# Publication-ready regression tables

    library(spicy)

`table_regression()` produces a coefficient summary table from one or several fitted `lm()` or `glm()` models. The output is publication-ready by default and follows APA Manual 7 (American Psychological Association 2020, Tables 7.13–7.15) formatting conventions: paired estimate-and-CI columns, APA p-values without leading zero, factor levels grouped under their parent variable, fit statistics at the foot of the table, and a self-documenting note line that names the variance estimator and any methodological choice that affected the rendered values.

The function is fit-first: you pass already-fitted models, not raw data and a formula. The same object exports cleanly to a long broom-canonical frame (`broom::tidy()`) and to a one-row-per-model glance summary (`broom::glance()`).

`lm` and `glm` are both supported. The Generalised linear models section covers the glm-specific argument semantics; everything else applies to both. Mixed-effects models are on the roadmap.

## Basic usage

Pass a fitted `lm()` object. The default rendering returns a single-model table with `B`, `SE`, `95% CI`, and `p` columns and a fit-statistics footer (`n`, `R²`, `Adj.R²`):

    fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth)
    table_regression(fit)
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B      SE       95% CI        p
    #> ─────────────────┼──────────────────────────────────────
    #>  (Intercept)     │   65.20  1.66  [61.95, 68.45]  <.001
    #>  age             │    0.05  0.03  [-0.01,  0.11]   .130
    #>  sex:            │
    #>    Female (ref.) │     —     —          —          —
    #>    Male          │    3.86  0.91  [ 2.08,  5.63]  <.001
    #>  smoking:        │
    #>    No (ref.)     │     —     —          —          —
    #>    Yes           │   -1.72  1.11  [-3.89,  0.45]   .121
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175
    #>  R²              │    0.02
    #>  Adj.R²          │    0.02
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).

Reading the table:

- Each predictor occupies one row; factor predictors are grouped under their parent variable name with one row per level. The reference level carries the `(ref.)` annotation and an em-dash across the statistic columns, so the substantive comparison is visible in a single glance (NEJM / BMJ clinical convention).
- The `95% CI` column reports a symmetric Wald interval at the level set by `ci_level` (default 0.95). The CI header tracks the chosen level — switching to `ci_level = 0.99` re-labels the column accordingly.
- The footer line names the variance estimator in plain English so the reader can find the inferential regime without leaving the table. The estimator switches with `vcov` (covered in Robust variance below).

## Standardised coefficients

Standardised coefficients (`β`) make predictors with different natural scales comparable: a one-standard-deviation increase in `X` predicts a `β`-standard-deviation change in `Y`. APA Manual 7 §7.13 recommends reporting both `B` and `β` so the unstandardised effect (natural units, interpretable) stays alongside the standardised effect (comparable across predictors).

`standardized` selects the method. Four are available; the choice is consequential and well-documented (Cohen, Cohen, West, and Aiken 2003 §3.4; Gelman 2008):

- `"refit"` — refit the model on z-scored outcome and predictors. Gold-standard convention, used by SPSS `REGRESSION` and Stata `regress, beta`. Both numeric and dummy-coded predictors enter the refit on the same scale.
- `"posthoc"` — algebraic rescaling `β = B × SD(X) / SD(Y)`, applied to the original fit. Numerically identical to `"refit"` for purely linear-additive Gaussian models; preferred when refitting is expensive or when `lm()` was wrapped in a pipeline that resists re-execution.
- `"basic"` — algebraic, but factor dummies keep their 0/1 scale rather than being z-scored. Useful when factor levels carry meaningful base rates that scale-free standardisation would obscure.
- `"smart"` — Gelman’s (2008) recommendation: numeric predictors divided by `2 × SD(X)`; binary predictors centred only. The resulting `β` is the predicted change in `Y` for one within- sample standard deviation of `X`, on the original `Y` scale, which keeps factor and numeric coefficients on roughly comparable footing.

