Tests, within a single network, whether two edge weights or two node
centralities differ. For every pair it forms the per-resample difference from
the stored bootstrap draws, takes the percentile interval of that difference,
and flags the pair significant when the interval excludes zero; it also
reports the two-sided bootstrap p-value (Epskamp, Borsboom & Fried 2018).
This is the within-network counterpart to the edge accuracy intervals
reported by net_boot().
Arguments
- boot
A
psychnet_bootstrapobject fromnet_boot().- type
Quantity to compare:
"edge"(default), or any centrality measure bootstrapped bynet_boot()(e.g."strength","expected_influence").- ci
Confidence level for the difference interval. Defaults to the level used by the bootstrap object.
- p_adjust
Multiple-comparison adjustment for the pairwise p-values (any stats::p.adjust method). Default
"none".
Value
A tidy data frame, one row per pair, with item1, item2, the two
observed values, their observed difference, the percentile interval of the
bootstrap difference (lower, upper), the two-sided p_value, and a
logical significant.
Examples
set.seed(1)
x <- matrix(stats::rnorm(150 * 5), 150, 5) %*% chol(0.4^abs(outer(1:5, 1:5, "-")))
colnames(x) <- paste0("V", 1:5)
# method = "pcor" keeps this example fast; the "glasso" default (and
# n_boot >= 1000) is what a real analysis should use.
bs <- net_boot(x, method = "pcor", n_boot = 50, cores = 1)
difference_test(bs, type = "strength")
#> item1 item2 value1 value2 obs_diff lower upper p_value
#> 1 V1 V2 0.4706062 0.7016822 -0.231076055 -0.48433623 0.06865718 0.12
#> 2 V1 V3 0.4706062 0.7057560 -0.235149802 -0.62747879 0.12286757 0.16
#> 3 V2 V3 0.7016822 0.7057560 -0.004073746 -0.39594719 0.36665932 0.76
#> 4 V1 V4 0.4706062 0.7978913 -0.327285134 -0.52087682 -0.08094752 0.00
#> 5 V2 V4 0.7016822 0.7978913 -0.096209079 -0.40837527 0.24606446 0.44
#> 6 V3 V4 0.7057560 0.7978913 -0.092135333 -0.39241669 0.24270074 0.72
#> 7 V1 V5 0.4706062 0.3862460 0.084360127 -0.29984389 0.31054446 0.84
#> 8 V2 V5 0.7016822 0.3862460 0.315436182 -0.10336275 0.51140973 0.24
#> 9 V3 V5 0.7057560 0.3862460 0.319509929 -0.09902091 0.55579333 0.12
#> 10 V4 V5 0.7978913 0.3862460 0.411645262 -0.02614931 0.57157415 0.08
#> significant
#> 1 FALSE
#> 2 FALSE
#> 3 FALSE
#> 4 TRUE
#> 5 FALSE
#> 6 FALSE
#> 7 FALSE
#> 8 FALSE
#> 9 FALSE
#> 10 FALSE