Kernel issue

[AylenM]

Hi!
I'm trying NMDS scores in rKIN (following a paper idea) and I have found 95% and 75% kernel levels area in group B (not the other one) being exactly the same:
-Method Group ConfInt ShapeArea
1 Kernel A 50 0.02000903
2 Kernel A 75 0.05167157
3 Kernel A 95 0.07948332
11 Kernel B 50 0.00819259
21 Kernel B 75 0.03357224
31 Kernel B 95 0.03357224
(Also, Why do the rows always have this numbering?)

Trying ellipses method resulted ok:
Method Group ConfInt ShapeArea
1 Ellipse A 50 0.01877666
2 Ellipse A 75 0.03755332
3 Ellipse A 95 0.08115138
4 Ellipse B 50 0.01742192
5 Ellipse B 75 0.03484385
6 Ellipse B 95 0.07529630

For my understanding, my kernel results are incorrect and maybe the NMDS scores (with a lot of negative and near-to-zero values) are affecting the model performance.

Any help/suggestion/advice will be welcome!
Thank you in advance!

[salbeke]

Hi Aylen, I don't think the function is broken, but probably more of a response to your small sample sizes (if I am recalling correctly). In reality for a 2D Kernel, we would have a minimum of 19 samples, but I believe you are well below that number. Thus there may just not be enough samples to provide a measurable difference between the two confidence intervals. Tough to tell without actually having your data though.