Category Archives: pca

Principal components analysis (PCA) is often used to reduce the number of variables, or dimensions, in a data set in order to simplify analysis or aid in visualization. The following is an example of using it to visualize Fisher’s five-dimensional iris data on a two-dimensional scatter plot, revealing patterns that would be difficult to detect otherwise.

First, principal components will be extracted from the four continuous variables (sepal-width, sepal-length, petal-width, and petal-length); next, these variables will be projected onto the subspace formed by the first two components extracted; and then this two-dimensional data will be shown on a scatter-plot. The fifth dimension (species) will be represented by the color of the points on the scatter-plot.

For this example, you will need the incanter.core, incanter.stats, incanter.charts, and incanter.datasets libraries. The incanter.datasets library contains sample data sets.

Load the necessary Incanter libraries.

(use '(incanter core stats charts datasets))

For more information on using these packages see the matrices, datasets, and sample plots pages on the Incanter wiki.

Next, load the iris dataset and view it.

(def iris (to-matrix (get-dataset :iris)))
(view iris)

Then, extract the columns to use in the PCA,

(def X (sel iris :cols (range 4)))

and extract the “species” column for identifying the group.

(def species (sel iris :cols 4))

Run the PCA on the first four columns only

(def pca (principal-components X))

Extract the first two principal components

(def components (:rotation pca))
(def pc1 (sel components :cols 0))
(def pc2 (sel components :cols 1))

Project the four dimension of the iris data onto the first two principal components

(def x1 (mmult X pc1)) 
(def x2 (mmult X pc2))

Now plot the transformed data, coloring each species a different color

(view (scatter-plot x1 x2 
                    :group-by species
                    :x-label "PC1" 
                    :y-label "PC2" 
                    :title "Iris PCA"))

The complete code for this example can be found here.

Further Reading

• Principal Components Analysis (Wikipedia)