tucker2R: Three-Mode Principal Components: Tucker 2 Model

In gusart/tuckerR_mmgg: Three-Mode Principal Components Analysis

Description

This function performs Three-Mode Principal Components using Tucker-2 Model.Compute all the output necessary to plot interactive Biplot.The Three-Mode Principal Component Analysis, provides both useful analytic and graphic tools to study and characterize phytogenetic resources, especially when the influence of environmental factors are possible.

Usage

tucker2R(datos, amb= 2, stand = TRUE, nc1 = 2, nc2 = 2, niter = 10000)

Arguments

datos	a data frame with n rows for individuals and p variable for columns. All the conditions must be the same variables names and individuals.
amb	The diferent conditions, in which the same variables and individuals had been studied. By default is 2.
stand	a boolean value, if it is TRUE (value set by default) each variable is centered and scale by variable.
nc1	number of components in the first mode, by default is 2
nc2	number of components in the second mode, by default is 2
niter	the iteration number for the Tuckals algorithm, by default 10000 iteration

Details

To determine the number of components that are going to be retained, we use previously to the algorithm applications,method called DifFit. The number of components in the third mode is obtained from the number of conditions.The labels of the variables must be the same for all conditions in the data frame.

Value

Resultado a list which stores the name of the individual and the variables, the number of iterations, the variability explained by the model, and the total variability. Proyeccion It is a list which holds the projection of individuals and variables to see if the biplot is difficult to understand because of the huge number of cases or plotted vectors. saltuck is a list with the results of the algorithm to plot the biplot, where the names of the conditions are well kept.

Author(s)

Marta Marticorena, Gustavo Gimenez, Cecilia Gonzalez, Sergio Bramardi

References

Marticorena, M.; Bramardi, S.; Defacio, R. 2010.

Characterization of maize populations in different environmental conditions by means of Three-Mode Principal Components Analysis. Revista Ciencia e Investigacion Agraria. 37(3): 93-105.

Timmerman, M.E., and H. Kiers. 2000.

Three-mode principal components analysis. Choosing numbers of components and sensitivity to local optima. The British Journal of the Mahematical and Statistical Psychology 53: 1-16.

See Also

The function plot.marta for a complete analisis.

Examples

data(maize_pop,package = "tuckerR.mmgg")
(output <- tucker2R(maize_pop,amb=2,stand=TRUE,nc1=3,nc2=3))

gusart/tuckerR_mmgg documentation built on May 17, 2019, 9:28 a.m.