromise Matrix

50 mpSTATIS

$Compromise$compromise.eigs



$Compromise$compromise.fi















Author(s)

Cherise R. Chin Fatt <[email protected]> and Derek Beaton

References



See Also

mpDISTATIS

mpSTATIS.columnPreproc 51

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 )

mpSTATIS.columnPreproc

mpSTATIS.columnPreproc: Column Preprocessing for STATIS

Description

Preprocessing of the columns of the table for STATIS.

Usage

mpSTATIS.columnPreproc(data, column.preprocess = 'None')

Arguments

data Data Matrixcolumn.preprocess

String option with the following options: ’None’ (default),’Center’,’1Norm’,’Center_1Norm’ and ’Z_Score’

Details

Column Preprocessing is the second preprocessing step in STATIS. The only combination of Col-umn Preprocessing allowed is Column Center plus 1 Norm.Besides this combination, all othercolumn preprocessing options are done independently.

If you need to create the Group Matrix into a design matrix, you can use makeNominalData whichwas developed by Derek Beaton.

Value

A matrix of the same dimensions as X, which is the result of the column preprocessing step chosen.

Author(s)


References


52 mpSTATIS.core

See Also

mpSTATIS.rowPreproc, mpSTATIS.tablePreproc, mpSTATIS.preprocess

Examples

# Center- type of column preprocessing chosencolumn.preprocess = 'Center'X <- matrix(1:10,2)preproc <- mpSTATIS.columnPreproc(X, column.preprocess)

mpSTATIS.core mpSTATIS.core

Description

mpSTATIS.core performed the core of STATIS.

Usage

mpSTATIS.core(data, num.obs, column.design, num.groups, optimization.option = 'STATIS')

Arguments


num.obs Number of Observations

column.design Matrix which identifies the tables.

num.groups Number of Groups/Tablesoptimization.option

String option with the following options: ’None’, ’STATIS’, ’RV Matrix’ and’STATIS Power 1’

Value

S Scalar Product Matrices

RVMatrix RV Matrix

C C Matrix


eigs Inner Product: Eigen Value



alphaWeights Alpha Weights

compromise Compromise Matrixcompromise.eigs.vector

Compromise: Eigen Vectors

mpSTATIS.core 53

compromise.eigs



compromise.ci Compromise: contribution of the rows

compromise.cj Compromise: contribution of the colunbs

compromise.tau Compromise: Percent Variance Explained

table.eigs.value

Table: Eigen Values

table.eigs Table: Eigen Vectors



table.partial.fi




table.ci Table: contribution of the rows

table.cj Table: contribution of the columns


Author(s)


References



See Also

mpSTATIS, mpDISTATIS.core, mpDISTATIS

54 mpSTATIS.optimize

mpSTATIS.optimize mpSTATIS.optimize: STATIS Optimization Options

Description

Provides various optimization options for STATIS.

Usage

mpSTATIS.optimize(data, num.obs, column.design = NULL,num.groups, optimization.option = 'STATIS')

Arguments

data Data Matrix

num.obs Number of Observation

column.design Table which identifies the tables.

num.groups Number of Tablesoptimization.option

String option with the following options: ’None’, ’STATIS’ (default), ’RV_Matrix’,’STATIS_Power1’, ’ANISOSTATIS_Type1’, ’ANISOSTATIS_Type2’

Details

After the optimization option is passed through this function, the core of the STATIS processing isperformed by calling either mpSTATIS.core or mpANISOSTATIS.core.

Author(s)


References


See Also

mpDISTATIS, mpSTATIS, mpANISOSTATIS.core

mpSTATIS.preprocess 55

mpSTATIS.preprocess mpSTATIS.preprocess: Preprocessing for STATIS

Description

Combines all preprocessing choices, and prepares the data for STAITS processing.

