mpCANOSTATIS.core: mpCANOSTATIS.core: Core Function for Canonical STATIS...

In MExPosition: Multi-table ExPosition

Description

Performs the core of CANOSTATIS on the given dataset

Usage

mpCANOSTATIS.core(data, num.obs = num.obs, column.design, row.design,
num.groups = num.groups, normalization = 'MFA', masses = NULL)

Arguments

data	Matrix of preprocessed data
num.obs	Number of observations
column.design	Column Design- used to identifty the tables of the data matrix
row.design	Row Design - used to identify the groups of the data matrix
num.groups	Number of groups
normalization	String option of either 'None', 'MFA' (DEFAULT), or 'Sum_PCA'
masses	Masses

Details

Computation of Canonical STATIS (CANOSTATIS), where the observations come from predefined groups and tables.

Value

mahalanobis	Mahalanobis distance matrices
normalization	Inner Product: Normalization option selected
column.design	Column Design- used to identifty the tables of the data matrix
row.design	Row Design - used to identify the groups 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

mpDISTATIS, mpSTATIS, mpCANOSTATIS

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