Description Usage Arguments Details Value Author(s) References See Also Examples
All PTA steps are combined in this function. It enables preparation of the data, processing and graphing.
1 2 |
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. |
mpPTA
performs Partial Triadic Analysis (PTA) on a set of data matrices.
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 |
Cherise R. Chin Fatt and Hervé Abdi.
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.
1 2 3 | #Multitable
data('wines2007')
demo.multitable.2007 <- mpMultitable(wines2007$data, wines2007$table)
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Loading required package: prettyGraphs
Loading required package: ExPosition
[1] "Preprocessed the Rows of the data matrix using: None"
[1] "Preprocessed the Columns of the data matrix using: None"
[1] "Preprocessed the Tables of the data matrix using: None"
[1] "Preprocessing Completed"
[1] "Optimizing using: None"
[1] "Processing Complete"
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
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