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In bayesqm: Bayesian Q Methodology: Probabilistic Factor Analysis
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bayesqm: Bayesian Q Methodology: Probabilistic Factor Analysis
Description
A Bayesian factor-analytic framework for Q methodology. Fits a low-rank factor model to Q-sort data with a Student-t likelihood and a hierarchical normal prior on loadings, samples the posterior with Stan, resolves rotational ambiguity via the MatchAlign post-processing of Poworoznek et al. (2025) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/25-BA1544")}, and returns posterior summaries including credible intervals for loadings and factor scores, probabilistic dominant-factor membership, distinguishing and consensus statements, and PSIS-LOO-based factor enumeration following Vehtari et al. (2017) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11222-016-9696-4")} with the Sivula et al. (2025) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/25-BA1569")} parsimony rule.
A Bayesian factor-analytic framework for Q methodology. Fits a low-rank
factor model to Q-sort data with a Student-t likelihood and a hierarchical
normal prior on loadings, samples the posterior with Stan, resolves
rotational ambiguity via MatchAlign post-processing, and returns
posterior summaries including credible intervals for loadings and factor
scores, probabilistic dominant-factor membership, distinguishing and
consensus statements, and PSIS-LOO-based factor enumeration.
Details
The typical workflow is:
-
Import data. read_qsort() auto-detects CSV, Excel,
PQMethod .DAT, Ken-Q JSON / multi-sheet Excel, KADE ZIP, or
Easy-HTMLQ Firebase JSON. qsort_data() constructs the object
directly from a matrix.
-
Fit the model. fit_bayesian() returns a bayesqm_fit
object. run_bayes() fits the model for a range of K and returns
a bayesqm_run object carrying the ELPD comparison table and the
peak-plus-Sivula protocol verdict.
-
Summarise the posterior. compute_loadings(),
compute_zscores(), compute_factor_array(),
compute_dominant_prob(), compute_threshold_prob(),
compute_divergence(), classify_membership(), and
compute_posterior_scalars().
-
Use standard R accessors. coef(), fitted(),
residuals(), sigma(), family(), nobs(), as.matrix(),
as.array(), as.data.frame(), update(), plus
rstantools::posterior_interval() and
rstantools::prior_summary() work directly on the fit.
Draws extraction works with the posterior package (as_draws_df(),
as_draws_matrix(), as_draws_array()), which in turn makes the fit
usable with bayesplot and tidybayes through their standard
conventions.
Relationship to the qmethod package
The bayesqm_fit object parallels qmethod::qmethod output where that
is meaningful, so scripts written against qmethod largely keep
working:
Slot names match: $dataset, $loa, $zsc, $zsc_n, $f_char,
$qdc, $flagged.
-
$qdc is the Bayesian divergence table (per-viewpoint grid and
z-score with 95% CrI, then D_j with 95% CrI, pi_D, pi_C),
not the classical significance-label vocabulary.
Dotted reader aliases (import.pqmethod(), import.htmlq(),
import.kenq(), import.easyhtmlq()) forward to the read_*
readers.
Intentional Bayesian divergences:
-
$f_char$characteristics omits the classical test-theory columns
(av_rel_coef, reliability, se_fscores, sd_dif). Factor-score
uncertainty is already quantified by the posterior credible
intervals in $ci_lower and $ci_upper, so Spearman-Brown
composite reliability is not the right construct.
-
$flagged is a logical N x K matrix defined as
P(argmax_k |Lambda[i, k]| = k) > 0.5 rather than Brown's (1980)
significance-based rule. The posterior probability makes the
Bayesian analogue direct.
-
$brief uses K, N, J (not nfactors, nqsort, nstat) and
includes Bayesian-specific fields (family, prob, priors,
backend).
Author(s)
Maintainer: Raymond Dacosta Azadda rdazadda@alaska.edu
Authors:
Raymond Dacosta Azadda rdazadda@alaska.edu
AK-ACE Team
Karsten Hueffer
Taa'aii Peter
Stacy Rasmus
References
Poworoznek, E., Anceschi, N., Ferrari, F., & Dunson, D. (2025). Efficiently
Resolving Rotational Ambiguity in Bayesian Matrix Sampling with
Matching. Bayesian Analysis.
Sivula, T., Magnusson, M., Matamoros, A. A., & Vehtari, A. (2025).
Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model
Comparison. Bayesian Analysis.
Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC. Statistics
and Computing, 27(5), 1413-1432.
