Comments (4)
Dear Danning,
have you set hubPar
to only a single value, e.g. to "eigenvector", and lnormFit = FALSE
?
Then, you should get some hub nodes.
Otherwise, could you please provide me with your netAnalyze() code and the dimension of the adjacency matrix (can be accessed via dim(net$adjaMat1)
and dim(net$adjaMat2)
, where net
is your constructed network object)?
Best,
Stefanie
from netcomi.
Dear Stefanie,
I did not set hubPar as I thought the default value of hubPar is 'eigenvector', and I also set lnormFit=T. My adjacency matrix is 322 x 322.
The following is my code for net construction and analysis.
treatment.net = netConstruct(t(rs.otuTab.lp),
measurePar = list(nlambda=40, lambda.min.ratio=1e-1, pulsar.params = list(rep.num=99)),
sparsMethod = "none",
seed = 1245356, verbose = 3)
treatment.analyze <- netAnalyze(treatment.net,
centrLCC = FALSE,
#avDissIgnoreInf = TRUE,
sPathNorm = FALSE,
clustMethod = "cluster_fast_greedy",
hubQuant = 0.95,
lnormFit = TRUE,
normDeg = FALSE)
Thanks,
Danning
from netcomi.
Dear Danning,
Thanks for the code and explanations.
If lnormFit = TRUE
, a log-normal distribution is fitted to the centrality values and the 95% quantile of the fitted distribution is taken for identifying the hubs. So, in this case, you won't necessarily find any hub nodes.
If, on the other hand, lnormFit
is set to FALSE, the empirical distribution function of the centralities is used and the number of hubs is predefined.
Best,
Stefanie
from netcomi.
Many thanks, Stefanie.
I see.
Best regards,
Danning
from netcomi.
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