Dual Scaling Analysis of Data
Contingency and frequency data analysis
Calculate SVD decomposition of the matrix
Multiple choice data analysis
Function to identify incorrect Multiple Choice input data
Forced multiple choice data analysis
Generate the dataframe out
Paired comparison data analysis
Rank order data analysis
Selection of solutions for analysis
Plot of Dual Scale analysis
Obtain the data used in the graphs
Print of Dual Scale analysis
Summary of Dual Scale analysis
Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets.