estimate_cs {tna} | R Documentation |
Estimate Centrality Stability
Description
Estimates the stability of centrality measures in a network using subset sampling without replacement. It allows for dropping varying proportions of cases and calculates correlations between the original centralities and those computed using sampled subsets.
Usage
estimate_cs(x, ...)
estimate_centrality_stability(x, ...)
## S3 method for class 'tna'
estimate_cs(
x,
loops = FALSE,
normalize = FALSE,
measures = c("InStrength", "OutStrength", "Betweenness"),
iter = 1000,
method = "pearson",
drop_prop = seq(0.1, 0.9, by = 0.1),
threshold = 0.7,
certainty = 0.95,
progressbar = FALSE,
...
)
## S3 method for class 'tna'
estimate_centrality_stability(
x,
loops = FALSE,
normalize = FALSE,
measures = c("InStrength", "OutStrength", "Betweenness"),
iter = 1000,
method = "pearson",
drop_prop = seq(0.1, 0.9, by = 0.1),
threshold = 0.7,
certainty = 0.95,
progressbar = FALSE,
...
)
## S3 method for class 'group_tna'
estimate_cs(
x,
loops = FALSE,
normalize = FALSE,
measures = c("InStrength", "OutStrength", "Betweenness"),
iter = 1000,
method = "pearson",
drop_prop = seq(0.1, 0.9, by = 0.1),
threshold = 0.7,
certainty = 0.95,
progressbar = FALSE,
...
)
## S3 method for class 'group_tna'
estimate_centrality_stability(
x,
loops = FALSE,
normalize = FALSE,
measures = c("InStrength", "OutStrength", "Betweenness"),
iter = 1000,
method = "pearson",
drop_prop = seq(0.1, 0.9, by = 0.1),
threshold = 0.7,
certainty = 0.95,
progressbar = FALSE,
...
)
Arguments
x |
A |
... |
Ignored. |
loops |
A |
normalize |
A |
measures |
A |
iter |
An |
method |
A |
drop_prop |
A |
threshold |
A |
certainty |
A |
progressbar |
A |
Details
The function works by repeatedly resampling the data, dropping varying proportions of cases, and calculating centrality measures on the subsets. The correlation between the original centralities and the resampled centralities is calculated for each drop proportion. The stability of each centrality measure is then summarized using a centrality stability (CS) coefficient, which represents the proportion of dropped cases at which the correlations drop below a given threshold (default 0.7).
The results can be visualized by plotting the output object showing the stability of the centrality measures across different drop proportions, along with confidence intervals. The CS-coefficients are displayed in the subtitle.
Value
A tna_stability
object which is a list
with an element for each
measure
with the following elements:
-
cs_coefficient
: The centrality stability (CS) coefficient of the measure. -
correlations
: Amatrix
of correlations between the original centrality and the resampled centralities for each drop proportion.
If x
is a group_tna
object, a group_tna_stability
object is returned
instead, which is a list
of tna_stability
objects.
See Also
Validation functions
bootstrap()
,
deprune()
,
permutation_test()
,
permutation_test.group_tna()
,
plot.group_tna_bootstrap()
,
plot.group_tna_permutation()
,
plot.group_tna_stability()
,
plot.tna_bootstrap()
,
plot.tna_permutation()
,
plot.tna_stability()
,
print.group_tna_bootstrap()
,
print.group_tna_permutation()
,
print.group_tna_stability()
,
print.summary.group_tna_bootstrap()
,
print.summary.tna_bootstrap()
,
print.tna_bootstrap()
,
print.tna_permutation()
,
print.tna_stability()
,
prune()
,
pruning_details()
,
reprune()
,
summary.group_tna_bootstrap()
,
summary.tna_bootstrap()
Examples
model <- tna(group_regulation)
# Small number of iterations and drop proportions for CRAN
estimate_cs(
model,
drop_prop = seq(0.3, 0.9, by = 0.2),
measures = c("InStrength", "OutStrength"),
iter = 10
)