See Also
Useful links:
bayesqm documentation built on June 18, 2026, 1:07 a.m.
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| bayesqm-package | R Documentation |
bayesqm: Bayesian Q Methodology: Probabilistic Factor Analysis
Description
A Bayesian factor-analytic framework for Q methodology. Fits a low-rank factor model to Q-sort data with a Student-t likelihood and a hierarchical normal prior on loadings, samples the posterior with Stan, resolves rotational ambiguity via the MatchAlign post-processing of Poworoznek et al. (2025) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/25-BA1544")}, and returns posterior summaries including credible intervals for loadings and factor scores, probabilistic dominant-factor membership, distinguishing and consensus statements, and PSIS-LOO-based factor enumeration following Vehtari et al. (2017) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11222-016-9696-4")} with the Sivula et al. (2025) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/25-BA1569")} parsimony rule.
A Bayesian factor-analytic framework for Q methodology. Fits a low-rank factor model to Q-sort data with a Student-t likelihood and a hierarchical normal prior on loadings, samples the posterior with Stan, resolves rotational ambiguity via MatchAlign post-processing, and returns posterior summaries including credible intervals for loadings and factor scores, probabilistic dominant-factor membership, distinguishing and consensus statements, and PSIS-LOO-based factor enumeration.
Details
The typical workflow is:
-
Import data.
read_qsort()auto-detects CSV, Excel, PQMethod.DAT, Ken-Q JSON / multi-sheet Excel, KADE ZIP, or Easy-HTMLQ Firebase JSON.qsort_data()constructs the object directly from a matrix. -
Fit the model.
fit_bayesian()returns abayesqm_fitobject.run_bayes()fits the model for a range of K and returns abayesqm_runobject carrying the ELPD comparison table and the peak-plus-Sivula protocol verdict. -
Summarise the posterior.
compute_loadings(),compute_zscores(),compute_factor_array(),compute_dominant_prob(),compute_threshold_prob(),compute_divergence(),classify_membership(), andcompute_posterior_scalars(). -
Use standard R accessors.
coef(),fitted(),residuals(),sigma(),family(),nobs(),as.matrix(),as.array(),as.data.frame(),update(), plusrstantools::posterior_interval()andrstantools::prior_summary()work directly on the fit.
Draws extraction works with the posterior package (as_draws_df(),
as_draws_matrix(), as_draws_array()), which in turn makes the fit
usable with bayesplot and tidybayes through their standard
conventions.
Relationship to the qmethod package
The bayesqm_fit object parallels qmethod::qmethod output where that
is meaningful, so scripts written against qmethod largely keep
working:
Slot names match:
$dataset,$loa,$zsc,$zsc_n,$f_char,$qdc,$flagged.-
$qdcis the Bayesian divergence table (per-viewpoint grid and z-score with 95% CrI, thenD_jwith 95% CrI,pi_D,pi_C), not the classical significance-label vocabulary. Dotted reader aliases (
import.pqmethod(),import.htmlq(),import.kenq(),import.easyhtmlq()) forward to theread_*readers.
Intentional Bayesian divergences:
-
$f_char$characteristicsomits the classical test-theory columns (av_rel_coef,reliability,se_fscores,sd_dif). Factor-score uncertainty is already quantified by the posterior credible intervals in$ci_lowerand$ci_upper, so Spearman-Brown composite reliability is not the right construct. -
$flaggedis a logicalN x Kmatrix defined asP(argmax_k |Lambda[i, k]| = k) > 0.5rather than Brown's (1980) significance-based rule. The posterior probability makes the Bayesian analogue direct. -
$briefusesK,N,J(notnfactors,nqsort,nstat) and includes Bayesian-specific fields (family,prob,priors,backend).
Author(s)
Maintainer: Raymond Dacosta Azadda rdazadda@alaska.edu
Authors:
Raymond Dacosta Azadda rdazadda@alaska.edu
AK-ACE Team
Karsten Hueffer
Taa'aii Peter
Stacy Rasmus
References
Poworoznek, E., Anceschi, N., Ferrari, F., & Dunson, D. (2025). Efficiently Resolving Rotational Ambiguity in Bayesian Matrix Sampling with Matching. Bayesian Analysis.
Sivula, T., Magnusson, M., Matamoros, A. A., & Vehtari, A. (2025). Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison. Bayesian Analysis.
Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5), 1413-1432.
See Also
Useful links:
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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