When `standardized != "none"`, the `"beta"` token is auto-injected into `show_columns` immediately after `"b"`:

    fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth)
    table_regression(fit, standardized = "refit")
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B       β     SE       95% CI        p
    #> ─────────────────┼─────────────────────────────────────────────
    #>  (Intercept)     │   65.20  -0.10  1.66  [61.95, 68.45]  <.001
    #>  age             │    0.05   0.04  0.03  [-0.01,  0.11]   .130
    #>  sex:            │
    #>    Female (ref.) │     —      —     —          —          —
    #>    Male          │    3.86   0.25  0.91  [ 2.08,  5.63]  <.001
    #>  smoking:        │
    #>    No (ref.)     │     —      —     —          —          —
    #>    Yes           │   -1.72  -0.11  1.11  [-3.89,  0.45]   .121
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175
    #>  R²              │    0.02
    #>  Adj.R²          │    0.02
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> β = standardised coefficient.

Caveat: standardised coefficients with interactions or transformed terms. When the model contains a product term, an `I()`, `poly()`, `log()`, or `splines::ns()` wrapper, the standardised coefficient of the non-additive term has no closed-form “one-SD change in X” reading (Aiken and West 1991; Cohen et al. 2003 §7.7). The function emits a classed `spicy_caveat` warning at runtime AND prints a method-specific caveat line in the table footer, so the limitation is exposed at the point of use without blocking the table.

## Per-coefficient effect sizes

For each predictor, you can request a partial effect-size column. Three measures are available — Cohen’s `f²` (`partial_f2`), Pearson’s partial `η²` (`partial_eta2`), and the Olejnik–Algina bias-corrected partial `ω²` (`partial_omega2`). Each carries a confidence interval derived from noncentral-`F` inversion (Smithson 2003; Steiger 2004), exposed as a separate `<token>_ci` column. The shortcuts `"all_f2"`, `"all_eta2"`, `"all_omega2"` expand to the point estimate and its CI pair in one go:

    fit <- lm(wellbeing_score ~ age + sex + smoking + education,
              data = sochealth)
    table_regression(
      fit,
      show_columns = c("b", "p", "all_eta2", "all_omega2")
    )
    #> Ordered factor(s) detected. Polynomial contrasts (the R default for `ordered()`) decompose the factor into orthogonal trend components: `.L` = linear, `.Q` = quadratic, `.C` = cubic, `^k` = degree k. Coefficients are trends across the ordered levels, NOT per-level effects against a reference.
    #> ℹ To display per-level (treatment) effects, refit with `factor(x, ordered = FALSE)` or set `options(contrasts = c("contr.treatment", "contr.treatment"))`.
    #> This message is displayed once per session.
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B       p     η²    η² 95% CI     ω²    ω² 95% CI
    #> ─────────────────┼────────────────────────────────────────────────────────
    #>  (Intercept)     │   64.49  <.001
    #>  age             │    0.03   .344  0.00  [0.00, 0.01]  0.00  [0.00, 0.01]
    #>  sex:            │
    #>    Female (ref.) │     —     —      —         —         —         —
    #>    Male          │    3.57  <.001  0.02  [0.01, 0.03]  0.02  [0.01, 0.03]
    #>  smoking:        │
    #>    No (ref.)     │     —     —      —         —         —         —
    #>    Yes           │    0.68   .496  0.00  [0.00, 0.01]  0.00  [0.00, 0.01]
    #>  education:      │
    #>    .L            │   13.94  <.001  0.21  [0.17, 0.25]  0.21  [0.17, 0.25]
    #>    .Q            │   -1.66   .013  0.21  [0.17, 0.25]  0.21  [0.17, 0.25]
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175
    #>  R²              │    0.22
    #>  Adj.R²          │    0.22
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> η² = partial eta-squared; ω² = bias-corrected partial omega-squared.
    #> Ordered factor `education`: polynomial trends (.L = linear, .Q = quadratic).

