Description Usage Arguments Details Value Author(s) References See Also Examples
All DISTATIS steps are combined in this function. It enables preparation of the data, processing and graphing.
1 2 3 |
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. |
mpDISTATIS
performs DISTATIS on a set of data matrices measured on the same set of observations.
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 |
Cherise R. Chin Fatt cherise.chinfatt@utdallas.edu
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.
1 2 3 4 5 6 7 | 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)
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