The program calculates Linear Discriminant Analysis (LDA) parameters using the train data separated onto two classes. The Linear Discriminant Analysis is commonly used techniques for data classification. This method maximizes the ratio of between-class variance to the within-class variance in dataset thereby guaranteeing maximal separability. The approach calculates Linear Discriminant Function (LDF) which coefficients are chosen so that they result in the best separation among the groups for train data set. Variables for the classification should be specified by the user; classes for the data should be specified in the ClassVar variable by 0 or 1 values.
The LDF can be applied in the LDAClass procedure to separate any data into two groups depending on whether the value of LDF is greater or less than 0.
File should contain table of numerical data: lines for observations (cases) columns should be separated by tabulation or user-defines symbol (; , etc); for example, if comma (,) separator is used, the file format is the same as the CSV (comma separated values) format. No missing data allowed.
This program use statistical functions from "R" free software environment for statistical computing and graphics (http://www.r-project.org).
This program requires the R-package to be installed on your computer.