Methodology notes:

- The partial F-test is computed on a Type-II ANOVA reference (`car::Anova`), which respects the principle of marginality and is the SAS / SPSS default for unbalanced designs.
- The `ω²` point estimate is bias-corrected via the Olejnik and Algina (2003) formula `((F − 1) × df1) / (F × df1 + N − df1)`, yielding a less-biased small-sample estimator than partial `η²`.
- The CI bounds come from inverting the noncentrality parameter of the F-distribution at the lower and upper confidence levels (Steiger 2004 §4). The Steiger inversion always brackets the bias-corrected point estimate, even when the lower bound clips at zero (common for near-null terms).
- For factor predictors with `k` levels, the partial F-test is the joint `(k − 1)` df Wald test, so the same effect-size value is broadcast across all non-reference dummy rows; the reference row shows an em-dash.

## Average marginal effects (AME)

The `B` coefficient is the model’s structural effect — the change in the linear predictor `Xβ` for a one-unit change in `x`, holding the other predictors constant. The average marginal effect (AME) is the observable effect on the response: the average partial derivative `dE[Y|X] / dx` over the sample. For a purely linear-additive `lm()` the two coincide. For models with interactions or transformed terms, and for any `glm()` with a non-identity link, the AME is the quantity the substantive reader actually needs.

Add `"ame"` to `show_columns` for the point estimate. Companion tokens display the SE, CI, and p-value: `"ame_se"`, `"ame_ci"`, `"ame_p"`. The shortcut `"all_ame"` expands to the full set.

    fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth)
    table_regression(
      fit,
      show_columns = c("b", "ame", "ame_ci", "ame_p")
    )
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B      AME      95% CI        p
    #> ─────────────────┼──────────────────────────────────────
    #>  (Intercept)     │   65.20
    #>  age             │    0.05   0.05  [-0.01, 0.11]   .130
    #>  sex:            │
    #>    Female (ref.) │     —      —          —         —
    #>    Male          │    3.86   3.86  [ 2.08, 5.63]  <.001
    #>  smoking:        │
    #>    No (ref.)     │     —      —          —         —
    #>    Yes           │   -1.72  -1.72  [-3.89, 0.45]   .121
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175
    #>  R²              │    0.02
    #>  Adj.R²          │    0.02
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> AME = average marginal effect.

Reading conventions:

- `"p"` always refers to the `B` (or `β`) coefficient, never to the AME. Use `"ame_p"` for the AME-specific p-value.
- Placing `"ame"` after `"p"` keeps the “which p belongs to what” reading unambiguous — the B-block (`B / SE / p`) is closed before the AME-block opens.
- For non-linear models the AME is reported on the response scale, not the link scale. The Generalised linear models section below works out a logistic-regression example.

Inference is delegated to [`marginaleffects::avg_slopes()`][marginaleffects::avg_slopes] and respects the chosen variance estimator. Under cluster-robust variance (covered in Robust variance), the AME inference shares the coefficient’s t-distribution and Satterthwaite-corrected degrees of freedom via `clubSandwich::linear_contrast()`, so `B` and AME are reported on the same inferential footing in the same table.

## Multiple models side by side

Pass a list of `lm()` fits. The default column layout places each model in its own panel under a centered spanner label showing the model name; sub-columns (`B / SE / p`) are repeated under the spanner. When dependent variables differ across models and the user did not supply labels (`model_labels =` or named list), the bare DV name is lifted into the spanner automatically:

    m_wellbeing <- lm(wellbeing_score ~ age + sex + smoking,
                       data = sochealth)
    m_bmi       <- lm(bmi             ~ age + sex + smoking,
                       data = sochealth)
    table_regression(list(m_wellbeing, m_bmi))
    #> Linear regression comparison
    #>
    #>                      wellbeing_score             bmi
    #>                    ────────────────────  ────────────────────
    #>  Variable        │    B      SE     p       B      SE     p
    #> ─────────────────┼────────────────────────────────────────────
    #>  (Intercept)     │   65.20  1.66  <.001    23.98  0.40  <.001
    #>  age             │    0.05  0.03   .130     0.04  0.01  <.001
    #>  sex:            │
    #>    Female (ref.) │     —     —     —         —     —     —
    #>    Male          │    3.86  0.91  <.001     0.51  0.22   .018
    #>  smoking:        │
    #>    No (ref.)     │     —     —     —         —     —     —
    #>    Yes           │   -1.72  1.11   .121    -0.06  0.26   .822
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175                  1163
    #>  R²              │    0.02                  0.02
    #>  Adj.R²          │    0.02                  0.02
    #>
    #> Note. Linear regression models.
    #> Std. errors: classical (OLS).

When all models share the same DV, the DV appears in the title. Use a named list — e.g. `list(Crude = m1, Adjusted = m2)` — to set the spanner labels explicitly; pass `model_labels = c(...)` to override the names from the list.

## Hierarchical / nested regression

Set `nested = TRUE` to add in-table change-statistic rows (APA Table 7.13 / Stata `esttab` / SPSS Model Summary convention). Each adjacent pair (M2 vs M1, M3 vs M2, …) contributes one column of change stats below `R² / Adj.R²`; the FIRST model column gets em-dashes (no previous model to compare to).

Hierarchical regression requires every model in the sequence to share the same analytic sample: identical `nobs` AND identical response variable. R’s listwise deletion otherwise produces different `n` per model as soon as a predictor introduced by M2 or M3 has missing values that M1 did not see — a silent bias on the change statistics. We restrict to complete cases on the union of predictors up front:

    sochealth_cc <- sochealth |>
      dplyr::select(wellbeing_score, age, sex, smoking, bmi, region) |>
      na.omit()

    m1 <- lm(wellbeing_score ~ age + sex,                 data = sochealth_cc)
    m2 <- lm(wellbeing_score ~ age + sex + smoking,       data = sochealth_cc)
    m3 <- lm(wellbeing_score ~ age + sex + smoking + bmi, data = sochealth_cc)
    table_regression(list(m1, m2, m3), nested = TRUE)
    #> Hierarchical linear regression: wellbeing_score
    #>
    #>                          Model 1                Model 2            Model 3
    #>                    ────────────────────  ─────────────────────  ──────────────
    #>  Variable        │    B      SE     p       B       SE     p       B       SE
    #> ─────────────────┼─────────────────────────────────────────────────────────────
    #>  (Intercept)     │   64.70  1.66  <.001    65.00   1.67  <.001    80.57   3.37
    #>  age             │    0.05  0.03   .118     0.05   0.03   .109     0.07   0.03
    #>  sex:            │
    #>    Female (ref.) │     —     —     —         —      —     —         —      —
    #>    Male          │    3.89  0.91  <.001     3.88   0.91  <.001     4.21   0.90
    #>  smoking:        │
    #>    No (ref.)     │     —     —     —         —      —     —         —      —
    #>    Yes           │                         -1.68   1.11   .132    -1.71   1.10
    #>  bmi             │                                                -0.65   0.12
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1163                  1163                   1163
    #>  R²              │    0.02                  0.02                   0.04
    #>  Adj.R²          │    0.02                  0.02                   0.04
    #>  ΔR²             │     —                   +0.00                  +0.02
    #>  F-change        │     —                   +2.28                 +28.13
    #>  p (change)      │     —                     .132                  <.001
    #>
    #>                    Model
    #>                    ─────
    #>  Variable        │   p
    #> ─────────────────┼───────
    #>  (Intercept)     │ <.001
    #>  age             │  .019
    #>  sex:            │
    #>    Female (ref.) │  —
    #>    Male          │ <.001
    #>  smoking:        │
    #>    No (ref.)     │  —
    #>    Yes           │  .119
    #>  bmi             │ <.001
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌
    #>  n               │
    #>  R²              │
    #>  Adj.R²          │
    #>  ΔR²             │
    #>  F-change        │
    #>  p (change)      │
    #>
    #> Note. Linear regression models.
    #> Std. errors: classical (OLS).

