Principal Component Analysis in R

Principal component analysis in R

Principal component analysis is a statistical technique used to represent high dimensional data in a more tractable, easier to understand, lower dimensional form without losing its meaning.  Data sets with high dimensions are more difficult to compute, analyze, and visualize. To effectively manipulate such data, we need to get rid of the redundant dimensions and keep only the most essential dimensions. There are two ways through which principal component analysis can be performed in R: – spectral decomposition and singular value decomposition. The spectral decomposition examines the correlations and covariances between variables and singular value decomposition studies the correlations and covariances between individuals. Our R code homework help experts explore the concept of principal component analysis in R to make it easierto understand for students.

Implementation of principal component analysis in R

There are several steps involved in the implementation of principal component analysis in R. Our principal component analysis assignment help experts have listed these steps below:

1.     Standardizing the data

This involves scaling and centering the data to make it easier to manipulate.

2.     Calculating Eigenvalues and Eigenvectors

An Eigenvalue is the measure of the extent to which a factor explains a set of observed variables. Eigenvector is a vector or value that doesn’t change when transformations are applied to it. Both Eigenvalues and Eigenvectors are used to reduce dimensionality in data to make it easier to understand. In R, Eigenvalues and Eigenvectors are calculated from the correlation matrix or covariance matrix. They can also be calculated using singular vector decomposition.

3.     Sorting Eigenvalues

Eigenvalues should be arranged in a descending order. This will make it easier to select the K largest Eigenvectors. K is used to represent the number of dimensions in data after factor analysis.

4.     Constructing the projection matrices

The projection matrix W must be constructed from the chosen K Eigenvectors.

5.     Transforming data sets

After constructing projection matrices, the original data set X must be transformed via W in order to obtain the K dimensional feature, also known as subspace Y.

These steps may seem a little complicated especially if you are just getting started with the concept of principal analysis in R. To help students master them better, has introduced a platform that offers both online tutoring and assignment writing services on this topic. What this means is that you can take principal component analysis assignment help from us or simply hire one of our tutors and have these steps explained further, so that you can prepare the assignment yourself.

Why does principal component analysis work?

While principal component analysis is a complex and a more technical way to reduce dimensionality in data, there are a number of reasons why it works in R programming. Here are the two main ones explained by our R code homework help professionals:

  1. First, the covariance matrix ZTZ contains approximations of how each variable in Z relates and interacts with other variables in Z. Knowing how variables related to each other is extremely important in reducing dimensions in data as it helps data analysts understand the effect each variable will have on the rest of the variables if it is removed from the data set.

Second, Eigenvalues and Eigenvectors are important. In R, they show which direction a distribution will take when the data is plotted on a multidimensional scatterplot.