Usage

mpSTATIS.preprocess(data, column.design = NULL, row.design = NULL,row.preprocess = 'None', column.preprocess = 'None', table.preprocess = 'None',make.columndesign.nominal = TRUE, make.rowdesign.nominal = TRUE)

Arguments

data Data Matrix


row.design Matrix which identifies the groups

row.preprocess String option for row preprocessing with the following options: ’None’ (de-fault), ’Profile’, ’Hellinger’, ’Center’ and ’Center_Hellinger’

column.preprocess

String option for column preprocessing with the following options: ’None’ (de-fault), ’Center’, ’1Norm’, ’Center_1Norm’ and ’Z_Score’

table.preprocess

String option for table preprocessing with the following options: ’None’ (de-fault), ’Num_Columns’, ’Tucker’, ’Sum_PCA’, ’RV_Normalization’ and ’MFA_Normalization’



make.rowdesign.nominal


Details

This function calls all the preprocessing functions and consolidates the results. In addition it pre-pares the group matrix, and gets the data ready for processing.

Valuedata.preprocessed

Matrix of the Preprocessed Data

num.obs Number of Observations

col.groups Original matrix which was selected in the initial step

groupMatrix Matrix which identifies the Tables

56 mpSTATIS.rowPreproc

numgroups Number of Tables

table.ids Table IDs

row.preprocess Option of row preprocessing selectedcolumn.preprocess

Option of column preprocessing selectedtable.preprocess


Author(s)


References


See Also

mpSTATIS.rowPreproc, mpSTATIS.columnPreproc, mpSTATIS.tablePreproc

Examples

X <- matrix(1:10,2)Y<- as.matrix(c('g1','g1','g1','g2','g2'))row.preprocess='Center'column.preprocess='Center'table.preprocess='Sum_PCA'preproc <-mpSTATIS.preprocess(X, column.design = t(Y), row.preprocess = row.preprocess,column.preprocess = column.preprocess, table.preprocess = table.preprocess)

mpSTATIS.rowPreproc mpSTATIS.rowPreproc: Row Preprocessing for STATIS

Description

Preprocessesing of the rows of the matrix for STATIS.

Usage

mpSTATIS.rowPreproc(data, row.preprocess = 'None')

Arguments

data Data Matrix

row.preprocess String option with the following options: ’None’(Default), ’Profile’, ’Hellinger’,’Center’,and ’Center_Hellinger’

mpSTATIS.tablePreproc 57

Details

Row Preprocessing is the first preprocessing step in STATIS. The only combination of row pre-processing that is allowed is Centering and Hellinger. The other preprocessing options cannot becombined.


Value

A matrix of the same dimensions as the data matrix, which is the result of the row preprocessingstep chosen.

Author(s)


References


See Also

mpSTATIS.rowPreproc, mpSTATIS.columnPreproc, mpSTATIS.tablePreproc

Examples

# Center - type of row preprocessing choosenrow.preprocess ='Center'X <- matrix(1:10,2)preproc <- mpSTATIS.rowPreproc(X, row.preprocess)

mpSTATIS.tablePreproc mpSTATIS.tablePreproc: Table Preprocessing for STATIS

Description

Preprocessing of the tables for STATIS.

Usage

mpSTATIS.tablePreproc(data, column.design, table.preprocess = 'None')

58 mpSTATIS.tablePreproc

Arguments

data Data Matrix


table.preprocess

String option with the following options: ’None’ (Default), ’Num_Columns’,’Tucker’,’Sum_PCA’, ’RV_Normalization’ and ’MFA_Normalization’

Details

Table Preprocessing is the last preprocessing step in STATIS. Only one type of table preprocessingis suggested.


Value

The output of STATIS.tablePreproc is a matrix of the same dimensions as the data matrix, which isthe result of the table preprocessing step chosen.

