CanonicalStatisBiplot function

CANONICAL STATIS-ACT for multiple tables with common rows and its associated Biplot

The procedure performs STATIS-ACT methodology for multiple tables with common rows and its associated biplot

CanonicalStatisBiplot(X, Groups, InitTransform = "Standardize columns", dimens = 2,
                 SameVar = FALSE)

Arguments

• X: A list containing multiple tables with common rows
• Groups: A factor containing the groups
• InitTransform: Initial transformation of the data matrices
• dimens: Dimension of the final solution
• SameVar: Are the variables the same for all occasions?

Details

The procedure performs Canonical STATIS-ACT methodology for multiple tables with common rows and its associated biplot. When the variables are the same for all occasions trajectories for the variables can also be plotted.

Returns

An object of class StatisBiplot

References

Vallejo-Arboleda, A., Vicente-Villardon, J. L., & Galindo-Villardon, M. P. (2007). Canonical STATIS: Biplot analysis of multi-table group structured data based on STATIS-ACT methodology. Computational statistics & data analysis, 51(9), 4193-4205.

Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Comput Stat, 4, 124-167.

Efron, B.,Tibshirani, RJ. (1993). An introduction to the bootstrap. New York: Chapman and Hall. 436p.

Escoufier, Y. (1976). Operateur associe a un tableau de donnees. Annales de laInsee, 22-23, 165-178.

Escoufier, Y. (1987). The duality diagram: a means for better practical applications. En P. Legendre & L. Legendre (Eds.), Developments in Numerical Ecology, pp. 139-156, NATO Advanced Institute, Serie G. Berlin: Springer.

L'Hermier des Plantes, H. (1976). Structuration des Tableaux a Trois Indices de la Statistique. [These de Troisieme Cycle]. University of Montpellier, France.

Ringrose, T.J. (1992). Bootstrapping and Correspondence Analysis in Archaeology. Journal of Archaeological Science. 19:615-629.

Author(s)

Jose Luis Vicente Villardon

Examples

data(Chemical)
x= Chemical[37:144,5:9]
weeks=as.factor(as.numeric(Chemical$WEEKS[37:144]))
levels(weeks)=c("W2" , "W3", "W4")
X=Convert2ThreeWay(x,weeks, columns=FALSE)
Groups=Chemical$Treatment[1:36]
canstbip=CanonicalStatisBiplot(X, Groups, SameVar = TRUE)
plot(canstbip, mode="s", PlotVars=TRUE, ShowBox=TRUE)