Ordinal regression tables

library(spicy)
library(MASS)        # polr()

This vignette covers ordinal regression — models for an ordered categorical response such as a Poor < Fair < Good < Very good health rating or a Likert agreement scale. The companion vignette Publication-ready regression tables covers the shared mechanics (vcov, ci_level, output formats, multi-model layouts, broom integration); here we focus on what is specific to ordinal fits.

table_regression() supports both ordinal engines:

MASS::polr() — proportional-odds cumulative logit (also probit / cloglog / cauchit via method =);
ordinal::clm() — the more flexible cumulative link model, with optional scale and partial-proportional-odds (nominal) effects.

The model used throughout is a self-rated-health rating regressed on age, sex, smoking, and physical activity, using the bundled sochealth survey data (?sochealth). self_rated_health is an ordered factor:

levels(sochealth$self_rated_health)
#> [1] "Poor"      "Fair"      "Good"      "Very good"

Check this before fitting anything. polr() and clm() estimate on the factor’s level order as given — they do not warn when the outcome is a plain (unordered) factor, or when the levels sit in the wrong order. Data read from text files is particularly exposed: factor() defaults to alphabetical levels (here Fair, Good, Poor, Very good), which would silently fit a nonsensical scale. And reversing the order negates every coefficient and marginal effect. Every interpretation below hinges on knowing the direction of the scale — here worst to best (Long & Freese 2014).

The proportional-odds model

A cumulative logit model with \(K\) ordered response categories estimates one slope per predictor (shared across all \(K-1\) cumulative splits — the proportional-odds assumption) plus \(K-1\) ordered thresholds (cut-points). A positive location (proportional-odds) slope means that higher values of the predictor push the response toward higher categories. There is deliberately no per-category coefficient: the proportional-odds restriction collapses the effect into a single slope per predictor, trading per-category detail for one interpretable number. The per-category structure reappears in the marginal effects (below), where a one-unit change has a different effect on the probability of each category — and in the relaxations of the shared-slope restriction that ordinal::clm() offers (tested and demonstrated below).

Basic table

Pass a fitted polr() object. table_regression() renders the shared slope coefficients with a Wald-\(z\) inference regime, followed by a subordinate Thresholds block listing the ordered cut-points with their own B / SE / CI / p (the field convention: summary.polr(), SPSS PLUM, SAS, Stata ologit, Bender & Grouven 1997):

fit <- polr(
  self_rated_health ~ age + sex + smoking + physical_activity,
  data = sochealth, Hess = TRUE
)
table_regression(fit)
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │    B      SE       95% CI        p   
#> ────────────────────┼──────────────────────────────────────
#>  age                │   -0.00  0.00  [-0.01,  0.01]   .831 
#>  sex:               │                                      
#>    Female (ref.)    │     –     –          –          –    
#>    Male             │    0.02  0.11  [-0.20,  0.23]   .874 
#>  smoking:           │                                      
#>    No (ref.)        │     –     –          –          –    
#>    Yes              │   -0.27  0.14  [-0.53, -0.00]   .047 
#>  physical_activity: │                                      
#>    No (ref.)        │     –     –          –          –    
#>    Yes              │    0.03  0.11  [-0.19,  0.24]   .794 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                      
#>    Poor | Fair      │   -2.98  0.24  [-3.45, -2.52]  <.001 
#>    Fair | Good      │   -1.02  0.21  [-1.43, -0.62]  <.001 
#>    Good | Very good │    1.04  0.21  [ 0.64,  1.45]  <.001 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                                 
#>  R² (McFadden)      │    0.00                              
#>  R² (Nagelkerke)    │    0.00                              
#>  AIC                │ 2761.2                               
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: Wald asymptotic (z).
#> Thresholds: latent-scale category cut-points.

Reading the table:

