Timeline for Statistical method for characterizing the relationship between body mass and metabolic rate
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 9, 2017 at 21:21 | answer | added | vkehayas | timeline score: 2 | |
| Feb 13, 2016 at 7:32 | answer | added | nbafrank | timeline score: 1 | |
| Jun 25, 2015 at 22:07 | comment | added | Vance L Albaugh | I would post this question to the statistics group on StackExchange (Cross Validated) under the "regression" tag. Dr. Frank Harrell, Professor of Biostatistics and Internationally Recognized leader in regression modeling is on that community. He's a phenomenal teacher and would easily be able to compare and contrast the statistical pitfalls of linear vs. nonlinear regression for that type of problem. | |
| Dec 17, 2014 at 0:41 | comment | added | quibble | One thing I can think of is that between-individual variation in metabolic rate would be larger for large (mass) individuals. Taking log will stabilize the variance. This kind of explanation is more of statistics than biology. However, this does not happen all the time, especially when we are studying a single species. | |
| Dec 17, 2014 at 0:25 | comment | added | quibble | Yes, estimated parameters are different in important ways (not negligible numerical errors). I think this is a biology question but am not sure. I am asking analyzing the data based on minimizing the sum of squares (regardless of linearized or nonlinear models). I think the question is in what (biological) space we want to minimize the sum of squares, which depends on, perhaps, the biological understanding of the metabolic theory, which I don't have. | |
| Dec 16, 2014 at 15:58 | comment | added | WYSIWYG | When you have a nonlinear curve then you should do a non-linear regression and correlation analysis. You can interpolate an exponential function. People are often more comfortable with linear analysis and that is the reason the curve is plotted in a log scale. | |
| Dec 16, 2014 at 14:34 | comment | added | fileunderwater | Are you saying that you've tried both and that the results are different (more than the margin of error from the different methods would suggest)? There shouldn't be a difference, as long as the relationship is linear on a log-log scale, beside the uncertainly that lies in the different estimation proceedures (traditionally, a linear regression has been easier to perform) | |
| Dec 16, 2014 at 14:31 | history | edited | fileunderwater | CC BY-SA 3.0 |
added relevant background
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| Dec 16, 2014 at 14:24 | history | tweeted | twitter.com/#!/StackBiology/status/544860514225254401 | ||
| Dec 16, 2014 at 12:57 | comment | added | Luke | This is more a statistics than biology question, but before choosing a statistical test it is essential to visually inspect your raw data. Is your data skewed at all? BMI in the normal population already has a Gaussian distribution, and I expect metabolic rate also would - so unless you have a reason to log-transform I would not do this. For me there is nothing wrong with performing a linear regression to test the linear association between these traits, and then also test for any non-linear effects (perhaps by including an interaction term between them). | |
| Dec 16, 2014 at 12:19 | history | asked | quibble | CC BY-SA 3.0 |