Author(s)


References


See Also

mpSTATIS.rowPreproc, mpSTATIS.columnPreproc, mpSTATIS.preprocess

Examples

# Sum PCA - type of table preprocessing choosentable.preprocess='Sum_PCA'X <- matrix(1:10,2)Y<- c('g1','g1','g1','g2','g2')groupMatrix <- t(makeNominalData(as.matrix(Y)))preproc <- mpSTATIS.tablePreproc(X,groupMatrix, table.preprocess)

mpSumPCA 59

mpSumPCA mpSumPCA: Sum PCA via MExPosition

Description

Sum PCA via MExPosition

Usage

mpSumPCA(data, column.design, make.columndesign.nominal = TRUE,DESIGN = NULL, make.design.nominal = TRUE, graphs = TRUE)

Arguments







Details

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

Value









Number of Groups

60 mpSumPCA

$Overview$num.obs






$InnerProduct$S




















mpTableCheck 61

$Table$eigs.vector









Author(s)


References



See Also

mpDISTATIS

Examples

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

mpTableCheck Table Check for MExPosition

Description

MExPosition’s table check function. Calls into ExPosition’s designCheck.

Usage

mpTableCheck(data, table = NULL, make_table_nominal = TRUE)

62 print.covstatis.compromise

Arguments

data original data that should be matched to a design matrix

table a column vector with levels for observations or a dummy-coded matrixmake_table_nominal

a boolean. Will make DESIGN nominal if TRUE (default).

Details

Execution stops if:1. design has only 1 column (group), or 2. A column of the table has too few column-table assign-ments, or 3. A column of the table has too many column-table assignments

Value

table dummy-coded design matrix

Author(s)

Derek Beaton and Cherise R. Chin Fatt

print.covstatis.compromise

Print the results of the Compromise for COVSTATIS

Description

S3 Class function to print results for MExPosition.

Usage

## S3 method for class 'covstatis.compromise'print(x,...)

Arguments

x an object of class COVSTATIS

... inherited/passed arguments for S3 print method(s).

Author(s)


print.covstatis.innerproduct 63

print.covstatis.innerproduct

Print the results of the Inner Product for COVSTATIS

Description


Usage

## S3 method for class 'covstatis.innerproduct'print(x,...)

Arguments



Author(s)


print.covstatis.overview

Print the Overview for COVSTATIS

Description


Usage

## S3 method for class 'covstatis.overview'print(x,...)

Arguments



Author(s)


64 print.distatis.compromise

print.covstatis.table Print the results of the Tables for COVSTATIS

Description


Usage

## S3 method for class 'covstatis.table'print(x,...)

Arguments



Author(s)


print.distatis.compromise

Print the results of the Compromise of DISTATIS

Description

S3 Class function to print the results for MExPosition.

Usage

## S3 method for class 'distatis.compromise'print(x,...)

Arguments

x an object of class STATIS


Author(s)


print.distatis.innerproduct 65

print.distatis.innerproduct

Print the results of the Inner Product of DISTATIS

Description


Usage

## S3 method for class 'distatis.innerproduct'print(x,...)

Arguments



Author(s)


print.distatis.overview

Print the results of the Overview of DISTATIS

Description


Usage

## S3 method for class 'distatis.overview'print(x,...)

Arguments



Author(s)


66 print.doact.statis.compromise

print.distatis.table Print the results of the Table of DISTATIS

Description


Usage

## S3 method for class 'distatis.table'print(x,...)

Arguments



Author(s)


print.doact.statis.compromise

Print the results of the Compromise for DO-ACT

Description


Usage

## S3 method for class 'doact.statis.compromise'print(x,...)

Arguments

x an object of class DO-ACT


Author(s)


print.doact.statis.innerproduct 67

print.doact.statis.innerproduct

Print the results of the Inner Product for DO-ACT

Description


Usage

## S3 method for class 'doact.statis.innerproduct'print(x,...)

Arguments

x an object of class Do-ACT


Author(s)


print.doact.statis.overview

Print the results of the Overview for DO-ACT

Description


Usage

## S3 method for class 'doact.statis.overview'print(x,...)

Arguments



Author(s)


68 print.KPlus1.statis.compromise

print.doact.statis.table

Print the results of the Table for DO-ACT

Description


Usage

## S3 method for class 'doact.statis.table'print(x,...)

Arguments



Author(s)


print.KPlus1.statis.compromise

Print the results of the Compromise for (K+1) STATIS

Description


Usage

## S3 method for class 'KPlus1.statis.compromise'print(x,...)

Arguments

x an object of class KPlus1.statis


Author(s)


print.KPlus1.statis.innerproduct 69

print.KPlus1.statis.innerproduct

Print the results of the Inner Product for (K+1) STATIS

Description


Usage

## S3 method for class 'KPlus1.statis.innerproduct'print(x,...)

Arguments



Author(s)


print.KPlus1.statis.overview

Print the results of the Overview for (K+1) STATIS

Description


Usage

## S3 method for class 'KPlus1.statis.overview'print(x,...)

Arguments



Author(s)


70 print.mexPosition.Output

print.KPlus1.statis.table

Print the results of the Table for (K+1) STATIS

Description


Usage

## S3 method for class 'KPlus1.statis.table'print(x,...)

Arguments



Author(s)


print.mexPosition.Output

Print the results of MExPosition

Description


Usage

## S3 method for class 'mexPosition.Output'print(x,...)

Arguments



Author(s)


print.mpANISOSTATIS 71

print.mpANISOSTATIS Print ANISOSTATIS results

Description

S3 Class function to print ANISOSTATIS results.

Usage

## S3 method for class 'mpANISOSTATIS'print(x,...)

Arguments

x list that contains items to make into the mpANISOSTATIS class.


Author(s)


print.mpCOVSTATIS Print COVSTATIS results

Description

S3 Class function to print COVSTATIS results.

Usage

## S3 method for class 'mpCOVSTATIS'print(x,...)

Arguments

x list that contains items to make into the mpCOVSTATIS class.


Author(s)


72 print.mpDOACT.STATIS

print.mpDISTATIS Print DISTATIS results

Description

S3 Class function to print DISTATIS results.

Usage

## S3 method for class 'mpDISTATIS'print(x,...)

Arguments

x list that contains items to make into the mpDISTATIS class.


Author(s)


print.mpDOACT.STATIS Print DOACT.STATIS results

Description

S3 Class function to print DOACT.STATIS results.

Usage

## S3 method for class 'mpDOACT.STATIS'print(x,...)

Arguments

x list that contains items to make into the mpDOACT.STATIS class.


Author(s)


print.mpGraphs 73

print.mpGraphs Print the results of the Graphs of STATIS

Description


Usage

## S3 method for class 'mpGraphs'print(x,...)

Arguments



Author(s)


print.mpKPlus1STATIS Print KPlus1STATIS results

Description

S3 Class function to print KPlus1STATIS results.

Usage

## S3 method for class 'mpKPlus1STATIS'print(x,...)

Arguments

x list that contains items to make into the mpKPlus1STATIS class.


Author(s)


74 print.mpSTATIS

print.mpMFA Print MFA results

Description

S3 Class function to print the MFA results.

Usage

## S3 method for class 'mpMFA'print(x,...)

Arguments

x list that contains items to make into the mpMFA class.


Author(s)


print.mpSTATIS Print STATIS results

Description

S3 Class function to print the STATIS results.

Usage

## S3 method for class 'mpSTATIS'print(x,...)

Arguments

x list that contains items to make into the mpSTATIS class.


