Package index • rasch

Estimation

Fit Rasch models and estimate item and person parameters.

rasch(): Fit a Rasch model
rasch_mfrm(): Fit a many-facet Rasch model
rasch_efrm(): Fit the extended frame of reference model
rasch_explanatory(): Fit an explanatory Rasch model
btl_explanatory(): Fit an explanatory comparative judgement model
explanatory_test(): Compare an explanatory model with its free calibration
explanatory_diagnostics(): Diagnose fixed departures from an explanatory model
relax_explanatory(): Relax a nominated explanatory restriction
relax_btl_explanatory(): Add a fixed object departure to an explanatory comparative judgement model
pcml(): Estimate Rasch thresholds by pairwise conditional maximum likelihood
pcml_pc(): Estimate Rasch thresholds using a principal-component parameterisation
threshold_index(): Enumerate item-category thresholds
item_moments(): Category-score moments for a polytomous item

Test of fit and comparison

Examine fit, targeting and reliability, or compare fitted models.

fit_summary_table(): Test-of-fit summary as a table
targeting_table(): Targeting and reliability summary as a table
chisq_detail(): Class-interval detail for one item's chi-square test of fit
fit_bootstrap(): Bootstrap fit statistics
test_information(): Test information function
lr_test(): Compare the partial credit and rating scale models
compare_fits(): Compare fitted Rasch models
guttman_table(): Guttman-ordered response matrix and reproducibility

Persons

person_wle(): Warm's weighted likelihood estimates by raw score
weighted_person_estimates(): Person estimates with externally imposed weights
person_extrapolated(): Person measures with extrapolated extreme scores
score_table(): Raw score to measure conversion table

Invariance and DIF

Examine DIF across groups or occasions and equate calibrations.

dif_anova(): Differential item functioning by residual analysis of variance
dif_bootstrap(): Bootstrap sensitivity analysis for DIF
dif_contrasts(): Planned DIF contrasts
dif_posthoc(): Pairwise follow-up comparisons for a DIF term
dif_size(): DIF differences between factor levels
split_items(): Split items by a person factor to resolve DIF
drop_items(): Drop items and refit
resolve_frames(): Resolve items that do not hold across frames
frame_invariance(): Test item invariance across frames
resolve_dif(): Resolve differential item functioning by iterative item splitting
equate_tests(): Equate two test calibrations through their common items
tailored_analysis(): Tailored analysis for guessing

Independence and dimensionality

Examine dimensionality and local response dependence.

residual_correlations(): Residual correlations for local dependence (Yen's Q3)
residual_pca(): Principal components of the residual correlations
dimensionality_test(): Residual-component test of unidimensionality
dimensionality_magnitude(): Magnitude of multidimensionality from a subtest analysis
dependence_magnitude(): Estimate the magnitude of response dependence between two items
spread_test(): Spread-parameter test for dependence within subtests
combine_items(): Combine items into subtests and re-analyse
rack_data() stack_data(): Reshape repeated measurements for racked or stacked analysis

Multiple choice and traditional statistics

distractor_analysis(): Distractor analysis for multiple-choice items
distractor_rescore(): Propose polytomous option scores from the distractor evidence
ctt_table(): Traditional (classical test theory) statistics

Paired comparisons

Fit and examine models for dichotomous or ordered paired comparisons.

btl(): Fit comparative judgement models to paired comparisons
btl_dif(): DIF analysis for paired comparisons
btl_efrm(): Fit the extended frame of reference model for paired comparisons
btl_transitivity(): Transitivity of paired comparisons
btl_dimensionality(): Experimental residual dimensionality of paired comparisons
btl_equate(): Equate two paired-comparison calibrations through their common objects
btl_information(): Information and targeting of a paired-comparison design
btl_next_pairs(): Recommend the next informative comparisons (adaptive step)
judge_surprise(): Unexpected judgements of one judge
judge_pair_surprise(): Unexpected judgements of one judge, pair by pair
plot_btl(): Plot Bradley-Terry-Luce object locations
plot_btl_categories(): Plot polytomous-comparison category curves
plot_btl_icc(): Plot an object characteristic curve
plot_btl_dependence(): Plot a within-judge dependence effect
plot_btl_transitivity(): Consistency plot for paired-comparison transitivity
plot_btl_scree(): Scree of paired-comparison residual bimensions
plot_btl_dim_map(): Residual map of the leading paired-comparison bimension
plot_btl_judge_map(): Unexpected-judgement map for one judge (pair level)
plot_btl_units(): Plot the frame units of a paired-comparison EFRM fit
plot_btl_equate(): Plot a paired-comparison equating comparison
plot_btl_targeting(): Targeting plot for a paired-comparison design

Plots

Plot fitted models and diagnostic results.

wright_map(): Draw a Wright map with WrightMap
plot_catfreq(): Plot category frequencies
plot_ccc(): Plot category probability curves
plot_distractors(): Plot multiple-choice option curves
plot_equate(): Plot a test-equating comparison
plot_facets(): Plot facet severities
plot_frames(): Plot frame units
plot_guttman(): Plot the Guttman scalogram
plot_icc(): Plot an item characteristic curve
plot_icc_frames(): Plot an item's characteristic curves across frames
plot_item_map(): Plot the item map (location against fit residual)
plot_kidmap(): Plot a kidmap
plot_pca(): Plot residual principal-component loadings
plot_pca_biplot(): Biplot of the first two residual components
plot_pcc(): Plot a person characteristic curve
plot_person_fit(): Plot person fit
plot_pimap(): Plot the person-item threshold distribution
plot_recovery(): Plot fitted against generating parameters
plot_resid_cor(): Plot the residual-correlation heatmap
plot_resid_dist(): Plot the fit residual distribution
plot_scree(): Scree plot of the residual components with parallel analysis
plot_tcc(): Plot the test characteristic curve
plot_threshold_map(): Plot the threshold map
plot_threshold_prob(): Plot threshold probability curves
plot_tif(): Plot the test information function
plot_wright(): Plot a Wright map

Data simulation

Simulate data from the fitted model families.

simulate_rasch(): Simulate person-by-item Rasch data
simulate_btl(): Simulate paired-comparison data
simulate_mfrm(): Simulate many-facet Rasch data
simulate_efrm(): Simulate extended frame-of-reference data with differing units
simulate_btl_efrm(): Simulate paired-comparison EFRM data with differing frame units
sim_replicate(): Replicate a simulation for Monte Carlo studies
sim_apply(): Apply a statistic across a simulation batch
sim_recovery(): Compare fitted and generating parameters
plot_recovery(): Plot fitted against generating parameters
rasch_rng: Random-number generation

Export and interface

Export results or launch the Shiny application.

save_outputs(): Save the outputs of a Rasch analysis
save_item_plots(): Save a plot for every item
save_person_plots(): Save a kidmap for every person
report_html(): Write a self-contained HTML report of a Rasch analysis
report_document(): Write an editable or print-ready analysis report
run_app(): Launch the rasch point-and-click graphical interface