mpANISOSTATIS.core: mpANISOSTATIS.core: Core Function for ANISOSTATIS via...

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

View source: R/mpANISOSTATIS.core.R

Description

Performs the core of ANISOSTATIS on the data

Usage

1
2
mpANISOSTATIS.core(data, num.obs, column.design, num.groups, 
  optimization.option='ANISOSTATIS_Type1')

Arguments

data

Matrix of preprocessed data

num.obs

Number of observations

column.design

Table Matrix- used to identifty the tables of the data matrix

num.groups

Number of groups

optimization.option

String option of either 'ANISOSTATIS_Type1' (DEFAULT), or 'ANISOSTATIS_Type2'

Details

Computation of Anisotropic STATIS (ANISOSTATIS), where the one weight is assigned per variable.

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

mpDISTATIS, mpSTATIS, mpANISOSTATIS


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