Package index • bayesqm

Model fitting

The main entry points: fit a single Bayesian factor model, or fit across a range of K under the peak-plus-Sivula protocol.

fit_bayesian(): Fit a Bayesian Q-methodology factor model
run_bayes() select_k_peak() select_k_sivula(): Fit the model across a range of K
matchalign(): MatchAlign post-processing for Bayesian factor draws

Posterior summaries

Tidy summaries of loadings, factor z-scores, and scalar hyperparameters.

compute_loadings(): Posterior summary of participant factor loadings
compute_zscores(): Posterior summary of statement factor z-scores
compute_factor_array(): Factor arrays on the forced Q-sort distribution
compute_posterior_scalars(): Posterior summary of scalar hyperparameters

Probabilistic factor membership

Posterior probabilities replacing the classical z-score tests and Brown-threshold flags.

compute_threshold_prob() compute_dominant_prob() compute_dominant_sign() compute_divergence() classify_membership(): Probabilistic factor-membership and divergence summaries
critical_delta(): Reliability-adjusted critical difference (default delta)
suggest_delta(): Suggested separation delta from the forced distribution

Plots (base R)

Every view reads a quantity the fit already carries; no new dependencies required.

plot(<bayesqm_fit>): Factor-score dotchart for a bayesqm_fit
plot_loading_posterior(): Loading forest with 50 and 95 percent credible intervals
plot_zscore_posterior(): Per-statement factor-score posterior across factors
plot_membership(): Dominant-factor posterior-probability heatmap
plot_elpd(): ELPD across K with peak and Sivula annotations
plot_ppc(): Posterior predictive check on the correlation-matrix RMSE
plot_tucker(): MatchAlign Tucker's phi distribution by factor
plot_dist_cons(): Distinguishing/consensus divergence forest
plot_hyper(): Hyperparameter posterior densities

ggplot2 figure renderers

Publication-grade ggplot2 figures for bayesqm_run and bayesqm_fit: the ELPD curve, the dominant-factor panel, and the PPC ridgeline. Also reachable through ggplot2::autoplot().

make_elpd_diff(): Delta-ELPD plot with Sivula band, peak, and adopted-K annotations
make_dominant_panel(): Probabilistic dominant-factor panel
make_ppc_ridge(): Posterior predictive RMSE ridgeline across K
autoplot.bayesqm_fit(): ggplot2 renderings of a bayesqm_fit
autoplot.bayesqm_run(): ggplot2 renderings of a bayesqm_run

Theming and export

Palette control, figure export, and ready-to-paste captions.

bayesqm_colors() bayesqm_set_colors(): Get or set the bayesqm colour scheme
save_bayesqm_plot(): Save a bayesqm plot to file
caption_bayesqm(): Dynamic figure caption for a bayesqm_fit
rename_factors(): Rename factors consistently across a bayesqm_fit

Standard accessors for a bayesqm_fit

coef(), fitted(), residuals(), and the rstantools generics.

coef(<bayesqm_fit>) fitted(<bayesqm_fit>) residuals(<bayesqm_fit>) nobs(<bayesqm_fit>) sigma(<bayesqm_fit>) family(<bayesqm_fit>) print(<bayesqm_family>) as.matrix(<bayesqm_fit>) as.array(<bayesqm_fit>) as.data.frame(<bayesqm_fit>) update(<bayesqm_fit>): Standard R accessors for bayesqm_fit
print(<bayesqm_fit>) summary(<bayesqm_fit>) print(<bayesqm_run>) summary(<bayesqm_run>): Print and summary methods for bayesqm_fit and bayesqm_run
posterior_interval(<bayesqm_fit>): Credible intervals for bayesqm_fit parameters
prior_summary(<bayesqm_fit>) print(<bayesqm_prior>): Prior summary for a bayesqm_fit

Data: import and construction

Readers for CSV/Excel/PQMethod/Ken-Q/KADE/HTMLQ and the qsort_data object.

read_qsort() read_qsort_csv() read_qsort_excel() read_pqmethod() read_kenq() read_kenq_excel() read_kade_zip() read_easyhtml_firebase() read_statements(): Read Q-sort data from file
import.pqmethod() import.htmlq() import.kenq() import.easyhtmlq(): qmethod-style import aliases
qsort_data() validate_qsort() check_distribution() infer_distribution() parse_distribution(): Construct a validated qsort_data object
print(<qsort_data>) summary(<qsort_data>) as.matrix(<qsort_data>): Print, summary, and matrix conversion for qsort_data

Simulation helpers

Data-generating functions and assessment utilities.

demo_fit(): A synthetic bayesqm_fit for examples and tutorials
demo_run(): A synthetic bayesqm_run for examples and tutorials
generate_data() generate_loadings() generate_noise() discretize_to_grid() get_distribution(): Simulate Q-sort data
assess_recovery() assess_classification(): Simulation-study assessment helpers
tucker_congruence() procrustes_rotation(): Tucker's congruence and orthogonal Procrustes rotation