Contents - Index

Why use GGEbiplot software

Graphical display is desirable, if not necessary, for a better understanding of large datasets with complex interconnectedness and interactions.  Hence the saying "One picture is worth a thousand words." The GGE biplot methodology is by far the most powerful "picture" for visualizing large two-way tables, particularly those from agricultural and life science researches. 

There is no argument among researchers whether biplot is useful in understanding their data. What limits the use of biplot by researchers is the availability of  user-friendly software. There are many macros in all major statistical packages, such as SAS, GenStat, R, etc.. However, they are not nearly as powerful and user-friendly as GGEbiplot, and their use requires considerable training. GGEbiplot is developed for all researchers, particularly for those who are not particularly trained in statistics and computer application.

• GGEbiplot is user-friendly. GGEbiplot was developed by a plant breeder for his own work. All functions are self-explanatory and all operations are through a mouse pointer. Although GGEbiplot comes with a complete help files, few users bother to use it as the functions are so self-evident. GGEbiplot was praised as "a computer game for scientists."

• GGEbiplot is powerful. 

• GGEbiplot graphically addresses most, if not all, questions the researcher is likely to ask.  For example, for a genotype-by-environment two-way dataset, GGEbiplot displays the "which-won-where" pattern and addresses the issue of mage-environment differentiation; displays both mean performance and stability of tested varieties and ranks them on an integrated index; displays both discriminating ability and representativeness of the test environments and ranks them on an integrated index; and displays the relationships among test environments and similarities among genotypes. See Functions for genotype-by-environment table for a more exhausted list of functions. 
• GGEbiplot also performs conventional statistics analysis such as analysis of variance for different experimental designs, correlation analysis, multivariate regression,  covariate analysis, etc., the results of which are both tabulated and displayed graphically. 
• GGEbiplot allows QTL mapping based on phenotypic data from a single or multiple environments. 
• GGEbiplot has powerful data manipulation tools, allowing analyzing any possible subset of the original dataset at run time. 
• GGEbiplot allows the user to modify the size, style, and color of the labels, lines, and background of the biplot to the user's preference. It generates biplots ready for publication and presentation. 
• GGEbiplot also produces numerical outputs from the original data to any subset. 
• GGEbiplot reads a 3- or 4-way dataset at once and produces all possible 2-way tables and corresponding biplots. 
• GGEbiplot has complete biplot analysis, many user-friendly conventional statistics and many specified functions for plant breeder's should meet the needs of most researchers. 
• More good reasons!

• GGEbiplot is efficient. It takes GGEbiplot about one second from reading data to generating and displaying an information-rich, well-labeled, publication-quality biplot. Although there are many macros for biplot analysis, GGEbiplot is literally a million times more efficient.