mpDISTATIS: mpDISTATIS: DISTATIS via MExPosition

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

View source: R/mpDISTATIS.R

Description

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

Usage

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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

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.

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

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

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:

$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

$Overview$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

The results for InnerProduct are bundled inside of $InnerProduct

$InnerProduct$S

Inner Product: Scalar Product Matrices

$norm.S

Normalized Scalar Product Matrices

$InnerProduct$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

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: Array of Partial Factor Scores

$Table$cj

Table:Contribution for the rows

$Table$cj

Table: Contribution for the columns

$Table$t

Table:Percent Variance Explained

Author(s)

Cherise R. Chin Fatt cherise.chinfatt@utdallas.edu

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

mpSTATIS

Examples

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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$data
demo.distatis <- mpDISTATIS(face.data, sorting = 'No', normalization = 'MFA', table = table)

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