Returns posterior summaries and diagnostics for a fitted bgms model.
Usage
# S3 method for class 'bgms'
summary(object, ...)See also
bgm(), print.bgms(), coef.bgms()
Other posterior-methods:
coef.bgmCompare(),
coef.bgms(),
print.bgmCompare(),
print.bgms(),
summary.bgmCompare()
Examples
# \donttest{
fit = bgm(x = Wenchuan[, 1:3])
#> 2 rows with missing values excluded (n = 360 remaining).
#> To impute missing values instead, use na_action = "impute".
#> Chain 1 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 100/2000 (5.0%)
#> Chain 2 (Warmup): ⦗━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 105/2000 (5.2%)
#> Chain 3 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 94/2000 (4.7%)
#> Chain 4 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 83/2000 (4.2%)
#> Total (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 382/8000 (4.8%)
#> Elapsed: 0s | ETA: 0s
#> Chain 1 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 400/2000 (20.0%)
#> Chain 2 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 435/2000 (21.8%)
#> Chain 3 (Warmup): ⦗━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 357/2000 (17.8%)
#> Chain 4 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 294/2000 (14.7%)
#> Total (Warmup): ⦗━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1486/8000 (18.6%)
#> Elapsed: 1s | ETA: 4s
#> Chain 1 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 700/2000 (35.0%)
#> Chain 2 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 746/2000 (37.3%)
#> Chain 3 (Warmup): ⦗━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 623/2000 (31.1%)
#> Chain 4 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 589/2000 (29.4%)
#> Total (Warmup): ⦗━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 2658/8000 (33.2%)
#> Elapsed: 1s | ETA: 2s
#> Chain 1 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1000/2000 (50.0%)
#> Chain 2 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━⦘ 1025/2000 (51.2%)
#> Chain 3 (Warmup): ⦗━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━⦘ 915/2000 (45.8%)
#> Chain 4 (Warmup): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 891/2000 (44.5%)
#> Total (Warmup): ⦗━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━⦘ 3831/8000 (47.9%)
#> Elapsed: 2s | ETA: 2s
#> Chain 1 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1250/2000 (62.5%)
#> Chain 2 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1276/2000 (63.8%)
#> Chain 3 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1194/2000 (59.7%)
#> Chain 4 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━⦘ 1152/2000 (57.6%)
#> Total (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━⦘ 4872/8000 (60.9%)
#> Elapsed: 3s | ETA: 2s
#> Chain 1 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1500/2000 (75.0%)
#> Chain 2 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1543/2000 (77.1%)
#> Chain 3 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1446/2000 (72.3%)
#> Chain 4 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━⦘ 1417/2000 (70.9%)
#> Total (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 5906/8000 (73.8%)
#> Elapsed: 3s | ETA: 1s
#> Chain 1 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 1750/2000 (87.5%)
#> Chain 2 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━⦘ 1825/2000 (91.2%)
#> Chain 3 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━⦘ 1707/2000 (85.4%)
#> Chain 4 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━⦘ 1675/2000 (83.8%)
#> Total (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 6957/8000 (87.0%)
#> Elapsed: 4s | ETA: 1s
#> Chain 1 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 2000/2000 (100.0%)
#> Chain 2 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 2000/2000 (100.0%)
#> Chain 3 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 2000/2000 (100.0%)
#> Chain 4 (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 2000/2000 (100.0%)
#> Total (Sampling): ⦗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⦘ 8000/8000 (100.0%)
#> Elapsed: 4s | ETA: 0s
summary(fit)
#> Posterior summaries from Bayesian estimation:
#>
#> Category thresholds:
#> mean mcse sd n_eff Rhat
#> intrusion (1) 0.811 0.006 0.235 1766.179 1.003
#> intrusion (2) -1.174 0.008 0.293 1253.238 1.003
#> intrusion (3) -3.538 0.013 0.433 1127.178 1.004
#> intrusion (4) -7.447 0.022 0.694 1009.023 1.005
#> dreams (1) -0.403 0.005 0.187 1559.151 1.003
#> dreams (2) -3.303 0.009 0.312 1243.566 1.006
#> ... (use `summary(fit)$main` to see full output)
#>
#> Pairwise interactions:
#> mean mcse sd n_eff n_eff_mixt Rhat
#> intrusion-dreams 0.350 0.001 0.034 1211.081 1.004
#> intrusion-flash 0.214 0.001 0.031 1341.739 1.003
#> dreams-flash 0.282 0.001 0.030 1543.999 1.002
#> Note: NA values are suppressed in the print table. They occur here when an
#> indicator was zero across all iterations, so mcse/n_eff/n_eff_mixt/Rhat are undefined;
#> `summary(fit)$pairwise` still contains the NA values.
#>
#> Inclusion probabilities:
#> mean mcse sd n0->0 n0->1 n1->0 n1->1 n_eff_mixt Rhat
#> intrusion-dreams 1 0 0 0 0 3999
#> intrusion-flash 1 0 0 0 0 3999
#> dreams-flash 1 0 0 0 0 3999
#> Note: NA values are suppressed in the print table. They occur when an indicator
#> was constant or had fewer than 5 transitions, so n_eff_mixt is unreliable;
#> `summary(fit)$indicator` still contains all computed values.
#>
#> Use `summary(fit)$<component>` to access full results.
#> Use `extract_log_odds(fit)` for log odds ratios.
#> See the `easybgm` package for other summary and plotting tools.
# }