Package: ordinalTables 1.0.0.3

John R. Donoghue

ordinalTables: Fit Models to Two-Way Tables with Correlated Ordered Response Categories

Fit a variety of models to two-way tables with ordered categories. Most of the models are appropriate to apply to tables of that have correlated ordered response categories. There is a particular interest in rater data and models for rescore tables. Some utility functions (e.g., Cohen's kappa and weighted kappa) support more general work on rater agreement. Because the names of the models are very similar, the functions that implement them are organized by last name of the primary author of the article or book that suggested the model, with the name of the function beginning with that author's name and an underscore. This may make some models more difficult to locate if one doesn't have the original sources. The vignettes and tests can help to locate models of interest. For more dertaiils see the following references: Agresti, A. (1983) <doi:10.1016/0167-7152(83)90051-2> "A Simple Diagonals-Parameter Symmetry And Quasi-Symmetry Model", Agrestim A. (1983) <doi:10.2307/2531022> "Testing Marginal Homogeneity for Ordinal Categorical Variables", Agresti, A. (1988) <doi:10.2307/2531866> "A Model For Agreement Between Ratings On An Ordinal Scale", Agresti, A. (1989) <doi:10.1016/0167-7152(89)90104-1> "An Agreement Model With Kappa As Parameter", Agresti, A. (2010 ISBN:978-0470082898) "Analysis Of Ordinal Categorical Data", Bhapkar, V. P. (1966) <doi:10.1080/01621459.1966.10502021> "A Note On The Equivalence Of Two Test Criteria For Hypotheses In Categorical Data", Bhapkar, V. P. (1979) <doi:10.2307/2530344> "On Tests Of Marginal Symmetry And Quasi-Symmetry In Two And Three-Dimensional Contingency Tables", Bowker, A. H. (1948) <doi:10.2307/2280710> "A Test For Symmetry In Contingency Tables", Clayton, D. G. (1974) <doi:10.2307/2335638> "Some Odds Ratio Statistics For The Analysis Of Ordered Categorical Data", Cliff, N. (1993) <doi:10.1037/0033-2909.114.3.494> "Dominance Statistics: Ordinal Analyses To Answer Ordinal Questions", Cliff, N. (1996 ISBN:978-0805813333) "Ordinal Methods For Behavioral Data Analysis", Goodman, L. A. (1979) <doi:10.1080/01621459.1979.10481650> "Simple Models For The Analysis Of Association In Cross-Classifications Having Ordered Categories", Goodman, L. A. (1979) <doi:10.2307/2335159> "Multiplicative Models For Square Contingency Tables With Ordered Categories", Ireland, C. T., Ku, H. H., & Kullback, S. (1969) <doi:10.2307/2286071> "Symmetry And Marginal Homogeneity Of An r × r Contingency Table", Ishi-kuntz, M. (1994 ISBN:978-0803943766) "Ordinal Log-linear Models", McCullah, P. (1977) <doi:10.2307/2345320> "A Logistic Model For Paired Comparisons With Ordered Categorical Data", McCullagh, P. (1978) <doi:10.2307/2335224> A Class Of Parametric Models For The Analysis Of Square Contingency Tables With Ordered Categories", McCullagh, P. (1980) <doi:10.1111/j.2517-6161.1980.tb01109.x> "Regression Models For Ordinal Data", Penn State: Eberly College of Science (undated) <https://online.stat.psu.edu/stat504/lesson/11> "Stat 504: Analysis of Discrete Data, 11. Advanced Topics I", Schuster, C. (2001) <doi:10.3102/10769986026003331> "Kappa As A Parameter Of A Symmetry Model For Rater Agreement", Shoukri, M. M. (2004 ISBN:978-1584883210). "Measures Of Interobserver Agreement", Stuart, A. (1953) <doi:10.2307/2333101> "The Estimation Of And Comparison Of Strengths Of Association In Contingency Tables", Stuart, A. (1955) <doi:10.2307/2333387> "A Test For Homogeneity Of The Marginal Distributions In A Two-Way Classification", von Eye, A., & Mun, E. Y. (2005 ISBN:978-0805849677) "Analyzing Rater Agreement: Manifest Variable Methods".

Authors:John R. Donoghue [aut, cre]

ordinalTables_1.0.0.3.tar.gz
ordinalTables_1.0.0.3.tar.gz(r-4.7-any)ordinalTables_1.0.0.3.tar.gz(r-4.6-any)
ordinalTables_1.0.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ordinalTables/json (API)

# Install 'ordinalTables' in R:
install.packages('ordinalTables', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • budget_actual - Participation in household budgeting by psychiatric patients. Rows are ratings by patient, columns are ratings by relative. 1 - not at all 2 - doing some 3 - doing regularly
  • budget_expected - Ratings of expected participation in household budgeting by psychiatric patients. Rows are ratings by patient, columns are ratings by relative. 1 - not at all 2 - doing some 3 - doing regularly
  • coal_g - Degree of disease measured at two points in time for mine workers.
  • depression - Ratings of severity of patient's depression by two therapists.
  • dogs - Dehydration in dogs data set.
  • dreams - Severity of disturbing dreams in adolescent boys, measured at two ages..
  • dumping - Occurrence of side effects after gastro-intestinal surgery.
  • esophageal_cancer - Ratings of number of hot drinks consumed by cases with cancer of the esophagus, compared with control subjects.
  • family_income - Family income for two years from US census.
  • gender_vision - Ratings of visual acuity for men and women employed at the Royal Ordinance factories, 1943-1946.
  • homicide_black_black - Data about charges of homicide in the state of Florida.
  • homicide_black_white - Data about charges of homicide in the state of Florida.
  • homicide_white_black - Data about charges of homicide in the state of Florida.
  • homicide_white_white - Data about charges of homicide in the state of Florida.
  • hypothalamus_1 - Measures of men's hypothalamus taken from cadavers. First data set.
  • hypothalamus_2 - Measures of men's hypothalamus taken from cadavers. Second data set.
  • interference_12 - Measures of interference in memory recall study.
  • interference_control_1 - Measures of interference in memory recall study.
  • interference_control_2 - Measures of interference in memory recall study.
  • mental_health - Relationship between child's mental health and parents' socioeconomic status.
  • movies - Movie ratings by two film critics, Siskel and Ebert.
  • new_orleans_data - Agreement between two clinicians on presence of multiple sclerosis based on file.
  • occupational_status - Cross tabulation of father's employment status with son's employment status.
  • paranoia - Interrater agreement of two psychologists' ratings of paranoia.
  • radiology - Interrater agreement of two radiologists diagnosis of severity of carcinoma.
  • social_status - Social mobility data with father's occupational social status and son's occupational social status.
  • social_status2 - Social mobility data with father's occupational social status and son's occupational social status. * categories instead of 7 in social status..
  • taste - Taste ratings
  • teachers - Teachers ratings of their students intelligence.
  • teaching_style - Style of teachers rated by supervisors
  • tonsils - Relationship between size of child's tonsils and their status as a carrier of a disease.
  • tv - Interrater agreement of two journalists' evaluation of proposed TV programs.
  • vision_data - Visual acuity of women factory workers.
  • vision_data_men - Visual acuity of men factory workers.
  • winnipeg_data - Agreement between two clinicians on presence of multiple sclerosis based on file.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 141 downloads 98 exports 1 dependencies

Last updated from:d65d652ee7. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK257
source / vignettesOK342
linux-release-x86_64OK208
wasm-releaseOK107

Exports:Agresti_create_design_matrixAgresti_extract_deltaAgresti_kappa_agreementAgresti_simple_diagonals_parameter_quasi_symmetryAgresti_w_diffAgresti_weighted_tauBhapkar_marginal_homogeneityBhapkar_quasi_symmetryBowker_symmetryClayton_marginal_locationClayton_stratified_marginal_locationClayton_two_way_associationCliff_as_d_matrixCliff_compute_dCliff_dependentCliff_dependent_compute_from_matrixCliff_dependent_compute_from_tableCliff_dependent_compute_paired_dCliff_independentCliff_independent_from_matrixCliff_independent_from_tableCliff_independent_weightedCliff_weighted_d_matrixexpandGoodman_constrained_diagonals_parameter_symmetryGoodman_diagonals_parameter_symmetryGoodman_fixed_parameterGoodman_mlGoodman_model_iGoodman_model_i_starGoodman_model_iiGoodman_model_ii_starGoodman_null_associationGoodman_symmetric_association_modelGoodman_uniform_associationIreland_marginal_homogeneityIreland_quasi_symmetryIreland_quasi_symmetry_modelIreland_symmetryis_invertiblekappalikelihood_ratio_chisqloadRDatalog_likelihoodlog_linear_add_all_diagonalslog_linear_append_columnlog_linear_create_linear_by_linearlog_Linear_create_log_nlog_linear_equal_weight_agreement_designlog_linear_fitlog_linear_main_effect_designlog_linear_matrix_to_vectorlog_linear_quasi_symmetry_model_designlog_linear_remove_columnlog_linear_symmetry_designMcCullagh_compute_log_lMcCullagh_compute_regression_weightsMcCullagh_conditional_symmetryMcCullagh_fit_location_regression_modelMcCullagh_generalized_palindromic_symmetryMcCullagh_initialize_betaMcCullagh_initialize_xMcCullagh_logistic_modelMcCullagh_maximize_q_symmetryMcCullagh_palindromic_symmetryMcCullagh_proportional_hazardsMcCullagh_quasi_symmetrymodel_i_column_thetamodel_i_effectsmodel_i_fHatmodel_i_normalize_fHatmodel_i_row_column_odds_ratiosmodel_i_row_thetamodel_i_star_effectsmodel_i_star_fHatmodel_i_zetamodel_ii_effectsmodel_ii_fHatmodel_ii_ksimodel_ii_star_effectsmodel_ii_star_fHatnull_association_fHatpearson_chisqSchuster_symmetric_rater_agreement_modelStuart_marginal_homogeneityuniform_association_fHatvar_kappavar_weighted_kappavon_Eye_diagonalvon_Eye_diagonal_linear_by_linearvon_Eye_equal_weight_diagonal_linearvon_Eye_equal_weighted_diagonalvon_Eye_linear_by_linearvon_Eye_main_effectvon_Eye_weight_by_response_category_designweighted_covweighted_kappaweighted_var

Dependencies:MASS

Analysis of the Minimum Discriminant Information Statistic
Analysis of the Minimum Doscriminant Information Statistic (mdis) | Data | The Minimum Discrinant Information Statistic | Symmetry | Marginal Homogeneity | Quasi-symmetry
Rendered fromanalysis_of_mdis.Rmdusingknitr::rmarkdown

Last update: 2025-09-18
Started: 2025-09-18

Checking Whether Margins are (Stochastically) Ordered
Checking Whether the Margins are Ordered | Testing Marginal Homogeneity | Using Cliff's d-test | Clayton's Marginal Location Test | Agresti's Mann-Whitney Test | Agresti's weighted difference: Agresti_w_diff() | McCullagh's Logistic Model: McCullagh_logistic_model() | Conclusions
Rendered fromstochastic_ordering.Rmdusingknitr::rmarkdown

Last update: 2025-09-18
Started: 2025-09-18

Goodman's (1979) Analysis of Association
Null Model | Uniform Association Model | Rows and Columns as Special Cases of Model I | Decomposing the Association | Model II | Reference
Rendered fromanalysis_of_association.Rmdusingknitr::rmarkdown

Last update: 2025-09-18
Started: 2025-09-18

Models for Rater Agreement and Reliability
What's Unique about Rater Agreement? | The Data | The basic main effects model | Regular Log-linear Models | Main Effects Model | The Weight by Response Category model | Unequal Weights on the Diagonal | Agresti's Model for Ordinal Agreeement | References
Rendered fromreliability_models.Rmdusingknitr::rmarkdown

Last update: 2025-09-18
Started: 2025-09-18

Models to Fit to Square Tables
Tests for Square Tables | Data | Symmetry | Mariginal Homogenity | Quasi-Symmetry | Variations of Quasi-Symmetry
Rendered fromtests_of_square_tables.Rmdusingknitr::rmarkdown