Each predictor has one coefficient row (proportional odds); factor predictors are grouped under their parent variable with the reference level carrying (ref.) and an en dash.
Inference is Wald-\(z\) (df = Inf): ordinal MLE has no residual degrees of freedom, matching summary.polr(), Stata ologit, and SPSS PLUM.
A subordinate Thresholds block lists the cut-points (Poor | Fair, Fair | Good, Good | Very good) with B / SE / CI / p, like the predictor rows. They locate the category boundaries on the latent logit scale and replace the single intercept of a binary logit. They are reported on the log-odds scale and are never exponentiated. The \(z\)-test against zero is rarely of interest — it only asks whether the cumulative split sits at 50/50 at the profile where every predictor equals zero, here a respondent aged 0, not a meaningful case — so do not over-read a “significant” threshold; the substance lies in the odds ratios and marginal effects below. Hide the block with show_thresholds = FALSE to fall back to a compact footer line.
Below the fit-stats rule, the model-fit block reports N, two pseudo-\(R^2\) (McFadden, the Stata ologit default, and Nagelkerke, the SPSS PLUM default), and AIC. Both compare the fitted log-likelihood to the intercept-only model’s, and both round to 0.00 here: these four predictors explain almost none of the variation in self-rated health. This survey model therefore illustrates table mechanics and interpretation wording; read the exemplary sentences below as templates, not as substantive findings. Override the block with show_fit_stats (e.g. show_fit_stats = c("nobs", "AIC", "BIC")).

Odds ratios: `exponentiate = TRUE`

On the logit scale a coefficient is a log cumulative-odds ratio. exponentiate = TRUE reports odds ratios instead, exponentiating the estimate and its CI bounds and relabelling the column header accordingly. This is link-specific: under method = "cloglog" the exponentiated coefficient is a hazard ratio (the grouped-time proportional-hazards reading) and the header and footer relabel to HR; under probit or cauchit the exponential has no ratio interpretation, and exponentiate = TRUE is refused with a clear error.

table_regression(fit, exponentiate = TRUE)
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │   OR      SE       95% CI        p   
#> ────────────────────┼──────────────────────────────────────
#>  age                │    1.00  0.00  [ 0.99,  1.01]   .831 
#>  sex:               │                                      
#>    Female (ref.)    │     –     –          –          –    
#>    Male             │    1.02  0.11  [ 0.82,  1.26]   .874 
#>  smoking:           │                                      
#>    No (ref.)        │     –     –          –          –    
#>    Yes              │    0.76  0.10  [ 0.59,  1.00]   .047 
#>  physical_activity: │                                      
#>    No (ref.)        │     –     –          –          –    
#>    Yes              │    1.03  0.11  [ 0.83,  1.28]   .794 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                      
#>    Poor | Fair      │   -2.98  0.24  [-3.45, -2.52]  <.001 
#>    Fair | Good      │   -1.02  0.21  [-1.43, -0.62]  <.001 
#>    Good | Very good │    1.04  0.21  [ 0.64,  1.45]  <.001 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                                 
#>  R² (McFadden)      │    0.00                              
#>  R² (Nagelkerke)    │    0.00                              
#>  AIC                │ 2761.2                               
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: Wald asymptotic (z).
#> Thresholds: latent-scale category cut-points (log-odds scale, not exponentiated).
#> OR = odds ratio.
#> Coefficients exponentiated and displayed as OR; SE on the OR scale (delta method); CI bounds exponentiated (asymmetric).

The OR here is a cumulative odds ratio: it multiplies the odds of scoring above any given cut-point rather than at or below it, and — because the model is proportional-odds — the same ratio applies at every cut-point (Poor | Fair, Fair | Good, Good | Very good). It is not the odds of landing in one specific category; that per-category detail is what the AME matrix below provides. For a location (proportional) coefficient, an OR above 1 raises the cumulative odds of being in a higher health category and an OR below 1 lowers them — the non-proportional block later in this vignette follows the opposite convention. Exact reading for the one borderline effect here: smoking’s OR of 0.76 means that, at every cut-point, smokers have about 24% lower odds of being in a higher health category rather than a lower one, adjusting for age, sex, and physical activity. The CI upper bound prints as 1.00 but is 0.997 before rounding; with p = .047 this is a borderline association, so treat the sentence as a wording template. The Thresholds rows stay on the log-odds scale (a cut-point is not an odds ratio, so it is never exponentiated); only the predictor coefficients become ORs, and the footer flags this.

Average marginal effects: a probability matrix

Odds ratios are multiplicative and describe the (cumulative) odds, not probabilities directly. For a reader-friendly, probability-scale summary, request average marginal effects (AME). Because an ordinal model predicts a probability for every response category, each predictor has one AME per category — the effect of the predictor on the probability of that category. table_regression() lays these out as the field-standard matrix: predictors in rows, response categories in columns (Long & Freese 2014; Williams 2012).

