View source: R/calcExtinctionIndices.r
calc_QSS_extinction_dif | R Documentation |
The QSS determines the maximum eingenvalue of the community matrix (Jacobian) and characterizes
the linear stability of the network. This uses the function calc_QSS()
so it can take
into account the interaction strength if weights are present. The comparison is made using the Anderson-Darling test with
the function kSamples::ad.test()
and the Kolmogorov-Smirnov test stats::ks.test()
, both the p-values are reported as a
measure of strength of the diference. If istrength is TRUE it makes a comparison to a null model with the same species and links than the
reduced network but with all interaction strengths equal to the mean interacion strength.
calc_QSS_extinction_dif(g, sp_list, nsim = 1000, ncores = 4, istrength = FALSE)
g |
igraph network |
sp_list |
list with the species/nodes we will delete for the comparison |
nsim |
number of simulations to calculate QSS |
ncores |
number of cores used to perform the operation |
istrength |
if TRUE takes the weigth attribute of the network as interaction strength to calculate QSS. |
a data.frame with:
the node deleted
the Anderson-Darling p-value of the comparison with the complete network
Kolmogorov-Smirnov p-value of the comparison with the complete network
If istrength == TRUE
the Anderson-Darling p-value of the comparison with the null network
If istrength == TRUE
Kolmogorov-Smirnov p-value of the comparison with the null network
median of QSS of the complete network
median of QSS of the network with the deleted node
difference between the two previous median QSS
If istrength == TRUE
median of QSS of the null network
If istrength == TRUE
difference between the median QSS of the network with the deleted node
and the null network.
## Not run: g <- netData[[1]] # Generate random weights # V(g)$weight <- runif(vcount(g)) # Without interaction strength # calc_QSS_extinction_dif(g,V(g)$name[1:3],nsim=10,istrength = FALSE) # With interaction strength # calc_QSS_extinction_dif(g,V(g)$name[1:3],nsim=10,istrength = TRUE) ## End(Not run)
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