| CDBiplot {biplotbootGUI} | R Documentation |
Clustering and/or Disjoint Biplot
Description
The CDBiplot function is a graphical user interface to construct and interact with Clustering and/or Disjoint Biplot.
Usage
CDBiplot(data, clase)
Arguments
data |
A data frame with the information to be analyzed |
clase |
A vector containing the real classification of the objects in the data |
Details
When the function is launched, firstly, it is necessary to select the kind of analysis to be used on the data. Then, a window to select the number of clusters, components, the tolerance, the number of iterations and the repetitions of the algorithm. Press the OK button and the graph will be shown. Press the left mouse button and a window will be displayed to select one option: Change the position label, Remove label or Do nothing. It is also possible to select the dimensions shown in the graph and to change the limits of the axes. In the window there are four menus:
File
Copy image
Save image
PDF file
Eps file
Png file
Jpg/Jpeg file
Exit
3D
3D
Options
Change title
Show/Hide axes
Show/Hide variables
Show/Hide row labels
Cluster
Convex-hull
The File menu provides different options to save the graph and permits to exit the program. The second menu shows the graph in 3 dimensions. The third menu allows the user to change the title and to show/hide the axes, the variables and the row labels in the graph. The last menu permits the user to draw (filled or empty) convex-hull on each cluster. The program saves a file containing the main results of the analysis.
Value
A graph showing the data representation and an output file containing the information about the results.
Author(s)
Ana Belen Nieto Librero ananieto@usal.es, Purificacion Vicente Galindo purivg@usal.es, Purificacion Galindo Villardon pgalindo@usal.es
References
Gabriel, K. R. (1971). The Biplot graphic display of matrices with applications to principal components analysis. Biometrika, 58(3), 453-467.
Galindo, M. P. (1986). Una alternativa de representacion simultanea: HJ-Biplot. Questiio, 10(1), 13-23.
Vichi, M and Saporta, G. (2009). Clustering and disjoint principal component analysis. Computational Statistics and Data Analysis, 53, 3194-3208.
Macedo, E. and Freitas, A. (2015). The alternating least-squares algorithm for CDPCA. Communications in Computer and Information Science (CCIS), Springer Verlag pp. 173-191.
Examples
data(iris)
CDBiplot(iris[,-5], iris[,5])