table_regression(fit, show_columns = c("b", "ame"))
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │    B     AME Poor  AME Fair  AME Good  AME Very good 
#> ────────────────────┼──────────────────────────────────────────────────────
#>  age                │   -0.00      0.00      0.00     -0.00          -0.00 
#>  sex:               │                                                      
#>    Female (ref.)    │     –                                                
#>    Male             │    0.02     -0.00     -0.00      0.00           0.00 
#>  smoking:           │                                                      
#>    No (ref.)        │     –                                                
#>    Yes              │   -0.27      0.01      0.04     -0.01          -0.05 
#>  physical_activity: │                                                      
#>    No (ref.)        │     –                                                
#>    Yes              │    0.03     -0.00     -0.00      0.00           0.01 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                                      
#>    Poor | Fair      │   -2.98                                              
#>    Fair | Good      │   -1.02                                              
#>    Good | Very good │    1.04                                              
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                                                 
#>  R² (McFadden)      │    0.00                                              
#>  R² (Nagelkerke)    │    0.00                                              
#>  AIC                │ 2761.2                                               
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: Wald asymptotic (z).
#> Thresholds: latent-scale category cut-points.
#> AME = average marginal effect on a response-category probability.

How to read it, with the smoking = Yes row:

The four AME ... columns are the average change in the probability of each health category for a smoker versus a non-smoker, holding the other predictors at their observed values.
The effects sum to ≈ 0 across the row: a predictor redistributes probability mass between categories (the total probability is always 1). Here smoking moves mass out of the better categories and into the worse ones — about 4.8 points out of Very good, 4.2 into Fair.
Cells are probabilities (the 0–1 scale, matching marginaleffects::avg_slopes() and the binary-glm AME). An AME of 0.04 is a change of 4 percentage points — not 4 percent. The footer note states this; reserve “percent” for multiplicative quantities such as odds ratios.

An AME is the single-number summary of a fuller object: the predicted probability of each category as the predictor varies. When the story deserves more than one number per category — or to check that an average is not masking a non-monotone pattern — tabulate those probabilities directly:

marginaleffects::avg_predictions(fit, by = "smoking")
#> 
#>      Group smoking Estimate Std. Error     z Pr(>|z|)     S  2.5 % 97.5 %
#>  Poor          No    0.0490    0.00635  7.71   <0.001  46.2 0.0365 0.0614
#>  Poor          Yes   0.0632    0.00995  6.35   <0.001  32.1 0.0437 0.0827
#>  Fair          No    0.2189    0.01276 17.15   <0.001 216.7 0.1939 0.2440
#>  Fair          Yes   0.2608    0.02172 12.01   <0.001 107.9 0.2182 0.3033
#>  Good          No    0.4746    0.01473 32.22   <0.001 754.4 0.4457 0.5034
#>  Good          Yes   0.4666    0.01562 29.87   <0.001 648.7 0.4360 0.4972
#>  Very good     No    0.2575    0.01397 18.44   <0.001 249.8 0.2301 0.2849
#>  Very good     Yes   0.2094    0.02128  9.84   <0.001  73.5 0.1677 0.2511
#> 
#> Type: probs

Smokers’ expected distribution sits lower on the scale than non-smokers’ — Very good 20.9% versus 25.8%, Fair 26.1% versus 21.9% — and the smoking AME row above is exactly the difference between these two profiles. For predicted-probability curves along a continuous predictor, marginaleffects::plot_predictions() plots the same quantities.

Request only the AME matrix (drop the coefficient column) with show_columns = "ame", or add the AME standard errors / CIs / p-values with "ame_se", "ame_ci", "ame_p" (or the "all_ame" bundle):

table_regression(fit, show_columns = "ame")
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │ AME Poor  AME Fair  AME Good  AME Very good 
#> ────────────────────┼─────────────────────────────────────────────
#>  age                │     0.00      0.00     -0.00          -0.00 
#>  sex:               │                                             
#>    Female (ref.)    │                                             
#>    Male             │    -0.00     -0.00      0.00           0.00 
#>  smoking:           │                                             
#>    No (ref.)        │                                             
#>    Yes              │     0.01      0.04     -0.01          -0.05 
#>  physical_activity: │                                             
#>    No (ref.)        │                                             
#>    Yes              │    -0.00     -0.00      0.00           0.01 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │  1156                                       
#>  R² (McFadden)      │     0.00                                    
#>  R² (Nagelkerke)    │     0.00                                    
#>  AIC                │  2761.2                                     
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: Wald asymptotic (z).
#> Thresholds: Poor|Fair = -2.98, Fair|Good = -1.02, Good|Very good = 1.04.
#> AME = average marginal effect on a response-category probability.

Marginal effects are the response-scale (probability) output of marginaleffects::avg_slopes(). For a single-outcome model (binary glm, lm, mixed) the AME stays a single column — the per-category matrix appears only when the response has more than two categories.