Default change tokens auto-injected: `c("r2_change", "f_change", "p_change")` for `lm` (APA hierarchical-regression standard), `c("lrt_change", "p_change")` for `glm` (Hosmer & Lemeshow §3.5). Customise via `show_fit_stats`; the order of tokens controls the order of the rows. Other change tokens are available: `"adj_r2_change"`, `"f2_change"`, `"deviance_change"`, `"aic_change"` / `"aicc_change"` / `"bic_change"`.

Validation is strict: identical `nobs` AND identical response across all models, otherwise a `spicy_invalid_input` error explains the listwise-deletion trap and suggests refitting on the common subset.

## Robust variance

The default `vcov = "classical"` reports the OLS standard error, valid under homoskedastic, independent errors. Two robust alternatives are first-class arguments; bootstrap and jackknife variants are also available via `vcov = "bootstrap"` / `"jackknife"`.

### Heteroskedasticity-consistent (HC)

When error variance plausibly depends on the predictors (a ubiquitous concern in cross-sectional social-science data), set `vcov = "HC*"` for sandwich-style standard errors via [`sandwich::vcovHC()`][sandwich::vcovHC]. The valid types are `HC0` through `HC5`; `HC3` is the small-sample-friendly default (Long and Ervin 2000):

    fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth)
    table_regression(fit, vcov = "HC3")
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B      SE       95% CI        p
    #> ─────────────────┼──────────────────────────────────────
    #>  (Intercept)     │   65.20  1.61  [62.05, 68.35]  <.001
    #>  age             │    0.05  0.03  [-0.01,  0.11]   .127
    #>  sex:            │
    #>    Female (ref.) │     —     —          —          —
    #>    Male          │    3.86  0.91  [ 2.07,  5.64]  <.001
    #>  smoking:        │
    #>    No (ref.)     │     —     —          —          —
    #>    Yes           │   -1.72  1.11  [-3.91,  0.47]   .123
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175
    #>  R²              │    0.02
    #>  Adj.R²          │    0.02
    #>
    #> Note. Linear regression.
    #> Std. errors: heteroskedasticity-robust (HC3).

The footer reads “Std. errors: heteroskedasticity-robust (HC3)”; the column header for the confidence interval automatically tracks `ci_level`.

### Cluster-robust (CR)

For clustered observations (repeated measures on the same person, students within schools, observations within regions), `vcov = "CR*"` requests cluster-robust variance via [`clubSandwich::vcovCR()`][clubSandwich::vcovCR], with the cluster identifier supplied through `cluster`. Three forms are accepted:

- Formula — `cluster = ~region`. The variables are looked up in `model.frame(fit)` first, then in the model’s original `data` argument. Recommended: independent of the dataset’s name, composable for multi-way clustering (`cluster = ~region:year`).
- String — `cluster = "region"`. Single column name resolved the same way. Convenient but cannot express interactions.
- Vector — `cluster = df$region`. Atomic vector of length `nobs(fit)`. Use this when the cluster key is derived on the fly (`cluster = interaction(df$region, df$year)`) or pulled from a different dataset with matching row order.

Bare unquoted names (`cluster = region`) are not accepted — they would require non-standard evaluation that breaks under programmatic use (function wrapping, loops, dynamic column choice).

    fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth_cc)
    table_regression(
      fit,
      vcov = "CR2",
      cluster = ~region,
      show_columns = c("b", "se", "ci", "p", "ame", "ame_p")
    )
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B      SE       95% CI        p     AME     p
    #> ─────────────────┼────────────────────────────────────────────────────
    #>  (Intercept)     │   65.00  1.74  [60.49, 69.51]  <.001
    #>  age             │    0.05  0.04  [-0.05,  0.15]   .247   0.05   .247
    #>  sex:            │
    #>    Female (ref.) │     —     —          —          —       —     —
    #>    Male          │    3.88  0.85  [ 1.68,  6.07]   .006   3.88   .006
    #>  smoking:        │
    #>    No (ref.)     │     —     —          —          —       —     —
    #>    Yes           │   -1.68  1.55  [-5.72,  2.37]   .331  -1.68   .331
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1163
    #>  R²              │    0.02
    #>  Adj.R²          │    0.02
    #>
    #> Note. Linear regression.
    #> Std. errors: cluster-robust (CR2), clusters by region.
    #> AME = average marginal effect.
    #> AME inference: t-distribution with Satterthwaite-corrected df (Pustejovsky & Tipton 2018) via `clubSandwich::linear_contrast()`.

