Hello Mahrous,
What is/are your specific research question(s)? What variables are involved? How are they measured or quantified? What sample and/or target population is your focus? How are/were data collected?
If you could possibly elaborate your query to explain these features, then I think you'd be far more likely to get constructive recommendations from the RGate community.
Good luck with your work.
Dear Mahrous Awad ,
PCA is used to reduce dimensionality of your data, therefore you will able to analize easily the relationship between treatments and other variables, as you are wondering!!
But be careful, PCA is only an explorative method that allow to describe in a visual way the relationship among the variables of your data (using the bi-plot). It is not an inferential method.
All the best,
Dear Mahrous,
I advise you to take a look at the ASCA method in case you have an experimental design.
Regards,
Hi Mahrous Awad , PCA is an important tool, especially when you have a set of variables and treatment parameters. For example, if variables are in the form of water quality parameters such as pollutant 1, pollutant 2......up to pollutant N and let`s say you have m observation variables in form of various treatment units such as treatment technology 1, treatment technology 2.......treatment technology M....then you have N x M data variables in total. Now, if you need to know the important, actually most important result of all these variable observation, PCA makes your life very easy. It will help you to discuss only the most critical observation where the 'load score' tool is of great value. You will be able to see all observation in one figure. Imagine a table of lengthy N x M data where N x M could be 100 or 1000 (I have worked even with 1000 data set, yes it was crazy), you will struggle and probably not be able to understand how to explain the results. You can go through some of my published papers where I have worked with more than 6 observation variables and more than dozen main variables or parameters. It became so easy to explain the critical results in 1-2 pages, which would have otherwise created a noisy argument. Having said that, you must learn the basics and insights to PCA theory from good books. Mastering PCA will take time but you will just love it. Some of my published papers where I have used PCA tool to explain my results: Article Physical and biological removal of Microcystin-LR and other ...
;Article Removal of Microcystin-LR and other Water Pollutants using S...
Good luck Mahrous Awad :)P.S.: And of course, it also becomes easy to understand the correlation between two or more variables. Its just one of the many benefits of using PCA.
Dear Mahrous Awad
The purpose of descriptive multivariate analysis is to get the best possible view of the structure. Large data tables usually contain a large amount of information, which is partly hidden because the data are too complex to be easily interpreted. Principal Component Analysis (PCA) is a projection method that helps you visualize all the information contained in a data table.
PCA helps you find out in what respect one sample is different from another, which variables contribute most to this difference, and whether those variables contribute in the same way (i.e. are correlated) or independently from each other. It also enables you to detect sample patterns, like any particular grouping. Finally, it quantifies the amount of useful information -as opposed to noise or meaningless variation contained in the data.
It is important that you understand PCA, since it is a very useful method in itself, and forms the basis for several classification (SIMCA) and regression (PLS/PCR) methods.
Best regards,
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