Cluster-robust standard errors

Ordinal fits honour the cluster-robust vcov family ("CR0"–"CR3") via sandwich::vcovCL(). Pass the cluster as a formula, a column name, or a vector (see the main vignette, How to specify cluster):

table_regression(
  fit, vcov = "CR2", cluster = ~region,
  show_columns = c("b", "ame")
)
#> Registered S3 method overwritten by 'clubSandwich':
#>   method    from    
#>   bread.mlm sandwich
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │    B     AME Poor  AME Fair  AME Good  AME Very good 
#> ────────────────────┼──────────────────────────────────────────────────────
#>  age                │   -0.00      0.00      0.00     -0.00          -0.00 
#>  sex:               │                                                      
#>    Female (ref.)    │     –                                                
#>    Male             │    0.02     -0.00     -0.00      0.00           0.00 
#>  smoking:           │                                                      
#>    No (ref.)        │     –                                                
#>    Yes              │   -0.27      0.01      0.04     -0.01          -0.05 
#>  physical_activity: │                                                      
#>    No (ref.)        │     –                                                
#>    Yes              │    0.03     -0.00     -0.00      0.00           0.01 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                                      
#>    Poor | Fair      │   -2.98                                              
#>    Fair | Good      │   -1.02                                              
#>    Good | Very good │    1.04                                              
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                                                 
#>  R² (McFadden)      │    0.00                                              
#>  R² (Nagelkerke)    │    0.00                                              
#>  AIC                │ 2761.2                                               
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: cluster-robust (CL), clusters by region.
#> Thresholds: latent-scale category cut-points.
#> AME = average marginal effect on a response-category probability.

The footer switches to name the estimator and the clustering variable. Heteroskedasticity-consistent (HC*) estimators and the bootstrap / jackknife resamplers are not defined for ordinal fits and are refused with a clear spicy_unsupported_vcov error rather than a silent fallback.

Standard errors and confidence intervals

The three inference regimes differ in how the standard error and the confidence interval relate:

Wald (default): both come from the model information matrix. The CI is estimate ± z × SE (symmetric) and the p-value uses the same SE — SE, CI and p are one coherent set.
Robust / cluster-robust (vcov = "CR*"): the whole set switches to the sandwich estimator — SE, CI (± z × SE_robust) and p shift together, still coupled.
Profile likelihood (ci_method = "profile"): the CI is inverted from the likelihood-ratio statistic and is asymmetric — not estimate ± z × SE. Profile is a CI-only refinement: the estimate, SE, statistic and p-value stay Wald; only the CI changes. It covers the predictor coefficients (via confint()); the thresholds stay Wald. A robust vcov takes precedence (profile is model-based), so requesting both uses the robust Wald CIs.

Because a profile CI cannot be reconstructed from the SE, the footer discloses it (95% CIs: profile likelihood.) alongside the SE method — following APA 7 / SAMPL / STROBE and matching parameters::model_parameters():

table_regression(fit, ci_method = "profile", show_columns = c("b", "ci", "p"))
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │    B         95% CI        p   
#> ────────────────────┼────────────────────────────────
#>  age                │   -0.00  [-0.01,  0.01]   .831 
#>  sex:               │                                
#>    Female (ref.)    │     –          –          –    
#>    Male             │    0.02  [-0.20,  0.23]   .874 
#>  smoking:           │                                
#>    No (ref.)        │     –          –          –    
#>    Yes              │   -0.27  [-0.53, -0.00]   .047 
#>  physical_activity: │                                
#>    No (ref.)        │     –          –          –    
#>    Yes              │    0.03  [-0.19,  0.24]   .794 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                
#>    Poor | Fair      │   -2.98  [-3.45, -2.52]  <.001 
#>    Fair | Good      │   -1.02  [-1.43, -0.62]  <.001 
#>    Good | Very good │    1.04  [ 0.64,  1.45]  <.001 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                           
#>  R² (McFadden)      │    0.00                        
#>  R² (Nagelkerke)    │    0.00                        
#>  AIC                │ 2761.2                         
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: Wald asymptotic (z).
#> 95% CIs: profile likelihood.
#> Thresholds: latent-scale category cut-points.

