Description Usage Arguments Details Value See Also Examples
View source: R/robustness.r View source: R/robustness.r
run robustness analysis for a source estimate by subsampling individual events.
1 2 3 4 5 6 7  robustness(
x,
type = c("edm", "backtracking", "centrality"),
prop,
n = 100,
...
)

x 

type 
character, specifying the method, 
prop 
numeric, value between zero and one, proportion of events to be sampled 
n 
numeric, number of resamplings 
... 
parameters to be passed to origin methods 
We create subsamples of individual events and their magnitude using a sampling proportion p in [0, 1]. After aggregating the data, we apply the source estimation approach. Using this result, we deduce the relative frequency of how often the source estimate obtained with the complete data set can be recovered by source estimation based on the subsample. Thus, the estimate robustness is assessed by the proportion of estimate recovery.
data.frame
with columns
est
origin estimated when all data is evaluated
rob
estimate uncertainty, computed as the proportion of resamplings when origin estimate was recovered
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  # generate random delay data
data(ptnAth)
require(igraph)
dat < data.frame(node = sample(size = 500, make.names(V(ptnAth)$name), replace = TRUE),
time = sample(size = 500, 1:10, replace = TRUE),
delay = rexp(500, rate=10))
# compute effective distance
net < igraph::as_adjacency_matrix(ptnAth, sparse=FALSE)
p < net/rowSums(net)
eff < eff_dist(p)
colnames(eff) < paste('x.',colnames(eff),sep='')
# run robustness analysis
r5 < robustness(x=dat, type='edm', prop=0.5, n=10, distance=eff)
summary(r5)
plot(r5)
# compare results
r9 < robustness(x=dat, type='edm', prop=0.9, n=10, distance=eff)
plot(r9, add=TRUE, col='gray')

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.