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How to get best fitting model decision for data X and Y? |
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| fonclbrsch |
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Anmeldedatum: 21.06.16
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Verfasst am: 21.06.2016, 11:56
Titel: How to get best fitting model decision for data X and Y?
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Hello everybody.
I have two sources of data, X and Y, which are basically counts, from 23 individual origins (3D ROIs in my case).
For example, see screenshot.
Now I want to know how these two data-sources correlate, so e.g. which of these ROIs show a linear relationship in X and Y, which a exponential/logarithmic/... relationship.
So I want to fit different models to X and Y for each ROI and get a decision, which model fits best.
I still struggle to get this working as I am unsure whats the best way to go and how to get this implemented. I think a Bayesian Model Selection approach would be suitable but couldn't find a good example of how to implement that. Furthermore, I am not quite sure if BMS would be the best way to go or if I should try another approach.
What I want to achieve is a 'decision' what's the underlying relationship of the data X and Y in each ROI. I already fitted a linear, exponential and power model to the data and got the GOF data for each. But I read that R^2 isn't as suitable as I thought, so I am looking for a more valid approach like e.g. Bayes Model Selection or similar.
Or maybe something like http://alumni.media.mit.edu/~tpminka/statlearn/demo/ at 'Polynomial fitting'. But I don't know how to get the likelihood and evidence plots as shown in his example.
Thanks a lot!
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