The `ordinal::clm()` engine

ordinal::clm() is rendered through the same code path and supports the same columns. It is the engine to reach for when you need flexible link functions, scale effects, or a relaxation of proportional odds.

clm_fit <- ordinal::clm(
  self_rated_health ~ age + sex + smoking + physical_activity,
  data = sochealth
)
table_regression(clm_fit, show_columns = c("b", "ame"))
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │    B     AME Poor  AME Fair  AME Good  AME Very good 
#> ────────────────────┼──────────────────────────────────────────────────────
#>  age                │   -0.00      0.00      0.00     -0.00          -0.00 
#>  sex:               │                                                      
#>    Female (ref.)    │     –                                                
#>    Male             │    0.02     -0.00     -0.00      0.00           0.00 
#>  smoking:           │                                                      
#>    No (ref.)        │     –                                                
#>    Yes              │   -0.27      0.01      0.04     -0.01          -0.05 
#>  physical_activity: │                                                      
#>    No (ref.)        │     –                                                
#>    Yes              │    0.03     -0.00     -0.00      0.00           0.01 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                                      
#>    Poor | Fair      │   -2.98                                              
#>    Fair | Good      │   -1.02                                              
#>    Good | Very good │    1.04                                              
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                                                 
#>  R² (McFadden)      │    0.00                                              
#>  R² (Nagelkerke)    │    0.00                                              
#>  AIC                │ 2761.2                                               
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: Wald asymptotic (z).
#> Thresholds: latent-scale category cut-points.
#> AME = average marginal effect on a response-category probability.

Testing proportional odds

The shared-slope restriction is a testable hypothesis, and the workflow is: fit, test, then relax only what fails (Long & Freese 2014). ordinal provides a likelihood-ratio test per predictor: nominal_test() refits the model freeing each predictor across the cut-points and compares, and scale_test() does the same for a scale (dispersion) effect. For polr there is no built-in equivalent — the classic logit-specific check is Brant’s (1990) test (package brant); clm’s tests are the likelihood-based generalisation.

ordinal::nominal_test(clm_fit)
#> Tests of nominal effects
#> 
#> formula: self_rated_health ~ age + sex + smoking + physical_activity
#>                   Df  logLik    AIC    LRT Pr(>Chi)  
#> <none>               -1373.6 2761.2                  
#> age                2 -1373.6 2765.1 0.0526  0.97407  
#> sex                2 -1373.2 2764.4 0.7847  0.67547  
#> smoking            2 -1371.0 2760.0 5.1474  0.07625 .
#> physical_activity  2 -1373.2 2764.4 0.8015  0.66980  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ordinal::scale_test(clm_fit)
#> Tests of scale effects
#> 
#> formula: self_rated_health ~ age + sex + smoking + physical_activity
#>                   Df  logLik    AIC     LRT Pr(>Chi)
#> <none>               -1373.6 2761.2                 
#> age                1 -1373.6 2763.2 0.02185   0.8825
#> sex                1 -1373.5 2763.1 0.13106   0.7173
#> smoking            1 -1372.8 2761.6 1.55287   0.2127
#> physical_activity  1 -1373.5 2762.9 0.28934   0.5906

Each nominal_test row asks: does freeing this predictor across the \(K-1\) cut-points (here 2 extra parameters) improve the fit? No term clearly fails. Smoking is the one borderline case (LRT = 5.15, df = 2, p = .076); age, sex and physical activity are firmly consistent with proportional odds (p > .66), and no term shows a scale effect (p > .21). The decision rule is to relax only the terms that fail — here none clearly does, so the proportional-odds table above is adequate as the primary analysis. Two caveats temper the rule: at moderate sample sizes these per-term tests have limited power, and one test per predictor invites multiplicity — so a borderline result argues for reporting the relaxed fit as a sensitivity analysis, not for switching automatically. In large samples the opposite failure dominates: the tests reject routinely — Long & Freese (2014) report rejection in the majority of real applications — while also being sensitive to other kinds of misspecification, and the freed model’s predictions often barely differ from the proportional fit’s. When a test rejects, compare predicted probabilities across the two fits before restructuring the model. And a violated assumption is never a rationale for linear regression on the ordinal outcome, whose assumptions are stronger still (Long & Freese 2014). The next two sections demonstrate the two relaxations, using smoking precisely because it is the borderline term.

