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- I found a paper utilizing PCA to a multivariable system, in which one of them is a dependent variable. I though the convention was to leave dependent variable out.
So I found a paper,
https://www.sciencedirect.com/science/article/pii/S0022231314006048?via=ihub
which concerns a property of a compound called "Quantum Yield", which is a result of various factors (independent variables). The authors are trying to figure out what factors affect the quantum yield.
The authors freely allow these factors to change and calculates the Quantum Yield (by numerically solving a system of differential equations). Obviously, this makes "Quantum yield" a dependent variable. The authors utilizes PCA on both the various factors and the quantum yield, therefore applying PCA on both independent and dependent variable .
Fig.4 of Page 607 shows the loading plot (correlation circle) of the result. The "Yield" refers to the "Quantum yield". The result is meaningful, at least for this figure, because it shows that how the independent variables affect the dependent variable. But I learned that you should separate dependent variables out before you perform PCA with it. However, it could the case that it is appropriate for this objective. So, is this a valid approach in using PCA for this purpose?
I'm kind of confused here, and I am sure I must be making some silly misunderstanding.
I would greatly appreciate it if anyone could help me.
Thank you.
https://www.sciencedirect.com/science/article/pii/S0022231314006048?via=ihub
which concerns a property of a compound called "Quantum Yield", which is a result of various factors (independent variables). The authors are trying to figure out what factors affect the quantum yield.
The authors freely allow these factors to change and calculates the Quantum Yield (by numerically solving a system of differential equations). Obviously, this makes "Quantum yield" a dependent variable. The authors utilizes PCA on both the various factors and the quantum yield, therefore applying PCA on both independent and dependent variable .
Fig.4 of Page 607 shows the loading plot (correlation circle) of the result. The "Yield" refers to the "Quantum yield". The result is meaningful, at least for this figure, because it shows that how the independent variables affect the dependent variable. But I learned that you should separate dependent variables out before you perform PCA with it. However, it could the case that it is appropriate for this objective. So, is this a valid approach in using PCA for this purpose?
I'm kind of confused here, and I am sure I must be making some silly misunderstanding.
I would greatly appreciate it if anyone could help me.
Thank you.