Data Analysis and Graphing using the JavaGrinders Library
Methods in the class
DataFileParser provide generalized techniques to read the content of data files (ASCII-text files) into an internal store, and extract variables of different data types from this internal store with error checking. Data are then stuffed into variables which may contain different data types. These variables are able to report descriptive statistics, allow the selection of subsets, graph the data, and subject them to statistical analysis. The nitty-gritty of the included routines are handled by the included
Apache Commons Mathematics Library and the
Java Matrix Library. My own work uses R for data analysis and Python for graphing support. The JavaGrinders library focuses on implementing routines that are either very basic, specifically instructive for use in my advanced statistics class, very experimental, or which simplify the workflow for migrating the data to the standard workhorses for subsequent data analysis.
Analyzer objects are used to perform the actual analyses, e.g., an analysis of independence on a cross-tabulation frequency table with a ContingencyAnalyzer. A HierarchyAnalyzer comes with a variety of techniques for exploring interaction and dominance tables. ContingencyAnalysisDemo illustrates how to perform a simple analysis of a cross-tabulation frequency table that has been created from information about which individual had initiated and which had received an attack. TransitionAnalysisDemo shows how to perform a sequence analysis. A spatial analysis is illustrated with the PlotAnimal demo. A demo for an initial multivariate analysis is shown at ParametricAnalysisDemo.
