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

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

View source: R/mpPTA.core.R

Description

Performs the core of PTA

Usage

1
mpPTA.core(data, num.obs, column.design, num.groups, optimization.option = 'STATIS')

Arguments

data

Matrix of dataset

num.obs

Number of observations in dataset

column.design

Column Design for dataset

num.groups

Number of groups in dataset

optimization.option

String option of either 'None', 'Multiable', 'RV_Matrix', 'STATIS' (DEFAULT), or 'STATIS_Power1'

Details

Computation of Partial Triadic Analyis (PTA). This function should not be used independently. It should be used with mpPTA.

Value

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.loadings

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

mpPTA


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