I am currently doing PCA for my data but don't really understand how to interpret the data from a PCA 2D score plot or bi plot. What does principal component 1 and principal component 2 mean? what is an eigenvalue?
Dear Asif,
PCA is an interesting linear transformation technique that helps you to understand how many data are really significant. Suppose there are 10 different variables are giving you data about an information. Now, among the variables some data of some variables could be correlated. Therefore considering all variables are just waste of computation. In that case, we use PCA to learn about the impact of the variables. It is usually found that the highest significant information are carried out by the initial principal components like prin. Comp. 1,2, and 3. Usually, we can visulize 2D or 3D data so far, that's why comp1 vs comp2 is the mostly used visualizing technique. And finally, eigenvalues are the solutions of the linear equations. If you have 10 variables to be performed PCA you have to calculate covariance matrix of the variables that will give you a 10X10 covariance matrix. Now this matrix will be considered for finding the eigenvalues. Finally you will get 10 eigenvalues those are the solution of the previous matrix. These eigenvalues will help you finding 10 different solution space for PCA projection those are called eigenvectors.
Wish you all the best for your research.
Regards!
When you do PCA you reduce your data and get items that correlate highly with each other. The correlations is usually over 0 .400. Usually you get e.g. 4 components where those items which belong together is gather in one component.
Only components with an Eigenvalue higher than 1.00 are taken into account. Read:
https://en.wikipedia.org/wiki/Principal_component_analysis
or look at youtube.
Dear Asif,
PCA is an interesting linear transformation technique that helps you to understand how many data are really significant. Suppose there are 10 different variables are giving you data about an information. Now, among the variables some data of some variables could be correlated. Therefore considering all variables are just waste of computation. In that case, we use PCA to learn about the impact of the variables. It is usually found that the highest significant information are carried out by the initial principal components like prin. Comp. 1,2, and 3. Usually, we can visulize 2D or 3D data so far, that's why comp1 vs comp2 is the mostly used visualizing technique. And finally, eigenvalues are the solutions of the linear equations. If you have 10 variables to be performed PCA you have to calculate covariance matrix of the variables that will give you a 10X10 covariance matrix. Now this matrix will be considered for finding the eigenvalues. Finally you will get 10 eigenvalues those are the solution of the previous matrix. These eigenvalues will help you finding 10 different solution space for PCA projection those are called eigenvectors.
Wish you all the best for your research.
Regards!
A.C. Rencher: Methods of Multivariate Analysis. John Wiley & Sons, New York, 2
edition, 2002 (https://b-ok.org/book/667347/2c5902/) is a good book to get before you start.
Principal Component Analysis (PCA) is an exploratory data analysis method. Principal component one (PC1) describes the greatest variance in the data. That variance is removed and the greatest remaining variance is described orthogonally to PC1 i.e. PC2. This procedure is continued until all the variance is described.
All data potentially contains noise which the software will attempt to describe. Eigenvalues are used to determine the value of the PC. Any PC with an eigenvalue
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