Three-Mode Principal Components Analysis

Performs Three-Mode Principal Components Analysis, which carries out Tucker Models.


This package performs Three-Mode Principal Components using Tuckers Models and plot interactive Biplot.Some experiment design generated three-way or three-mode data, repeated observations of a set of attributes for a set of individuals in different conditions. The information was displayed in a three-dimensional array, and the structure of the data was explored using Three-Mode Principal Component Analysis, the Tucker-2 Model.

Installation

devtools::install_github("gusart/tuckerR_mmgg")

Important contribution of this package

The most important contribution of this package are the interactive biplot graphics and the application of the diffit() function to find the best combination of components to retain.

library(tuckerR.mmgg)
#> 
#> Attaching package: 'tuckerR.mmgg'
#> The following object is masked from 'package:graphics':
#> 
#>     plot
data(maize_pop)
output <- tucker2R(maize_pop,amb=2,stand=TRUE,nc1=3,nc2=3)

Extract the core matrix.

output$matrizG  
#>           [,1]     [,2]      [,3]      [,4]     [,5]       [,6]
#> [1,] 10.260719 1.847900  3.553432  8.380775 3.021522 -0.5999851
#> [2,] -2.014825 3.989558  3.306571 -1.322206 3.332721 -4.2685767
#> [3,] -1.290695 3.355101 -3.429868  1.325232 3.341179  3.2866310

The plot from output of function

plot(output) 

News

Reference manual

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install.packages("tuckerR.mmgg")

1.5.1 by Gustavo Gimenez, 3 months ago


https://github.com/gusart/tuckerR_mmgg


Report a bug at https://github.com/gusart/tuckerR_mmgg/issues


Browse source code at https://github.com/cran/tuckerR.mmgg


Authors: Marta Marticorena [aut], Gustavo Gimenez [cre], Cecilia Gonzalez [ctb], Sergio Bramardi [aut]


Documentation:   PDF Manual  


GPL-3 license


Suggests knitr, testthat


See at CRAN