Just like PCA, the basic idea behind correspondence analysis is to reduce the dimensionality of data and represent it in a low-dimensionality space. Correspondence analysis basically deals with contingency tables or cross tabs. This technique is designed to perform exploratory analysis on multi-way tables with some degree of correspondence between their dimensions. The common methodology followed for correspondence analysis involves the standardization of the cross tab table of frequencies so that the entries in the cross tab can be represented in terms of distance between the dimensions in a low-dimensional space.
There are a few packages available in R that provide efficient functions for correspondence analysis:
|
R functions |
Package |
|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Let's look at an example application of the R functions for simple correspondence analysis:
# Load...