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.
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':#>#> plotdata(maize_pop)output <- tucker2R(maize_pop,amb=2,stand=TRUE,nc1=3,nc2=3)
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