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

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

View source: R/mpPTA.R


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


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



Matrix of raw data

Matrix which identifies the different tables.


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.


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


a design matrix to indicate if rows belong to groups.

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 of Results


Results for the Inner Product


Results for the Tables

The results for Overview are bundled inside of $Overview.


Data Matrix


Table which indicates the tables


Preprocessed Data Matrix


Number of Groups


Number of Observations


Option of row preprocessing selected


Option of column preprocessing selected


Option of table preprocessing selected

The results for InnerProduct are bundled inside of $InnerProduct


Inner Product: Scalar Product Matrices


Inner Product: RV Matrix


Inner Product: C Matrix


Inner Product: Eigen Vectors


Inner Product: Eigen Values


Inner Product: Factor Scores


Inner Product: Percent Variance Explained (tau)


Alpha Weights (alpha)

The results for the Compromise are bundled inside of $Compromise


Compromise Matrix


Compromise: Eigen Values


Compromise: Eigen Vector


Compromise: Factor Scores


Compromise: Percent Variance Explained


Compromise: Contributions of the rows


Compromise: Contributions of the Columns

The results for the Tables are bundled inside of $Table.


Table: Eigen Values


Table: Eigen Vectors


Table: Loadings


Table: Factor Scores


Table: Partial Factor Scores


Table: Array of Partial Factor Scores


Table: Contribution of the rows


Table: Contribution of the columns


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.

See Also



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

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