Author(s)


print.statis.compromise 75

print.statis.compromise

Print the results for the Compromise of STATIS

Description


Usage

## S3 method for class 'statis.compromise'print(x,...)

Arguments



Author(s)


print.statis.innerproduct

Print the results of the Inner Product of STATIS

Description


Usage

## S3 method for class 'statis.innerproduct'print(x,...)

Arguments



Author(s)


76 print.statis.table

print.statis.overview Print the results of the Overview of STATIS

Description


Usage

## S3 method for class 'statis.overview'print(x,...)

Arguments



Author(s)


print.statis.table Print the results of the Tables of STATIS

Description


Usage

## S3 method for class 'statis.table'print(x,...)

Arguments



Author(s)


Index

∗Topic graphsmpGraphs, 31

∗Topic miscmpGraphs, 31mpTableCheck, 61

∗Topic multivariateMExPosition-package, 3mpANISOSTATIS, 4mpANISOSTATIS.core, 7mpCANOSTATIS, 9mpCANOSTATIS.core, 12mpCOVSTATIS, 14mpCOVSTATIS.core, 17mpDISTATIS, 19mpDISTATIS.core, 22mpDOACT.STATIS, 24mpDOACT.STATIS.core, 28mpGraphs, 31mpKPlus1STATIS, 32mpKPlus1STATIS.core, 35mpMahalanobis, 37mpMFA, 38mpMultitable, 40mpPTA, 43mpPTA.core, 46mpSTATIS, 48mpSTATIS.columnPreproc, 51mpSTATIS.core, 52mpSTATIS.optimize, 54mpSTATIS.preprocess, 55mpSTATIS.rowPreproc, 56mpSTATIS.tablePreproc, 57mpSumPCA, 59

∗Topic packageMExPosition-package, 3

designCheck, 61

ExPosition, 3

makeNominalData, 51, 57, 58MExPosition, 31MExPosition (MExPosition-package), 3MExPosition-package, 3mpANISOSTATIS, 4, 9mpANISOSTATIS.core, 7, 7, 54mpCANOSTATIS, 9, 12, 14, 16, 18, 37mpCANOSTATIS.core, 12, 12mpCOVSTATIS, 14mpCOVSTATIS.core, 17mpDISTATIS, 4, 9, 14, 19, 22, 23, 40, 43, 45,

50, 53, 54, 61mpDISTATIS.core, 22, 53mpDOACT.STATIS, 24, 28, 30mpDOACT.STATIS.core, 28mpGraphs, 31mpKPlus1STATIS, 32, 35, 37mpKPlus1STATIS.core, 35mpMahalanobis, 37mpMFA, 38mpMultitable, 40mpPTA, 43, 46, 47mpPTA.core, 46mpSTATIS, 4, 9, 14, 21, 23, 28, 30, 35, 37, 48,

53, 54mpSTATIS.columnPreproc, 51, 56–58mpSTATIS.core, 23, 52, 54mpSTATIS.optimize, 54mpSTATIS.preprocess, 52, 55, 58mpSTATIS.rowPreproc, 52, 56, 56, 57, 58mpSTATIS.tablePreproc, 52, 56, 57, 57mpSumPCA, 59mpTableCheck, 61

prettyGraphs, 31, 32print.covstatis.compromise, 62print.covstatis.innerproduct, 63print.covstatis.overview, 63print.covstatis.table, 64print.distatis.compromise, 64

77

78 INDEX

print.distatis.innerproduct, 65print.distatis.overview, 65print.distatis.table, 66print.doact.statis.compromise, 66print.doact.statis.innerproduct, 67print.doact.statis.overview, 67print.doact.statis.table, 68print.KPlus1.statis.compromise, 68print.KPlus1.statis.innerproduct, 69print.KPlus1.statis.overview, 69print.KPlus1.statis.table, 70print.mexPosition.Output, 70print.mpANISOSTATIS, 71print.mpCOVSTATIS, 71print.mpDISTATIS, 72print.mpDOACT.STATIS, 72print.mpGraphs, 73print.mpKPlus1STATIS, 73print.mpMFA, 74print.mpSTATIS, 74print.statis.compromise, 75print.statis.innerproduct, 75print.statis.overview, 76print.statis.table, 76

svd, 3

Documents

Package ‘MExPosition’ - The Comprehensive R … ‘MExPosition’ February 19, 2015 Type Package Title Multi-table ExPosition Version 2.0.3 Date 2013-06-10 Author Cherise R. Chin