Last update: 2025-09-18
Started: 2025-09-18

Readme and manuals

Help Manual

Help pageTopics
Solves equation Agresti_f() = 0 for delta by method of bisection..Agresti_bisection
Computes value of lambda parameterAgresti_compute_lambda
Computes the matrix pi of model-based proportionsAgresti_compute_pi
Creates the design matrix for Agresti's simple diagonal quasi-symmetry model.Agresti_create_design_matrix
First equation in section 3. Solved for kappa.Agresti_equation_1
Second equation in section 3. Solved for pi_margin.Agresti_equation_2
Third equation in section 3. Solved for lambdaAgresti_equation_3
Extracts the quasi-symmetry information from the result provided.Agresti_extract_delta
Function value for first equation in section 3.Agresti_f
Fits Agresti's agreement model that includes kappa as a parameter.Agresti_kappa_agreement
Agresti's simple diganal quasi-symmetry model.Agresti_simple_diagonals_parameter_quasi_symmetry
Computes staring values for marginal pi.Agresti_starting_values
Computes the weighted statistics listed in section 2.3.Agresti_w_diff
Computes weighted tau from Section 2.1. Agresti, A. (1983). Testing marginal homogeneity for ordinal categorical variables. Biometrics, 39(2), 505-510.Agresti_weighted_tau
Bhapkar's (1979) test for marginal homogeneityBhapkar_marginal_homogeneity
Bhapkar's 1979 test for quasi-symmetry.Bhapkar_quasi_symmetry
Computes Bowker's test of symmetry.Bowker_symmetry
Participation in household budgeting by psychiatric patients. Rows are ratings by patient, columns are ratings by relative. 1 - not at all 2 - doing some 3 - doing regularlybudget_actual
Ratings of expected participation in household budgeting by psychiatric patients. Rows are ratings by patient, columns are ratings by relative. 1 - not at all 2 - doing some 3 - doing regularlybudget_expected
Fits the tests comparing locations of the margins of a two-way table.Clayton_marginal_location
Clayton's stratified version of the marginal location comparison.Clayton_stratified_marginal_location
Computes summary, cumulative proportions up to index providedClayton_summarize
Analysis stratified by column variable j.Clayton_summarize_stratified
Clayton's stratified measure of associationClayton_two_way_association
Converts two vectors containing scores and integer frequencies (cell counts) into a d-matrixCliff_as_d_matrix
Computes between groups dominance matrix "d".Cliff_compute_d
Generates counts from table frequencies for 2 category itemsCliff_counts_2
Generates counts from table frequencies for 3 category itemsCliff_counts_3
Generates counts from table frequencies for 4 category itemsCliff_counts_4
Generates counts from table frequencies for 5 category itemsCliff_counts_5
Generates counts from table frequencies for 6 category itemsCliff_counts_6
Computes Cliff's dependent d-statistics based on a dominance matrix.Cliff_dependent
Computes sum term in covariance db-dw for weighted dominance matrix.Cliff_dependent_compute_cov
Compute the sum in the covariance of db+dwCliff_dependent_compute_cov_from_d
Computes Cliff's dependent d-statistics based on a dominance matrix.Cliff_dependent_compute_from_matrix
Computes Cliff's dependent d-statistics based on a table of frequency counts.Cliff_dependent_compute_from_table
Computes Cliff's dependent d-statistics based on cell frequencies.Cliff_dependent_compute_paired_d
Computes the independent groups d-statistic comparing the two vectors provided.Cliff_independent
Computes d-statistic from dominance matrix provided.Cliff_independent_from_matrix
Computes independent group's d-statistic from the matrix of frequencies provided.Cliff_independent_from_table
Computes d-statistic based on scores and integer weights(frequencies) for each group.Cliff_independent_weighted
Computes weighted version of dominance matrix "d"Cliff_weighted_d_matrix
Degree of disease measured at two points in time for mine workers.coal_g
Computes the constant of integration of a multinomial sample.constant_of_integration
Ratings of severity of patient's depression by two therapists.depression
Dehydration in dogs data set.dogs
Severity of disturbing dreams in adolescent boys, measured at two ages..dreams
Occurrence of side effects after gastro-intestinal surgery.dumping
Ratings of number of hot drinks consumed by cases with cancer of the esophagus, compared with control subjects.esophageal_cancer
Converts weighted (x, w) pairs into unweighted data by replicating x[i] w[i] timesexpand
Computes the "expit" function - inverse of logit.expit
Family income for two years from US census.family_income
Ratings of visual acuity for men and women employed at the Royal Ordinance factories, 1943-1946.gender_vision
Fits the model where some of the delta parameters are constrained to be equal to one another.Goodman_constrained_diagonals_parameter_symmetry
Fit's Goodman's diagonals parameter symmetry model.Goodman_diagonals_parameter_symmetry
Fits the model with given parameters fixed to specific values.Goodman_fixed_parameter
Performs ML estimation of the model.Goodman_ml
Fits Goodman's (1979) Model IGoodman_model_i
Fits Goodman's (1979) Model I*Goodman_model_i_star
Fits Goodman's (1979) Model IIGoodman_model_ii
Fits Goodman's (1979) model II*, where row and column effects are equal.Goodman_model_ii_star
Fits Goodman's L. A. (1979) Simple Models for the Analysis of Association in Cross-Classifications Having Ordered CategoriesGoodman_null_association
Computes the model-based probability for cell i, jGoodman_pi
Computes the full matrix of model-based cell probabilities.Goodman_pi_matrix
