Necessary Steps to Perform Discriminant Analysis

Before we begin with the steps needed to perform DFA, it is essential to understand the following concepts related to the topic:

1. Dependent variable: The dependent variable is the variable that we want to predict or classify. In DFA, the dependent variable is categorical, i.e., it consists of two or more categories or groups.
2. Independent variable: Independent variables are the variables that are used to predict the dependent variable. In DFA, the independent variables are continuous.
3. Discriminant function: The discriminant function is a linear combination of the independent variables that separate the groups in the dependent variable.
4. Eigenvalue: Eigenvalues are the values that indicate the proportion of variance explained by each discriminant function.
5. Canonical correlation: Canonical correlation is a measure of the correlation between the discriminant function and the dependent variable.

Let us consider an example of DFA using the “iris” dataset available in R. The “iris” dataset contains four independent variables (sepal length, sepal width, petal length, and petal width) and one dependent variable (species).

Discriminant Function Analysis (DFA) is a statistical method used to find a discriminant function that can separate two or more groups based on their independent variables. In other words, DFA is used to determine which variables contribute most to group separation. DFA is a helpful tool in various fields such as finance, biology, marketing, etc.

In this article, we will discuss the concepts related to DFA, the steps needed to perform DFA, provide good examples, and show the output of the analysis using the R programming language.