Partial proportional odds: `nominal = ~`

A nominal component frees a predictor’s effect across the cut-points: a separate coefficient per cut-point instead of one shared slope — Peterson & Harrell’s (1990) partial proportional odds. These render as a labelled Non-proportional effects block — one row per cut-point — between the proportional coefficients and the thresholds:

clm_npo <- ordinal::clm(
  self_rated_health ~ age, nominal = ~ smoking,
  data = sochealth
)
table_regression(clm_npo)
#> Cumulative logit regression (partial proportional odds): self_rated_health
#> 
#>  Variable                        │    B      SE       95% CI        p   
#> ─────────────────────────────────┼──────────────────────────────────────
#>  age                             │   -0.00  0.00  [-0.01,  0.01]   .844 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Non-proportional effects:       │                                      
#>    smokingYes @ Poor | Fair      │    0.82  0.28  [ 0.28,  1.37]   .003 
#>    smokingYes @ Fair | Good      │    0.22  0.16  [-0.09,  0.53]   .160 
#>    smokingYes @ Good | Very good │    0.24  0.17  [-0.10,  0.58]   .165 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:                     │                                      
#>    Poor | Fair                   │   -3.17  0.25  [-3.65, -2.69]  <.001 
#>    Fair | Good                   │   -1.03  0.20  [-1.42, -0.65]  <.001 
#>    Good | Very good              │    1.03  0.20  [ 0.64,  1.41]  <.001 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                               │ 1156                                 
#>  R² (McFadden)                   │    0.00                              
#>  R² (Nagelkerke)                 │    0.01                              
#>  AIC                             │ 2756.1                               
#> 
#> Note. Cumulative logit regression (partial proportional odds).
#> Std. errors: Wald asymptotic (z).
#> Thresholds: latent-scale category cut-points.

The freed structure is read within the block: the smoking effect is concentrated at the lowest split (Poor | Fair: B = 0.82, p = .003) and near-null at the two higher splits (B = 0.22 and 0.24, p > .16). That heterogeneity is what the shared slope averaged away, and what relaxing proportional odds buys.

One trap, and it matters: nominal coefficients carry the opposite sign to location coefficients. clm folds nominal terms into the threshold side of the model — they shift the cut-points \(\theta_j\) rather than enter the slope — so a positive nominal coefficient raises the cumulative odds of being at or below that cut-point, pushing toward the lower categories there. Under exponentiate = TRUE the smoking row at Poor | Fair becomes OR = 2.28: a smoker’s odds of rating health Poor (rather than Fair or better) are 2.28 times a non-smoker’s. That is the same worsening of health the proportional fit expressed as OR = 0.76 (< 1) on the higher-category side — reverse polarity, because the nominal term enters the threshold, not the slope. Do not apply the earlier location-coefficient sign rules to this block.

Two caveats for the non-proportional terms, both surfaced by the table: robust / cluster SEs are not available (sandwich has no estimating-functions method, so a robust vcov is refused), and ci_method = "profile" covers the proportional coefficients only (the non-proportional and threshold rows stay Wald).

Scale effects: `scale = ~`

A scale component lets the dispersion of the latent response depend on covariates, relaxing the constant-variance assumption of the basic model. The motivating question is about spread, not location: is latent health more variable among smokers than non-smokers? Test it like any nested pair:

clm_loc <- ordinal::clm(
  self_rated_health ~ age + smoking,
  data = sochealth
)
clm_scale <- ordinal::clm(
  self_rated_health ~ age + smoking, scale = ~ smoking,
  data = sochealth
)
anova(clm_loc, clm_scale)
#> Likelihood ratio tests of cumulative link models:
#>  
#>           formula:                          scale:   link: threshold:
#> clm_loc   self_rated_health ~ age + smoking ~1       logit flexible  
#> clm_scale self_rated_health ~ age + smoking ~smoking logit flexible  
#> 
#>           no.par    AIC  logLik LR.stat df Pr(>Chisq)
#> clm_loc        5 2757.3 -1373.6                      
#> clm_scale      6 2757.8 -1372.9   1.506  1     0.2197

No evidence here (LR = 1.51, df = 1, p = .220). The scale coefficient lives on the log standard-deviation metric: the estimate of 0.09 says smokers’ latent SD is exp(0.09) ≈ 1.10 — about 10% larger — but the CI includes 0 on the log scale, so the data cannot distinguish it from equal spread. The table renders the location coefficients, a subordinate Scale effects block for the scale coefficients, and the thresholds; the footer states the log-SD scale. Scale rows are never exponentiated — their exponential is a ratio of latent standard deviations, not an odds ratio:

table_regression(clm_scale)
#> Cumulative logit regression (proportional odds): self_rated_health
#> 
#>  Variable           │    B      SE       95% CI        p   
#> ────────────────────┼──────────────────────────────────────
#>  age                │   -0.00  0.00  [-0.01,  0.01]   .884 
#>  smoking:           │                                      
#>    No (ref.)        │     –     –          –          –    
#>    Yes              │   -0.27  0.14  [-0.55,  0.01]   .060 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Scale effects:     │                                      
#>    smokingYes       │    0.09  0.08  [-0.06,  0.24]   .224 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                      
#>    Poor | Fair      │   -3.06  0.24  [-3.53, -2.60]  <.001 
#>    Fair | Good      │   -1.05  0.20  [-1.44, -0.66]  <.001 
#>    Good | Very good │    1.05  0.20  [ 0.66,  1.45]  <.001 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                                 
#>  R² (McFadden)      │    0.00                              
#>  R² (Nagelkerke)    │    0.01                              
#>  AIC                │ 2757.8                               
#> 
#> Note. Cumulative logit regression (proportional odds).
#> Std. errors: Wald asymptotic (z).
#> Thresholds: latent-scale category cut-points.
#> Scale effects: covariate effects on the log standard deviation of the latent response.

Cluster-robust SEs are not available for scale fits (sandwich has no estimating-functions method for them), so CR* is refused up front with a clear spicy_unsupported_vcov error:

table_regression(clm_scale, vcov = "CR2", cluster = ~region)
#> Error in `validate_vcov_cluster_lists()`:
#> ! `vcov = "CR2"` is not available for `clm` models.
#> ℹ This class supports: classical. Robust standard errors for more model classes are being added; see ?table_regression.

Choosing an ordinal specification

The driving question is what to do when proportional odds fails — or was never plausible.

When the tests pass, the shared-slope cumulative model is the most parsimonious choice: one slope per predictor, one table row each.
When a few predictors fail, free just those with nominal = ~ and report the freed fit, at least as a sensitivity analysis.
When the assumption fails broadly, or the ordering of the response is itself doubtful, abandon ordinality for multinomial logit — the standard unconstrained alternative (Long & Freese 2014), compared to the ordinal fit by AIC — covered in the multinomial vignette.
For asymmetric responses and grouped survival times, the cloglog link (method = "cloglog" in polr, link = "cloglog" in clm) replaces the odds-ratio reading with a hazard-ratio one, connecting the link menu above to a modelling rationale.

Several models side by side

The same fit can appear several times with different estimators, or different fits can be compared, with table_regression(list(...)). Per-model vcov is supplied as a list:

fit_min <- polr(self_rated_health ~ smoking, data = sochealth, Hess = TRUE)
table_regression(
  list(Unadjusted = fit_min, Adjusted = fit),
  show_columns = c("b", "ame_p")
)
#> Cumulative logit regression (proportional odds) comparison: self_rated_health
#> 
#>                                        Unadjusted                   Adjust… 
#>                       ────────────────────────────────────────────  ─────── 
#>  Variable           │    B     p Poor  p Fair  p Good  p Very good     B    
#> ────────────────────┼───────────────────────────────────────────────────────
#>  smoking:           │                                                       
#>    No (ref.)        │     –                                             –   
#>    Yes              │   -0.27    .067    .050    .218         .037    -0.27 
#>  age                │                                                 -0.00 
#>  sex:               │                                                       
#>    Female (ref.)    │                                                   –   
#>    Male             │                                                  0.02 
#>  physical_activity: │                                                       
#>    No (ref.)        │                                                   –   
#>    Yes              │                                                  0.03 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                                       
#>    Poor | Fair      │   -2.97                                         -2.98 
#>    Fair | Good      │   -1.01                                         -1.02 
#>    Good | Very good │    1.06                                          1.04 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  n                  │ 1156                                          1156    
#>  R² (McFadden)      │    0.00                                          0.00 
#>  R² (Nagelkerke)    │    0.00                                          0.00 
#>  AIC                │ 2755.3                                        2761.2  
#> 
#>                                    Adjusted               
#>                       ─────────────────────────────────── 
#>  Variable           │ p Poor  p Fair  p Good  p Very good 
#> ────────────────────┼─────────────────────────────────────
#>  smoking:           │                                     
#>    No (ref.)        │                                     
#>    Yes              │   .069    .052    .221         .038 
#>  age                │   .831    .831    .833         .831 
#>  sex:               │                                     
#>    Female (ref.)    │                                     
#>    Male             │   .874    .874    .875         .875 
#>  physical_activity: │                                     
#>    No (ref.)        │                                     
#>    Yes              │   .794    .794    .794         .794 
#> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
#>  Thresholds:        │                                     
#>    Poor | Fair      │                                     
#>    Fair | Good      │                                     
#>    Good | Very good │                                     
#> 
#> Note. Cumulative logit regression (proportional odds) models.
#> Std. errors: Wald asymptotic (z).
#> Thresholds: latent-scale category cut-points.
#> AME = average marginal effect on a response-category probability.