Fits the symmetric association model from Goodman (1979). Note the model is a reparameterized version of the quasi-symmetry model, so the quasi-symmetry model has the same fit indices.Goodman_symmetric_association_model
Fits Goodman's (1979) uniform association modelGoodman_uniform_association
Case where j == r, i == k == k2handle_max_i_i
Case where j == r, i != k, i == k2handle_max_i_k
Case where j == r, i != k && i != k2handle_max_k_k2
Case where pi[i, r] with k and k2handle_one_maximum
Case where i == j, i < r, j < rhandle_tied_below_maximum
Case where pi[r, r] with k and k2handle_tied_maximum
Case where i != j, i < r && j < rhandle_untied_below_maximum
Data about charges of homicide in the state of Florida.homicide_black_black
Data about charges of homicide in the state of Florida.homicide_black_white
Data about charges of homicide in the state of Florida.homicide_white_black
Data about charges of homicide in the state of Florida.homicide_white_white
Measures of men's hypothalamus taken from cadavers. First data set.hypothalamus_1
Measures of men's hypothalamus taken from cadavers. Second data set.hypothalamus_2
Measures of interference in memory recall study.interference_12
Measures of interference in memory recall study.interference_control_1
Measures of interference in memory recall study.interference_control_2
Fits marginal homogeneity modelIreland_marginal_homogeneity
Computes the MDIS between the two matrices provided.Ireland_mdis
Renormalize counts to account for truncation of diagonalIreland_normalize_for_truncation
Fit for quasi-symmetry model. Obtained by subtraction, so no model-based probabilities.Ireland_quasi_symmetry
Fitss the quasi-symmetry model.Ireland_quasi_symmetry_model
Fits symmetry model.Ireland_symmetry
Tests whether a square matrix is invertible (non singular)is_invertible
Determines if its argument is not a valid number.is_missing_or_infinite
Computes Cohen's 1960 kappa coefficientkappa
Computes the likelihood ratio G^2 measure of fit.likelihood_ratio_chisq
Function to load a data set written out using save().loadRData
Computes the multinomial log(likelihood).log_likelihood
Adds indicator variables for the diagonal cells in table n.log_linear_add_all_diagonals
Appends a column to an existing design matrix.log_linear_append_column
Creates missing column nameslog_linear_create_coefficient_names
Creates a vector containing the linear-by-linear vector.log_linear_create_linear_by_linear
Computes the logs of the cell frequencies.log_Linear_create_log_n
Creates design matrix for model with main effects and a single agreement parameter delta.log_linear_equal_weight_agreement_design
Fits a log-linear model to the data provided, using the design matrix provided. Names for the effects will be "rows1", "cols1" etc. If there are remaining entries, they can be specified as the "effect_names" character vector. This function is a wrapper around a call to glm() that handles some of the details of the call and packages the output in a more convenient form.log_linear_fit
Design matrix for baseline independence model with main effects for rows and columns.log_linear_main_effect_design
Converts a matrix of data into a vector suitable for use in analysis with the design matrices created. Unlike simply calling vector() on the matrix the resulting vector is organized by rows, then columns. This order corresponds to the order in the design matrix.log_linear_matrix_to_vector
Creates the design matrix for a quasi-symmetry designlog_linear_quasi_symmetry_model_design
Removes a column from an existing design matrix.log_linear_remove_column
Creates design matrix for symmetry model.log_linear_symmetry_design
Computes the log-odds (logit) for the value providedlogit
Computes sums c+ used in maximizing the log(likelihod)McCullagh_compute_c_plus
Compute the linear constraint on psi elements for identifiablity.McCullagh_compute_condition
Computes cumulative sums for rows,McCullagh_compute_cumulative_sums
Computes the model-based cumulative probability matrices pij and qijMcCullagh_compute_cumulatives
Computes the degrees of freedom for the modelMcCullagh_compute_df
Computes gamma from x and betaMcCullagh_compute_gamma
Computes value of gamma from phi. Inverse of usual computation.McCullagh_compute_gamma_from_phi
Computes value of gamma[j + 1] from phi.McCullagh_compute_gamma_plus_1_from_phi
Coompute the model-based cumulative probabilities pij and qij.McCullagh_compute_generalized_cumulatives
Cpompute matrix pi under generalized model.McCullagh_compute_generalized_pi
Computes lambda, log of cumulative odds.McCullagh_compute_lambda
Computes the log(likelihood) for the general nonlinear model.McCullagh_compute_log_l
Compute the observed sums NijMcCullagh_compute_Nij
Compute the value of the Lagrange multiplier for the constraint on psi.McCullagh_compute_omega
Computes phi based on gammaMcCullagh_compute_phi
Compute matrix of model-based logitsMcCullagh_compute_phi_matrix
Compute the regular (non-cumulative) model-based pi valuesMcCullagh_compute_pi
Computes matrix of p-values pi based on x and current value of beta.McCullagh_compute_pi_from_beta
Compute the cell probabilities pi from gamma.McCullagh_compute_pi_from_gamma
Computes regression weights w; R_dot_j * (N - R_dot_j[j]) * (n_do_j[j] a= na_dot_j[j+ 1] )McCullagh_compute_regression_weights
Compute sums too use in maximizing log(likelihood)McCullagh_compute_s_plus
Compute the Newton-Raphson update.McCullagh_compute_update
Computes Z, where z is w * lambda.McCullagh_compute_z
Fits the McCullagh (1978) conditional-symmetry model.McCullagh_conditional_symmetry
Computes sums used in maximizing theta.McCullagh_conditional_symmetry_compute_s
Initializes symmetry matrix phiMcCullagh_conditional_symmetry_initialize_phi