`CR2` (the Bell-McCaffrey adjustment) is the recommended default under few clusters (Pustejovsky and Tipton 2018; Imbens and Kolesár 2016). Coefficient inference uses [`clubSandwich::coef_test()`][clubSandwich::coef_test] with Satterthwaite-corrected degrees of freedom. When AME columns are requested, the same Satterthwaite framework is applied to the AME contrast via `clubSandwich::linear_contrast()`, so `B` and AME share the same t-distribution with the same df — visible in the footer.

## Multiple-comparison adjustment

`p_adjust` applies a family-wise or false-discovery-rate adjustment to the p-values via [`stats::p.adjust()`][stats::p.adjust]. The family is the model’s full coefficient set (intercept and reference rows excluded); `B` and `AME` are adjusted as independent families within the same call. Available methods are the same as in `stats::p.adjust()`: `"none"` (default), `"holm"`, `"hochberg"`, `"hommel"`, `"bonferroni"`, `"BH"` / `"fdr"`, `"BY"`.

    fit <- lm(wellbeing_score ~ age + sex + smoking + education,
              data = sochealth)
    table_regression(fit, p_adjust = "bonferroni")
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B      SE       95% CI        p
    #> ─────────────────┼──────────────────────────────────────
    #>  (Intercept)     │   64.49  1.48  [61.58, 67.40]  <.001
    #>  age             │    0.03  0.03  [-0.03,  0.08]  1.000
    #>  sex:            │
    #>    Female (ref.) │     —     —          —          —
    #>    Male          │    3.57  0.81  [ 1.99,  5.15]  <.001
    #>  smoking:        │
    #>    No (ref.)     │     —     —          —          —
    #>    Yes           │    0.68  0.99  [-1.27,  2.63]  1.000
    #>  education:      │
    #>    .L            │   13.94  0.79  [12.38, 15.49]  <.001
    #>    .Q            │   -1.66  0.67  [-2.97, -0.35]   .065
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175
    #>  R²              │    0.22
    #>  Adj.R²          │    0.22
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> P-values adjusted via stats::p.adjust(method = "bonferroni"); m = 5 coefficient(s) per model.
    #> Ordered factor `education`: polynomial trends (.L = linear, .Q = quadratic).

The footer documents the chosen method and the family size; the SE column is unchanged. `p_adjust` is orthogonal to `vcov`: combining a robust SE with a family-wise correction is fully supported, e.g. `vcov = "HC3", p_adjust = "holm"`. The adjustment is applied before `keep` / `drop` filtering, so the family stays the model’s full coefficient set and the displayed adjusted p-values reflect the right denominator regardless of which subset is shown.

A methodological note. Adjusting the p-values of every coefficient of a single regression model is not the standard convention in social-science or clinical reporting (Rothman 1990; Gelman, Hill, and Yajima 2012; Greenland 2017; Harrell 2015 §5.4; APA Manual 7 §6.46). Each coefficient tests a scientifically distinct hypothesis on a distinct predictor, which is not the situation that family-wise procedures were designed for. The default `"none"` reflects this consensus.

Adjustment is nonetheless legitimate in three contexts:

1. Mass screening with many candidate predictors and no prior hypothesis — typically `"BH"` (Benjamini–Hochberg, false discovery rate).
2. Pre-registered multi-endpoint confirmatory designs — typically `"holm"` (Holm’s step-down, strong family-wise error rate control).
3. When a reviewer, an editor, or a statistical analysis plan explicitly requests it — apply the requested method and document it in the footer.

