Read an exported Bayesian measurement summary
Source:R/api-posterior-summary.R
read_mfrm_posterior.RdBring a saved Stan/brms summary back into mfrmr for figures and reports, without loading either backend or running an estimator.
Arguments
- path
An export directory containing
posterior-summary.rdsand its matchingmodel-data.rds, or the path to that summary file.- x, object
A saved
mfrm_posterior_summary.- ...
Unused.
- parm
Optional saved parameter names (for example
"locations[1]") or row indices in$parameters.NULLreturns mapped model parameters.- level
The stored level, currently
0.95. Other levels require posterior draws and are rejected rather than approximated from an SD.
Value
An mfrm_posterior_summary containing unrounded parameter and
diagnostic tables, source settings, priors, mappings and constraints.
Details
Run the exported bundle's Rscript run.R --summarize first.
The reader checks the recorded model-data checksum, parameter mappings,
fixed-value semantics and consistency of the saved sampling checks. Older
summaries without a checksum must be regenerated with the current bundled
run.R and summarize.R; no new sampling is needed. Keep the original
model-data.rds bytes with the fit when moving an export directory.
These checks detect mismatched files and inconsistent summaries; they do
not independently verify draws, convergence, prior adequacy or model fit.
The object contains marginal summaries, not joint posterior draws or a
native fitted model. It supports mfrm_report(), apa_table(),
build_summary_table_bundle() and export_summary_appendix().
Use the backend's saved fit for new credible levels, contrasts, predictions,
trace plots or posterior predictive checks. Native scoring and MML/JML
diagnostics are not available from this object.
Estimated-population one-family Stan summaries retain the population mean
and SD as reported parameters. Their schema-2 files require this newer reader;
existing fixed-population schema-1 summaries remain supported. Use
plot(x, quantity = "population_sd") to inspect between-Person variation
on the native metric. This SD is not an individual Person standard error.
confint() retrieves the stored equal-tailed 95% posterior credible
intervals, retaining fixed/unavailable rows and review flags. It does not
calculate frequentist confidence intervals or change the saved level.
Fixed anchors and constraint-determined values have no estimated SD or
interval. External calibration uncertainty has not been propagated.
summary() returns the complete validated summary; printing is compact.
The bundled synthetic example uses short chains and requires sampling
review. Its numbers are workflow illustrations, not assessment findings.
Examples
directory <- system.file("extdata", "posterior-example", package = "mfrmr")
posterior <- read_mfrm_posterior(directory)
summary(posterior) # Read the decision and next action before the estimates.
#> External Bayesian measurement summary
#> GPCM | Stan | Fixed N(0,1)
#> Review sampling before interpreting intervals
#> Why: Fewer than four chains; Chain mixing (R-hat) needs review; Effective sample size for central summaries needs review
#> Next: inspect $checks, then model fit, prior sensitivity and target-specific MCSE.
#> 53 mapped parameters; fixed values have no estimated interval.
#> Parameter Role Mean Lower Upper Interval
#> Person: p001 estimated -0.403 -1.414 0.543 Sampling review
#> Task: t1 fixed_anchor 0.400 NA NA Fixed value
#> Task: t2 estimated -0.272 -0.471 -0.078 Sampling review
#> Step owner: r1 | 0 -> 1 fixed_anchor 0.250 NA NA Fixed value
#> Step owner: r1 | 1 -> 2 estimated -1.061 -1.997 -0.058 Sampling review
#> Task: t1 estimated 0.635 0.445 0.887 Sampling review
#> Use $parameters, confint(x), plot(x), or mfrm_report(x). Sampling checks do not establish model validity.
intervals <- confint(posterior)
head(intervals[, c("facet", "level", "value_role", "lower", "upper", "interval_status")])
#> facet level value_role lower upper
#> 2 Person p001 estimated -1.4139833 0.5430092
#> 3 Person p002 estimated -1.8840950 -0.7452628
#> 4 Person p003 estimated -1.2022894 0.6702318
#> 5 Person p004 estimated -1.7130268 0.1526192
#> 6 Person p005 estimated -0.5258744 0.2955422
#> 7 Person p006 estimated -2.7862091 -0.3048554
#> interval_status
#> 2 computed_sampling_review_required
#> 3 computed_sampling_review_required
#> 4 computed_sampling_review_required
#> 5 computed_sampling_review_required
#> 6 computed_sampling_review_required
#> 7 computed_sampling_review_required
report <- mfrm_report(posterior, style = "apa", study_context = c(
score_direction = "Higher coded scores indicate more favorable performance"))
summary(report, view = "reader")
#> mfrmr Report Summary
#>
#> Overview
#> Style OverallStatus
#> apa review
#> FirstAction
#> Read report$tables$sampling_review; resolve recorded sampling concerns before interpretation.
#> ReviewAreas NotComputedAreas CaveatAreas OptionalAreas UnavailableAreas
#> 0 0 0 0 0
#> OkAreas SourceInclude
#> 0 mfrm_posterior_summary
#>
#> First screen
#> Area Status Readiness
#> Overall review Review sampling before interpreting intervals
#> MainIssue
#> Review sampling before interpreting intervals Fewer than four chains; Chain mixing (R-hat) needs review; Effective sample size for central summaries needs review
#> NextAction
#> Read report$tables$sampling_review; resolve recorded sampling concerns before interpretation.
#> PrimaryRoute
#> report$tables$sampling_review; plot(x); confint(x)