Maximizes log(likelihood) wrt phi.McCullagh_conditional_symmetry_maximize_phi
Maximizes the log(likelihood) wrt theta.McCullagh_conditional_symmetry_maximize_theta
Computes model-based proportions.McCullagh_conditional_symmetry_pi
Derivative of the condition wrt psi[i, j].McCullagh_derivative_condition_wrt_psi
Derivative of gamma j + 1 wrt phi.McCullagh_derivative_gamma_plus_1_wrt_phi
Derivative of gamma wrt phi.McCullagh_derivative_gamma_wrt_phi
Derivative of y wrt gamma.McCullagh_derivative_gamma_wrt_y
Derivative of Lagrange multiplier wrt scalar delta.McCullagh_derivative_lagrangian_wrt_delta
Derivative of Lagrangian wrt delta_vec.McCullagh_derivative_lagrangian_wrt_delta_vec
Derivative of Lagrangian wrt psi[i1, j1].McCullagh_derivative_lagrangian_wrt_psi
Derivative of log(likelihood) wrt alpha[index].McCullagh_derivative_log_l_wrt_alpha
Derivative of log(likelihood) wrt beta, as given in appendix of McCullagh.McCullagh_derivative_log_l_wrt_beta
Derivative of log(likelihood) wrt c.McCullagh_derivative_log_l_wrt_c
Derivative of log(likelihood) wrt delta (scalar or vector0.McCullagh_derivative_log_l_wrt_delta
Derivative of log(likelihood) wrt delta_vec[k].McCullagh_derivative_log_l_wrt_delta_vec
Derivative of log(likelihood) wrt parameters.McCullagh_derivative_log_l_wrt_params
Derivative of log(likelihood) wrt phi[i, j]McCullagh_derivative_log_l_wrt_phi
Derivative of log(likelihood) wrt psi.McCullagh_derivative_log_l_wrt_psi
Derivative of Lagrange multiplier omega wrt alpha[index].McCullagh_derivative_omega_wrt_alpha
Derivative of Lagrange multiplier omega wrt c.McCullagh_derivative_omega_wrt_c
Derivative of Lagrange multiplier omega wrt scalar delta.McCullagh_derivative_omega_wrt_delta
Derivative of Lagrange multiplier omega wrt vector delta[k].McCullagh_derivative_omega_wrt_delta_vec
Derivative of Lagrange multiplier omega wrt psi[i, j].McCullagh_derivative_omega_wrt_psi
Derivative of phi wrt gamma.McCullagh_derivative_phi_wrt_gamma
Derivative of pi[i, j] wrt alpha[index].McCullagh_derivative_pi_wrt_alpha
Derivative pi[i, j] wrt c.McCullagh_derivative_pi_wrt_c
Derivative of pi[i, j] wrt delta.McCullagh_derivative_pi_wrt_delta
Derivative pi[i, j] wrt delta[k].McCullagh_derivative_pi_wrt_delta_vec
Derivative of pi[i, j] wrt psi[i1, j1].McCullagh_derivative_pi_wrt_psi
Derivative of pij[i, j] wrt alpha[index]McCullagh_derivative_pij_wrt_alpha
Derivative pij[i, j] wrt c.McCullagh_derivative_pij_wrt_c
Derivative of pij[i, j] wrt scalar delta.McCullagh_derivative_pij_wrt_delta
Derivative pij[i,j] wrt vector delta[k].McCullagh_derivative_pij_wrt_delta_vec
Derivative of pij[a, b] wrt psi[h, k]McCullagh_derivative_pij_wrt_psi
Extracts the weights to convert cumulative model-based probabilities to regular probabilities.McCullagh_extract_weights
Fit location modelMcCullagh_fit_location_regression_model
Generalized version of palindromic symmetry modelMcCullagh_generalized_palindromic_symmetry
Computes culuative model probabilities for the generalized model using vector delta.McCullagh_generalized_pij_qij
Generates names to label the parameters.McCullagh_generate_names
Computes summary statistics needed to compute estimate of delta.McCullagh_get_statistics
Gradient vector of log(likelihood)McCullagh_gradient_log_l
Hessian matrix of log(likelihood)McCullagh_hessian_log_l
Initializes the beta vector.McCullagh_initialize_beta
Compute initial values for scalar deltaMcCullagh_initialize_delta
Initialize vector deltaMcCullagh_initialize_delta_vec
Initialize the symmetry matrix psiMcCullagh_initialize_psi
Initialize design matrix for location model.McCullagh_initialize_x
Logical test of whether a specific psi will be in the constraint set.McCullagh_is_in_constraint_set
Test whether pi matrix is valid, i.e., 0 < all values.McCullagh_is_pi_invalid
Computes the log(likelihood).McCullagh_log_L
MCCullagh's logistic model.McCullagh_logistic_model
Computed cumulative logits.McCullagh_logits
Maximize the log(likelihood) wrt parameters phi and alphaMcCullagh_maximize_q_symmetry
Newton-Raphson update.McCullagh_newton_raphson_update
McCullagh's palindromic symmetry modelMcCullagh_palindromic_symmetry
Computes the penalized value of a derivative by adding the derivative of the penalty to it.McCullagh_penalized
Compute model-based cumulative probabilitiesMcCullagh_pij_qij
Computes the proportional hazards.McCullagh_proportional_hazards
Initializes the asymmetry vector alphaMcCullagh_q_symmetry_initialize_alpha
Initializes the phi matrixMcCullagh_q_symmetry_initialize_phi
Computes the model-based p-valuesMcCullagh_q_symmetry_pi
Fits McCullagh's (1978) quasi-symmetry model.McCullagh_quasi_symmetry
Second derivative of Lagrangian wrt psi^2.McCullagh_second_order_lagrangian_wrt_psi_2
Second derivative of Lagrangian wrt psi[i1, j1] and alpha[index].McCullagh_second_order_lagrangian_wrt_psi_alpha
Second derivative of Lagrangian wrt psi[i1, j1] and delta.McCullagh_second_order_lagrangian_wrt_psi_delta
Second derivative of Lagrangian wrt psi[i1, j1] and delta_vec[k[.McCullagh_second_order_lagrangian_wrt_psi_delta_vec
Second derivative of log(likelihood) wrt alpha^2.McCullagh_second_order_log_l_wrt_alpha_2
Second derivative of log(likelihood) wrt alpha[index] and c.McCullagh_second_order_log_l_wrt_alpha_c
Expected values of second order derivatives of log(likelihood) wrt beta.McCullagh_second_order_log_l_wrt_beta_2
Second derivative of log(likelihood) wrt c^2.McCullagh_second_order_log_l_wrt_c_2
Second derivative of log(likelihood) wrt delta^2.McCullagh_second_order_log_l_wrt_delta_2
Second derivative of log(likelihood) wrt delta and alpha[index].McCullagh_second_order_log_l_wrt_delta_alpha
