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

In MExPosition: Multi-table ExPosition

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

data	Matrix of preprocessed data
normalization	String option of either 'None', 'MFA' (DEFAULT), or 'Sum_PCA'
masses	Masses
table	Design Matrix - used to identifty the tables of the data matrix
make.table.nominal	a boolean. If TRUE (default), table is a vector that indicates tables (and will be dummy-coded). If FALSE, table is a dummy-coded matrix.

Details

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

Value

data	Data matrix
normalization	Inner Product: Normalization option selected
table	Design matrix used to identifty the tables of the data matrix
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	Compromise Matrix
compromise.eigs	Compromise: Eigen Values
compromise.eigs.vector	Compromise: Eigen Vector
compromise.fi	Compromise: Factor Scores
Compromise.tau	Compromise: Percent Variance Explained
compromise.ci	Compromise: Contributions of the rows
compromise.cj	Compromise: Contributions of the Columns
masses	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.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

See Also

mpCANOSTATIS

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