Experimental. Bootstraps the item weights of a
build_mcml_pc fit: rows of the raw data are resampled,
the node-level network is re-estimated each time, and the
connectivity-based composite weights are recomputed. Wide intervals
mean the weighting (and therefore the "loadings" macro
network) should not be over-interpreted.
Value
An object of class "pc_loading_stability": a list with
summary (tidy data frame: node, cluster,
weight, boot_mean, boot_sd, ci_lower,
ci_upper, sign_flips - the proportion of replicates
in which the item's sign differed from the observed one),
boot_weights (iter x n_nodes matrix), iter, and
ci_level. Has print and plot methods.
Examples
# \donttest{
set.seed(1)
df <- as.data.frame(matrix(rnorm(600), 100, 6))
names(df) <- c("a1", "a2", "a3", "b1", "b2", "b3")
cl <- list(A = c("a1", "a2", "a3"), B = c("b1", "b2", "b3"))
fit <- build_mcml_pc(df, cl, aggregation = "loadings",
method = "cor")
#> Warning: Item(s) more strongly connected to another cluster than their own (possible misassignment): a1, a2, a3, b1, b2. See $loadings (misfit, cross_cluster).
#> Warning: Reverse-keyed item(s) flipped in composites: a2, b3. See $loadings (sign).
ls <- loading_stability(fit, iter = 50, seed = 1)
ls$summary
#> node cluster weight boot_mean boot_sd ci_lower ci_upper sign_flips
#> 1 a1 A 0.1405873 0.2119349 0.2931353 -0.4594061 0.4760502 0.18
#> 2 a2 A -0.3666270 0.1041219 0.3064605 -0.4215860 0.4384606 0.68
#> 3 a3 A 0.4927857 0.1710647 0.3171502 -0.4661878 0.4662406 0.24
#> 4 b1 B 0.2856282 0.2469936 0.2441808 -0.4553946 0.4688604 0.10
#> 5 b2 B 0.2708179 0.2257656 0.2509415 -0.4394226 0.4624575 0.12
#> 6 b3 B -0.4435540 -0.1735737 0.3268693 -0.4691528 0.4829590 0.26
# }