The argument is exposed under the same transparency rule used for `standardized`: the tool is here, the methodological choice is yours, and the footer makes the choice visible to the reader.

## Filtering displayed coefficients

`keep` and `drop` accept regular expressions matched against coefficient names (as returned by [stats::coef()]). They are mutually exclusive — pick `keep` to whitelist focal predictors, or `drop` to hide a few control variables. Multiple patterns combine with logical OR.

    fit <- lm(wellbeing_score ~ age + sex + smoking + bmi + education,
              data = sochealth)
    table_regression(fit, keep = c("^smoking", "^bmi$"))
    #> Linear regression: wellbeing_score
    #>
    #>  Variable    │    B      SE      95% CI        p
    #> ─────────────┼─────────────────────────────────────
    #>  smoking:    │
    #>    No (ref.) │     —     —          —         —
    #>    Yes       │    0.79  1.00  [-1.17, 2.75]   .428
    #>  bmi         │    0.10  0.12  [-0.14, 0.33]   .418
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n           │ 1163
    #>  R²          │    0.23
    #>  Adj.R²      │    0.22
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> Ordered factor `education`: polynomial trends (.L = linear, .Q = quadratic).

    table_regression(fit, drop = "^education")
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │    B      SE       95% CI        p
    #> ─────────────────┼──────────────────────────────────────
    #>  (Intercept)     │   62.12  3.23  [55.78, 68.45]  <.001
    #>  age             │    0.02  0.03  [-0.03,  0.08]   .407
    #>  sex:            │
    #>    Female (ref.) │     —     —          —          —
    #>    Male          │    3.50  0.81  [ 1.91,  5.10]  <.001
    #>  smoking:        │
    #>    No (ref.)     │     —     —          —          —
    #>    Yes           │    0.79  1.00  [-1.17,  2.75]   .428
    #>  bmi             │    0.10  0.12  [-0.14,  0.33]   .418
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1163
    #>  R²              │    0.23
    #>  Adj.R²          │    0.22
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> Ordered factor `education`: polynomial trends (.L = linear, .Q = quadratic).

Together with `p_adjust`, this is the article-ready workflow: adjust on the full model, display only the rows the reader cares about — `table_regression(fit, p_adjust = "BH", keep = "^treatment")`.

## Generalised linear models (glm)

`table_regression()` accepts any `glm()` fit. Inference defaults to the `z`-asymptotic Wald regime — the convention used by `summary.glm()`, Stata’s `logit, or`, and SPSS `LOGISTIC REGRESSION`. The table title becomes family-aware (“Logistic regression”, “Poisson regression”, “Probit regression”, …) and the default fit-statistics block swaps in `nobs`, `pseudo_r2_mcfadden`, `pseudo_r2_nagelkerke`, and `AIC` instead of `R²` and `Adj.R²`.

We illustrate with a logistic regression of `smoking` on `sex`, `age`, and `education` from `sochealth`. The `education` variable is an ordered factor in the source data; we convert it to a plain factor so contrasts are dummy-coded (one row per level) rather than the polynomial trends (`.L`, `.Q`, …) R applies by default to ordered factors. The model mixes a binary factor (`sex`), a numeric predictor (`age`), and a 3-level factor (`education`) — enough to exercise every glm-specific feature below.

