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This document contains all statistical analyses conducted for the manuscript. Note that due to the random iterative nature of some analyses (such as RDA & PCA) some figure parameters will change slightly
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01
Standardize the data set to ensure that all variables are on the same scale.
02
Calculate the covariance matrix of the standardized data set.
03
Compute the eigenvectors and eigenvalues of the covariance matrix.
04
Sort the eigenvectors by their corresponding eigenvalues in descending order.
05
Select the top k eigenvectors to form the principal components, where k is the desired number of dimensions in the reduced feature space.

Who needs what is principal component?

01
Data analysts and data scientists who want to reduce the dimensionality of a data set while retaining as much variance as possible.
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Machine learning practitioners who need to preprocess data before feeding it into a model for tasks like clustering, classification, and regression.

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Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of data while retaining as much variance as possible. It identifies the underlying patterns in the data.
Typically, researchers, data analysts, and scientists who are working with large datasets and wish to simplify the data for further analysis are required to perform principal component analysis.
To fill out a principal component analysis, one must first standardize the data, calculate the covariance matrix, compute the eigenvectors and eigenvalues, and then select the principal components based on explained variance.
The purpose of principal component analysis is to simplify the data by reducing its dimensionality while retaining as much relevant information as possible. This allows for easier visualization and interpretation of the data.
The results of a principal component analysis typically include the eigenvalues, eigenvectors, explained variance, and the loading scores for each variable in the dataset.
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