Package multiway. February 20, 2016

Package ‘multiway’ February 20, 2016 Type Package Title Component Models for Multi-Way Data Version 1.0-2 Date 2016-02-19 Author Nathaniel E. Helwig ...
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Package ‘multiway’ February 20, 2016 Type Package Title Component Models for Multi-Way Data Version 1.0-2 Date 2016-02-19 Author Nathaniel E. Helwig Maintainer Nathaniel E. Helwig Depends parallel Description Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality, non-negativity, and structural). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis. License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication 2016-02-20 00:54:26

R topics documented: multiway-package congru . . . . . . corcondia . . . . fitted . . . . . . . fnnls . . . . . . . indscal . . . . . . krprod . . . . . . mpinv . . . . . . ncenter . . . . . . nscale . . . . . . parafac . . . . . . parafac2 . . . . . reorder . . . . . . rescale . . . . . .

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multiway-package resign . sca . . . smpower sumsq . tucker .

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Component Models for Multi-Way Data

Description Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality and non-negativity). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis. Details indscal fits the Individual Differences Scaling model. parafac fits the 3-way and 4-way Parallel Factor Analysis-1 model. parafac2 fits the 3-way and 4-way Parallel Factor Analysis-2 model. sca fits the four different Simultaneous Component Analysis models. tucker fits the 3-way and 4-way Tucker Factor Analysis model. Author(s) Nathaniel E. Helwig Maintainer: Nathaniel E. Helwig References Bro, R., & De Jong, S. (1997). A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics, 11, 393-401. Bro, R., & Kiers, H.A.L. (2003). A new efficient method for determining the number of components in PARAFAC models. Journal of Chemometrics, 17, 274-286. Carroll, J. D., & Chang, J-J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of "Eckart-Young" decmoposition. Psychometrika, 35, 283-319. Harshman, R. A. (1970). Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multimodal factor analysis. UCLA Working Papers in Phonetics, 16, 1-84. Harshman, R. A. (1972). PARAFAC2: Mathematical and technical notes. UCLA Working Papers in Phonetics, 22, 30-44. Harshman, R. A., & Lundy, M. E. (1994). PARAFAC: Parallel factor analysis. Computational Statistics and Data Analysis, 18, 39-72. Helwig, N. E. (2013). The special sign indeterminacy of the direct-fitting Parafac2 model: Some implications, cautions, and recommendations, for Simultaneous Component Analysis. Psychometrika, 78, 725-739.

congru

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Kiers, H. A. L., ten Berge, J. M. F., & Bro, R. (1999). PARAFAC2-part I: A direct-fitting algorithm for the PARAFAC2 model. Journal of Chemometrics, 13, 275-294. Kroonenberg, P. M., & de Leeuw, J. (1980). Principal component analysis of three-mode data by means of alternating least squares algorithms. Psychometrika, 45, 69-97. Moore, E.H. (1920). On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society 26, 394-395. Penrose, R. (1950). A generalized inverse for matrices. Mathematical Proceedings of the Cambridge Philosophical Society 51, 406-413. Timmerman, M. E., & Kiers, H. A. L. (2003). Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences. Psychometrika, 68, 105-121. Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis. Psychometrika, 31, 279-311. Examples # See examples for indscal, parafac, parafac2, sca, and tucker

congru

Tucker’s Congruence Coefficient

Description Calculates Tucker’s congruence coefficient (uncentered correlation) between x and y if these are vectors. If x and y are matrices then the congruence between the columns of x and y are computed. Usage congru(x, y = NULL) Arguments x

Numeric vector, matrix or data frame.

y

NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to y = x (but more efficient).

Details Tucker’s congruence coefficient is defined as Pn

i=1 xi yi Pn 2 i=1 xi i=1

r = pPn

where xi and yi denote the i-th elements of x and y.

yi2

4

corcondia

Value Returns a scalar or matrix with congruence coefficient(s). Note If x is a vector, you must also enter y. Author(s) Nathaniel E. Helwig References Tucker, L.R. (1951). A method for synthesis of factor analysis studies (Personnel Research Section Report No. 984). Washington, DC: Department of the Army. Examples ##########

EXAMPLE 1

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