Second derivative of log(likelihood) wrt scalar delta and c.McCullagh_second_order_log_l_wrt_delta_c
Second derivative of log(likelihood) wrt delta_vec^2.McCullagh_second_order_log_l_wrt_delta_vec_2
Second derivative of log(likelihood) wrt delta[k] and alpha[index].McCullagh_second_order_log_l_wrt_delta_vec_alpha
Second derivative of log(likeloihood) wrt delta_vec[k] and c.McCullagh_second_order_log_l_wrt_delta_vec_c
Expected second order derivatives of log(likelihood)McCullagh_second_order_log_l_wrt_parms
Second derivative of log(likelihoood) wrt psi^2.McCullagh_second_order_log_l_wrt_psi_2
Second derivative of log(likelihoood) wrt ps[i1, j1] and alpha[index].McCullagh_second_order_log_l_wrt_psi_alpha
Second derivative of log(likelihood) wrt psi[i1, j1] and c.McCullagh_second_order_log_l_wrt_psi_c
Second derivative of log(likelihood) wrt psi[i1, j1] and scalar delta..McCullagh_second_order_log_l_wrt_psi_delta
Second derivative of log(likelihood) wrt psi[i1, j1] and delta_vec[k].McCullagh_second_order_log_l_wrt_psi_delta_vec
Second derivative of Lagrange multiplier omega wrt alpha^2.McCullagh_second_order_omega_wrt_alpha_2
Second derivative of Lagrange multiplier omega wrt alpha[index] and c.McCullagh_second_order_omega_wrt_alpha_c
Second derivative of Lagrange multiplier omega wrt c^2.McCullagh_second_order_omega_wrt_c_2
Second derivative of Lagrange multiplier omega wrt scalae delta^2.McCullagh_second_order_omega_wrt_delta_2
Second derivative of Lagrange multiplier omega wrt delta and alpha[index].McCullagh_second_order_omega_wrt_delta_alpha
Second derivative of Lagrange multiplier omega wrt scalar delta and c.McCullagh_second_order_omega_wrt_delta_c
Second derivative of Lagrange multiplier omega wrt delta_vec^2.McCullagh_second_order_omega_wrt_delta_vec_2
Second derivative of Lagrange multiplier omega wrt delta_vec[k] and alpha[index].McCullagh_second_order_omega_wrt_delta_vec_alpha
Second derivative of Lagrange multiplier omega wrt delta_vec[k] and c.McCullagh_second_order_omega_wrt_delta_vec_c
Second derivative of Lagrange multiplier omega wrt psi^2.McCullagh_second_order_omega_wrt_psi_2
Second derivative of Lagrange multiplier omega wrt psi[i1, j1] and alpha[index].McCullagh_second_order_omega_wrt_psi_alpha
Second derivative of Lagrange multiplier omega wrt psi[i1, j1] and c.McCullagh_second_order_omega_wrt_psi_c
Second derivative of Lagrange multiplier omega wrt psi and scalar delta.McCullagh_second_order_omega_wrt_psi_delta
Second derivative of Lagrange multiplier omega wrt psi[i1, j1] and delta_vec[k].McCullagh_second_order_omega_wrt_psi_delta_vec
Second derivative of pi[i, j] wrt alpha^2.McCullagh_second_order_pi_wrt_alpha_2
Second derivaitve of pi[i, j] wrt alpha[index] and c.McCullagh_second_order_pi_wrt_alpha_c
Second order derivative of pi[i, j] wrt c^2.McCullagh_second_order_pi_wrt_c_2
Second order derivative of pi[i, j] wrt scalar delta.McCullagh_second_order_pi_wrt_delta_2
Second order deriviative of pi[i, j] wrt scalar delta and alpha[index]McCullagh_second_order_pi_wrt_delta_alpha
Second order derivative of pi[i, j] wrt scalae delta and c.McCullagh_second_order_pi_wrt_delta_c
Derivative of pi[i, j] wrt delta^2.McCullagh_second_order_pi_wrt_delta_vec_2
Second order dertivative of pi[i, j] wrtt delta[k] alpha[index].McCullagh_second_order_pi_wrt_delta_vec_alpha
Second derivative of pi[i, j] wrt delta[k] and c.McCullagh_second_order_pi_wrt_delta_vec_c
Second order derivative wrt psi^2.McCullagh_second_order_pi_wrt_psi_2
Second order derivative of pi[i, j] wrt psi[i1, j1] and alpha[index].McCullagh_second_order_pi_wrt_psi_alpha
Second order derivative of pi[i, j] wrt psi[i1, j1] and c.McCullagh_second_order_pi_wrt_psi_c
Second order derivaitve of pi wrt pshi and scalar delta.McCullagh_second_order_pi_wrt_psi_delta
Second order derivaitve of pi[i, j] wrt psi[i1, j1] and kelta[k].McCullagh_second_order_pi_wrt_psi_delta_vec
Update the parameters based on Newton-Raphson step.McCullagh_update_parameters
Compute v_inverse (from appendix).McCullagh_v_inverse
Relationship between child's mental health and parents' socioeconomic status.mental_health
Computes the column association values theta-hatmodel_i_column_theta
Gets the overall effects for Model I.model_i_effects
Computes model-based expected cell counts for Model Imodel_i_fHat
Normalizes pi(fHat) to sum to 1.0. If exclude_diagonal is TRUE, the sum of the off-diagonal terms sums to 1.0.model_i_normalize_fHat
Computes the table of adjacent odds-ratios theta-hat.model_i_row_column_odds_ratios
Computes the row association values theta-hatmodel_i_row_theta
Gets the Model I* effects.model_i_star_effects
Computes expected frequencies for Model I*model_i_star_fHat
Updates the row/column parameters for Model I*.model_i_star_update_theta
Computes crude starting values for Model I.model_i_starting_values
Updates the estimate of the alpha vector for Model Imodel_i_update_alpha
Updates the estimate of the beta vector for Model Imodel_i_update_beta
Updates the estimate of the delta vector for Model Imodel_i_update_delta
Updates the estimate of the gamma vector for Model Imodel_i_update_gamma
Computes the overall association theta and the row and column effects zetamodel_i_zeta
Gets the effects phi, ksi_i_dot and ksi_dot_j for Model II results.model_ii_effects
Computes expected counts for Model IImodel_ii_fHat
Gets the effects phi, ksi_i_dot and ksi_dot_j for Model II matrix of odds-ratios.model_ii_ksi
Gets the effects for Model II*model_ii_star_effects
Computes expected counts for Model II*model_ii_star_fHat
Updates estimate of phi vectormodel_ii_star_update_phi
Computes crude starting values for Model IImodel_ii_starting_values
