robustness: run robustness analysis for a source estimate by subsampling individual events.

Description

run robustness analysis for a source estimate by subsampling individual events.

Usage

robustness(
  x,
  type = c("edm", "backtracking", "centrality"),
  prop,
  n = 100,
  ...
)

Value

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

Arguments

x: data.frame, dataset with individual events and their magnitude, to be passed to aggr_data
type: character, specifying the method, 'edm', 'backtracking' and 'centrality' are available.
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 origin_edm, origin_backtracking or origin_centrality

Details

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.

Examples

Run this code

# 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')