Timeline for How to reduce GAM sensitivity to sparse data
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| yesterday | comment | added | Gavin Simpson | I agree with Peter, but am more bullish; often, I don't think it matters too much which transform you use, you just want to spread out the observations of the covariate. However, one would likely do this spreading out, in this case, to better reflect know variation in depth (soil types, etc), as there is likely much more going in the uppermost layers and less at depth, in general, if my recollection of soil sampling is not faulty. | |
| yesterday | comment | added | Peter Flom | No. What I am saying is that you, as a subject matter expert, have to figure out whether you want the "correction" of the model. There is nothing wrong with what you have from a statistical point of view and, in many cases, outliers are important. There is also a tendency, on the part of subject matter experts, to give away their expertise. Do you want to take logs of depth? Go ahead. The curve will be less influenced by extreme points because they will be less extreme. Do you want to keep the model as is? Also fine. | |
| yesterday | comment | added | Aurélien Lengrand | If I understand correctly, you would prefer to keep the model as it is and attribute the issue to limited data or to the limitations of the method, rather than trying to correct it. | |
| yesterday | history | answered | Peter Flom | CC BY-SA 4.0 |