Output formats

The default console table shown above is one of several targets. The output argument also produces a raw data frame, a long broom-style tibble, and — with the corresponding Suggests package — rich gt, flextable, tinytable, Excel, or Word tables. The structure (the per-category AME matrix included) carries through to every format.

head(table_regression(fit, show_columns = c("b", "ame"), output = "data.frame"))
#>          Variable       B AME Poor AME Fair AME Good AME Very good
#> 1             age   -0.00     0.00     0.00    -0.00         -0.00
#> 2            sex:                                                 
#> 3   Female (ref.)     –                                           
#> 4            Male    0.02    -0.00    -0.00     0.00          0.00
#> 5        smoking:                                                 
#> 6       No (ref.)     –

table_regression(fit, show_columns = c("b", "ame"), output = "gt")

Variable	B	AME Poor	AME Fair	AME Good	AME Very good
Cumulative logit regression (proportional odds): self_rated_health

age	-0.00	0.00	0.00	0.00	0.00
sex:
Female (ref.)	–	–	–	–	–
Male	0.02	-0.00	-0.00	-0.00	-0.00
smoking:
No (ref.)	–	–	–	–	–
Yes	-0.27	0.01	0.01	0.01	0.01
physical_activity:
No (ref.)	–	–	–	–	–
Yes	0.03	-0.00	-0.00	-0.00	-0.00
Thresholds:
Poor \| Fair	-2.98
Fair \| Good	-1.02
Good \| Very good	1.04
n	1156
R² (McFadden)	0.00
R² (Nagelkerke)	0.00
AIC	2761.2

Note. Cumulative logit regression (proportional odds). Std. errors: Wald asymptotic (z). Thresholds: latent-scale category cut-points. AME = average marginal effect on a response-category probability.

broom::tidy() returns the long frame, one row per (term, estimate_type, outcome_level); each per-category AME row is labelled by its response category in the outcome_level column:

broom::tidy(table_regression(fit, show_columns = c("b", "ame")))
#> # A tibble: 23 × 16
#>    model_id outcome         outcome_level term  estimate_type estimate std.error
#>    <chr>    <chr>           <chr>         <chr> <chr>            <dbl>     <dbl>
#>  1 M1       self_rated_hea… Poor          age   ame            3.91e-5 0.000183 
#>  2 M1       self_rated_hea… Fair          age   ame            1.20e-4 0.000565 
#>  3 M1       self_rated_hea… Good          age   ame           -1.20e-5 0.0000570
#>  4 M1       self_rated_hea… Very good     age   ame           -1.47e-4 0.000692 
#>  5 M1       self_rated_hea… NA            age   B             -7.94e-4 0.00372  
#>  6 M1       self_rated_hea… Poor          sexM… ame           -8.52e-4 0.00539  
#>  7 M1       self_rated_hea… Fair          sexM… ame           -2.62e-3 0.0166   
#>  8 M1       self_rated_hea… Good          sexM… ame            2.62e-4 0.00166  
#>  9 M1       self_rated_hea… Very good     sexM… ame            3.21e-3 0.0204   
#> 10 M1       self_rated_hea… NA            sexM… B              1.73e-2 0.110    
#> # ℹ 13 more rows
#> # ℹ 9 more variables: conf.low <dbl>, conf.high <dbl>, statistic <dbl>,
#> #   df <dbl>, p.value <dbl>, test_type <chr>, is_intercept <lgl>,
#> #   factor_term <chr>, factor_level <chr>

References

Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley.
Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46(4), 1171–1178.
Long, J. S., & Freese, J. (2014). Regression Models for Categorical Dependent Variables Using Stata (3rd ed.). Stata Press.
Peterson, B., & Harrell, F. E. (1990). Partial proportional odds models for ordinal response variables. Journal of the Royal Statistical Society: Series C (Applied Statistics), 39(2), 205–217.
Williams, R. (2012). Using the margins command to estimate and interpret adjusted predictions and marginal effects. The Stata Journal, 12(2), 308–331.
Arel-Bundock, V., Greifer, N., & Heiss, A. (2024). How to Interpret Statistical Models Using marginaleffects in R and Python. Journal of Statistical Software.