Updates the estimate of the alpha vector for Model IImodel_ii_update_alpha
Updates the estimate of the beta vector for Model IImodel_ii_update_beta
Updates the estimate of the rho vector for Model IImodel_ii_update_rho
Updates the estimate of the sigma vector for Model IImodel_ii_update_sigma
Movie ratings by two film critics, Siskel and Ebert.movies
Agreement between two clinicians on presence of multiple sclerosis based on file.new_orleans_data
Computes expected counts for null association modelnull_association_fHat
Cross tabulation of father's employment status with son's employment status.occupational_status
Interrater agreement of two psychologists' ratings of paranoia.paranoia
Computes the Pearson X^2 statistic.pearson_chisq
Interrater agreement of two radiologists diagnosis of severity of carcinoma.radiology
Computes the degrees of freedom for the model.Schuster_compute_df
Compute matrix of model-based proportions pi.Schuster_compute_pi
Computes starting values for the model.Schuster_compute_starting_values
Derivative of log(likelihood) wrt kappa.Schuster_derivative_log_l_wrt_kappa
Derivative of log(likelihood) wrt marginal_pi[k]Schuster_derivative_log_l_wrt_marginal_pi
Derivative of log(likelihood) wrt v[i1, j1]Schuster_derivative_log_l_wrt_v
Derivative of pi[i, j] wrt kappa coefficient.Schuster_derivative_pi_wrt_kappa
Derivative of pi[i, j] wrt marginal_pi[k].Schuster_derivative_pi_wrt_marginal_pi
Computes derivative of pi[i, j] wrt v[i1, j1]Schuster_derivative_pi_wrt_v
Computes derivative of v[i1, j1] wrt v[i2, j2]Schuster_derivative_v_wrt_v
Compute v matrix subject to constraints on rows 1..r-1.Schuster_enforce_constraints_on_v
Gradient vector log(L) wrt parameters.Schuster_gradient
Computes the hessian matrix of second-order partial derivatives of log(L).Schuster_hessian
Determines whether the candidate pi matrix is valid.Schuster_is_pi_valid
Performs Newton-Raphson step.Schuster_newton_raphson
Second order partial log(L) wrt kappa^2.Schuster_second_deriv_log_l_wrt_kappa_2
Second order partial log(L) wrt kappa and v.Schuster_second_deriv_log_l_wrt_kappa_v
Second order partial log(L) wrt marginal_pi^2.Schuster_second_deriv_log_l_wrt_marginal_pi_2
Second order partial log(L) wrt marginal_pi and kappa.Schuster_second_deriv_log_l_wrt_marginal_pi_kappa
Second order partial log(L) wrt marginal_pi and v.Schuster_second_deriv_log_l_wrt_marginal_pi_v
Second order partial log(L) wrt v^2.Schuster_second_deriv_log_l_wrt_v_2
Second order partial wrt kappa, kappaSchuster_second_deriv_pi_wrt_kappa_2
Second order partial wrt kappa, vSchuster_second_deriv_pi_wrt_kappa_v
Second derivative of pi[i, j] wrt marginal_pi[k]^2Schuster_second_deriv_pi_wrt_marginal_pi_2
Second order partial wrt kappa, marginal_piSchuster_second_deriv_pi_wrt_marginal_pi_kappa
Second order partial pi wrt marginal_pi and vSchuster_second_deriv_pi_wrt_marginal_pi_v
Second order partial wrt v^2Schuster_second_deriv_pi_wrt_v_2
Solves for the last row and diagonal of symmetry matrix v (v-tilde) using constraint equationsSchuster_solve_for_v
Solves for the last row and diagonal of symmetry matrix v (parameteer v-tilde) using linear algebra formulation from paper.Schuster_solve_for_v1
Computes the model that has kappa as a coefficient and symmetry.Schuster_symmetric_rater_agreement_model
Computes the Newton-Raphson updateSchuster_update
Computes the common diagonal term v-tilde.Schuster_v_tilde
Social mobility data with father's occupational social status and son's occupational social status.social_status
Social mobility data with father's occupational social status and son's occupational social status. * categories instead of 7 in social status..social_status2
Computes Stuart's Q test of marginal homogeneity.Stuart_marginal_homogeneity
Taste ratingstaste
Teachers ratings of their students intelligence.teachers
Style of teachers rated by supervisorsteaching_style
Relationship between size of child's tonsils and their status as a carrier of a disease.tonsils
Interrater agreement of two journalists' evaluation of proposed TV programs.tv
Computes expected counts for uniform association modeluniform_association_fHat
Updates estimate of theta value of the uniform association modeluniform_association_update_theta
Computes the sampling variance of kappa.var_kappa
Computes the sampling variance of weighted kappa.var_weighted_kappa
Visual acuity of women factory workers.vision_data
Visual acuity of men factory workers.vision_data_men
Fits the diagonal effects model, where each category has its own parameter delta[k].von_Eye_diagonal
Fits the diagonal effects model, where each category has its own parameter delta[k], while also incorporating a linear-by-linear term.von_Eye_diagonal_linear_by_linear
Fits the diagonal effects model, where there is a single delta parameter for all categories, while also incorporating a linear-by-linear term.von_Eye_equal_weight_diagonal_linear
Fits the equal weighted diagonal model, where the diagonals all have an additional parameter delta, with the constraint that delta is equal across all categories.von_Eye_equal_weighted_diagonal
Fits the basic independent rows and columns model incorporating a linear-by-linear term.von_Eye_linear_by_linear
Fits the base model with only independent row and column effects.von_Eye_main_effect
Creates design matrix for weight be response category model.von_Eye_weight_by_response_category_design
Computes the weighted covarianceweighted_cov
Computes Cohen's 1968 weighted kappa coefficientweighted_kappa
Computes the weighted varianceweighted_var
Agreement between two clinicians on presence of multiple sclerosis based on file.winnipeg_data