twoway_lda: Two Way Linear Discriminant Analysis of Objective Variable

In bhsu4/weightprog: Visualizing, Summarizing, and Analyzing Data with Objective Variable

Description

Two way linear discriminant analysis of the objective variable finds the linear combination of the original variables chosen that give the best possible classification. The best classification is defined as groups that are most distinctly separated from one another. twoway_lda() allows a visualization of how each of the definitive subgroups of our objective variables are separated through both histograms and scatterplots.

Usage

twoway_lda(main, avar, bvar, cvar, dvar, data, bylabel)

Arguments

main	Chosen variable with subgroups (objective variable recommended).
avar	First variable for the discriminant function.
bvar	Second variable for the discriminant function.
cvar	Third variable for the discriminant function.
dvar	Fourth variable for the discriminant function.
data	Dataset for the variables above.
bylabel	Label for scatterplot of discriminant function data points (objective variable subgroups.)

Details

twoway_lda() returns a scatterplot of the top two discriminant functions from linear combinations, with the utilization of the objective variable's subgroups as labels to see how well each subgroup is separated. Along with the scatterplot, the histogram for the top two discriminant functions can be seen.

Examples

example <- iris
twoway_lda(example$Species, example$Sepal.Length,
           example$Petal.Length, example$Sepal.Width,
           example$Petal.Width, example, example$Species)

bhsu4/weightprog documentation built on May 28, 2019, 7:10 p.m.