I’ve recently runned get.communitytype analysis on my dataset and it outputted …dmm.mix.fit file with a table:
K NLE logDet BIC AIC Laplace
1 1029199.17 23961.2 1076567.76 1048693.17 1023265.99
2 1040591.29 62.39 1135330.9 1079580.29 1004794.01
3 1063994.88 -21839.64 1206105.51 1122478.88 999331.88
4 1118626.7 -53030.58 1308108.36 1196605.7 1020453.54
5 1129508.97 -85252.62 1366361.65 1226982.97 997310.09
6 1188609.82 -112403.6 1472833.52 1305578.82 1024920.75
7 1230202.12 -169400.09 1561796.84 1366666.12 1020100.1
8 1273052.85 -nan 1652018.59 1429011.85 -nan
Can you please explain the meanings of columns, especially “logDet” one? I haven’t found them neither in manual, nor in Holmes et al., 2012
Can you please also explain what does it mean “-nan”? I see that my Laplace-values haven’t gone to the minimum, oscillating instead. Does “-nan” mean that analysis was closed violently, because 7 is the maximum possible number of classes?
My apologies for asking, this calculation took literally a week on our computer, so I’m trying to understand, what went wrong, before to do a new try)
logDet column is the log of the determinant. It’s been a while since I looked, but I think it was outputted in Chris Quince’s original code. What you really care about is
Laplace and you want
Laplace to hit a minimum.
get.communitytype will continue on for several iterations after it finds a new minimum.
How many samples do you have? Are these OTUs, phylotypes, or ASVs?
Thank you for a response!
My attention was sticked to that logDet column, cause it first positive and then became negative. I thought maybe there is some meaning in that. But maybe not)
I’m not sure I can securely trust my Laplace minimum, cause instead of finding one minimum, it oscillated and found the second, smaller, minimum, than after 2 more divisions stopped. What if it should oscillate more and reach best minimum e.g. when K=9? Is 7 the maximum possible number of classes?
Do you know what does an abbreviation “-nan” mean?
There are 130 soil samples from 4 horizons of the same soil, some of them are large 250 mg samples, some of them small 10-50 mg. There are OTU’s 97% (~19 000 OTU’s in all dataset).
I thought next time to try to analise large and small samples separately, or to analise only mineral horizons without humus horizon…
I’m not sure why you’re getting
-nan - there’s no limit on the value of K. My only thought is that your samples are so diverse that it’s hard for the algorithm to find good types. I wouldn’t suggest getting fewer samples, rather you might want more. I think you should probably also try with genus-level phylotypes. I’m not sure what you mean by large and small samples - you want them all to have the same number of sequences (use sub.sample if you aren’t already).
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