View source: R/robustness.r View source: R/robustness.r
robustness | R Documentation |
run robustness analysis for a source estimate by subsampling individual events.
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
robustness-methods
# 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')
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