Linear discriminant analysis is a method you can use when you have a set of predictor variables and you’d like to classify a response variable into two or more classes.
This tutorial provides a step-by-step example of how to perform linear discriminant analysis in R.
Step 1: Load Necessary Libraries
First, we’ll load the necessary libraries for this example:
Step 2: Load the Data
For this example, we’ll use the built-in iris dataset in R. The following code shows how to load and view this dataset:
#attach iris dataset to make it easy to work with attach(iris) #view structure of dataset str(iris) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 ...
We can see that the dataset contains 5 variables and 150 total observations.
For this example we’ll build a linear discriminant analysis model to classify which species a given flower belongs to.
We’ll use the following predictor variables in the model:
And we’ll use them to predict the response variable Species, which takes on the following three potential classes:
Step 3: Scale the Data
One of the key assumptions of linear discriminant analysis is that each of the predictor variables have the same variance. An easy way to assure that this assumption is met is to scale each variable such that it has a mean of 0 and a standard deviation of 1.
We can quickly do so in R by using the scale() function:
#scale each predictor variable (i.e. first 4 columns) iris[1:4] <- scale(iris[1:4])
We can use the apply() function to verify that each predictor variable now has a mean of 0 and a standard deviation of 1:
#find mean of each predictor variable apply(iris[1:4], 2, mean) Sepal.Length Sepal.Width Petal.Length Petal.Width -4.484318e-16 2.034094e-16 -2.895326e-17 -3.663049e-17 #find standard deviation of each predictor variable apply(iris[1:4], 2, sd) Sepal.Length Sepal.Width Petal.Length Petal.Width 1 1 1 1
Step 4: Create Training and Test Samples
Next, we’ll split the dataset into a training set to train the model on and a testing set to test the model on:
#make this example reproducible set.seed(1) #Use 70% of dataset as training set and remaining 30% as testing set sample <- sample(c(TRUE, FALSE), nrow(iris), replace=TRUE, prob=c(0.7,0.3)) train <- iris[sample, ] test <- iris[!sample, ]
Step 5: Fit the LDA Model
Next, we’ll use the lda() function from the MASS package to fit the LDA model to our data:
#fit LDA model model <- lda(Species~., data=train) #view model output model Call: lda(Species ~ ., data = train) Prior probabilities of groups: setosa versicolor virginica 0.3207547 0.3207547 0.3584906 Group means: Sepal.Length Sepal.Width Petal.Length Petal.Width setosa -1.0397484 0.8131654 -1.2891006 -1.2570316 versicolor 0.1820921 -0.6038909 0.3403524 0.2208153 virginica 0.9582674 -0.1919146 1.0389776 1.1229172 Coefficients of linear discriminants: LD1 LD2 Sepal.Length 0.7922820 0.5294210 Sepal.Width 0.5710586 0.7130743 Petal.Length -4.0762061 -2.7305131 Petal.Width -2.0602181 2.6326229 Proportion of trace: LD1 LD2 0.9921 0.0079
Here is how to interpret the output of the model:
Prior probabilities of group: These represent the proportions of each Species in the training set. For example, 35.8% of all observations in the training set were of species virginica.
Group means: These display the mean values for each predictor variable for each species.
Coefficients of linear discriminants: These display the linear combination of predictor variables that are used to form the decision rule of the LDA model. For example:
- LD1: .792*Sepal.Length + .571*Sepal.Width – 4.076*Petal.Length – 2.06*Petal.Width
- LD2: .529*Sepal.Length + .713*Sepal.Width – 2.731*Petal.Length + 2.63*Petal.Width
Proportion of trace: These display the percentage separation achieved by each linear discriminant function.
Step 6: Use the Model to Make Predictions
Once we’ve fit the model using our training data, we can use it to make predictions on our test data:
#use LDA model to make predictions on test data predicted <- predict(model, test) names(predicted)  "class" "posterior" "x"
This returns a list with three variables:
- class: The predicted class
- posterior: The posterior probability that an observation belongs to each class
- x: The linear discriminants
We can quickly view each of these results for the first six observations in our test dataset:
#view predicted class for first six observations in test set head(predicted$class)  setosa setosa setosa setosa setosa setosa Levels: setosa versicolor virginica #view posterior probabilities for first six observations in test set head(predicted$posterior) setosa versicolor virginica 4 1 2.425563e-17 1.341984e-35 6 1 1.400976e-21 4.482684e-40 7 1 3.345770e-19 1.511748e-37 15 1 6.389105e-31 7.361660e-53 17 1 1.193282e-25 2.238696e-45 18 1 6.445594e-22 4.894053e-41 #view linear discriminants for first six observations in test set head(predicted$x) LD1 LD2 4 7.150360 -0.7177382 6 7.961538 1.4839408 7 7.504033 0.2731178 15 10.170378 1.9859027 17 8.885168 2.1026494 18 8.113443 0.7563902
We can use the following code to see what percentage of observations the LDA model correctly predicted the Species for:
#find accuracy of model mean(predicted$class==test$Species)  1
It turns out that the model correctly predicted the Species for 100% of the observations in our test dataset.
In the real-world an LDA model will rarely predict every class outcome correctly, but this iris dataset is simply built in a way that machine learning algorithms tend to perform very well on it.
Step 7: Visualize the Results
Lastly, we can create an LDA plot to view the linear discriminants of the model and visualize how well it separated the three different species in our dataset:
#define data to plot lda_plot <- cbind(train, predict(model)$x) #create plot ggplot(lda_plot, aes(LD1, LD2)) + geom_point(aes(color = Species))
You can find the complete R code used in this tutorial here.