I want to correlate meteorological data and particulate matter data. Can I use both the Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA)? Or is there any preliminary test to determine which one to use? Thanks!
If you use PCA, you can run any analysis you want on the newly created PCA variables, including correlation with other variables.
If you use CCA, you need an environmental matrix.
Your answer lies in your data structure and what you hope to accomplish.
PCA used to analyze the independent variables. For example it can be Used among the concentration of different gases. To conect between dependent and independent variables, Canonical correlation analysis is suitable.
I believe that you can use both the methodologies because they will give you different information. PCA is mainly a data reduction technique, while you can use CCA to examine if correlations exist between meteorological and PM data.
All the above answers are in place, so you can use both techniques to enhance your interpretation of the data.
Again, Canonical correlation analysis can be Used to find the relationship between the pollutants concentration as dependent variables and meteorological parameters as independent variables. On the other hand, PCA can be Used to analyze the pollutants concentrations as independent variables.
CCA is used to correlate between two groups of data, while PCA is used to find the correlation within a single group of data.
- Badges
- Science topic
- Similar topics
- Engineering
- Materials Engineering
- Annealing
- Metallurgy
- Metals