    sh <- sochealth |>
      dplyr::mutate(education = factor(education, ordered = FALSE))

    fit <- glm(smoking ~ sex + age + education, data = sh,
               family = binomial)
    table_regression(fit)
    #> Logistic regression: smoking
    #>
    #>  Variable                 │    B      SE       95% CI        p
    #> ──────────────────────────┼──────────────────────────────────────
    #>  (Intercept)              │   -1.11  0.29  [-1.67, -0.55]  <.001
    #>  sex:                     │
    #>    Female (ref.)          │     —     —          —          —
    #>    Male                   │   -0.04  0.14  [-0.32,  0.25]   .800
    #>  age                      │    0.01  0.00  [-0.00,  0.02]   .214
    #>  education:               │
    #>    Lower secondary (ref.) │     —     —          —          —
    #>    Tertiary               │   -0.91  0.20  [-1.29, -0.52]  <.001
    #>    Upper secondary        │   -0.48  0.17  [-0.82, -0.14]   .005
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175
    #>  R² (McFadden)            │    0.02
    #>  R² (Nagelkerke)          │    0.03
    #>  AIC                      │ 1200.9
    #>
    #> Note. Logistic regression.
    #> Std. errors: classical (MLE inverse Hessian).

### Response-scale display: `exponentiate = TRUE`

Set `exponentiate = TRUE` to switch the `B` column to the response scale and rebrand its header per family and link: `OR` for `binomial(logit)`, `IRR` for `poisson(log)`, `HR` for `binomial(cloglog)`, `RR` for `binomial(log)`, `MR` for `Gamma(log)`, and the generic `exp(B)` otherwise. The standard error follows the delta-method approximation `SE_OR = OR × SE_log-odds` (the Stata `logit, or` convention). The test statistic and the p-value are invariant under any monotone transformation, so they remain on the link scale and match the unexponentiated table verbatim:

    table_regression(fit, exponentiate = TRUE)
    #> Logistic regression: smoking
    #>
    #>  Variable                 │   OR      SE      95% CI       p
    #> ──────────────────────────┼────────────────────────────────────
    #>  (Intercept)              │    0.33  0.09  [0.19, 0.58]  <.001
    #>  sex:                     │
    #>    Female (ref.)          │     —     —         —         —
    #>    Male                   │    0.96  0.14  [0.73, 1.28]   .800
    #>  age                      │    1.01  0.00  [1.00, 1.02]   .214
    #>  education:               │
    #>    Lower secondary (ref.) │     —     —         —         —
    #>    Tertiary               │    0.40  0.08  [0.27, 0.59]  <.001
    #>    Upper secondary        │    0.62  0.11  [0.44, 0.87]   .005
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175
    #>  R² (McFadden)            │    0.02
    #>  R² (Nagelkerke)          │    0.03
    #>  AIC                      │ 1200.9
    #>
    #> Note. Logistic regression.
    #> Std. errors: classical (MLE inverse Hessian).
    #> OR = odds ratio.
    #> Coefficients exponentiated and displayed as OR; CI bounds exponentiated; SE delta-method approximation: SE_OR = OR × SE_link.

### Term-level partial chi-square

`partial_chi2` is the glm analog of `partial_f2`: for each model term, the partial likelihood-ratio chi-square via `drop1(test = "LRT")` (SAS PROC LOGISTIC `TYPE3`; Long & Freese 2014 §3.5). Rendered as `value (df)` so factor terms (`k − 1` df) and numeric terms (1 df) read at a glance:

    fit2 <- glm(smoking ~ age + education, data = sh, family = binomial)
    table_regression(fit2, show_columns = c("b", "partial_chi2", "p"))
    #> Logistic regression: smoking
    #>
    #>  Variable                 │    B        χ²        p
    #> ──────────────────────────┼───────────────────────────
    #>  (Intercept)              │   -1.13             <.001
    #>  age                      │    0.01   1.55 (1)   .213
    #>  education:               │
    #>    Lower secondary (ref.) │     —      —         —
    #>    Tertiary               │   -0.91  21.70 (2)  <.001
    #>    Upper secondary        │   -0.48  21.70 (2)   .005
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175
    #>  R² (McFadden)            │    0.02
    #>  R² (Nagelkerke)          │    0.03
    #>  AIC                      │ 1198.9
    #>
    #> Note. Logistic regression.
    #> Std. errors: classical (MLE inverse Hessian).
    #> χ² = partial likelihood-ratio chi-squared.

### Standardised coefficients: `refit` and the new `pseudo`

For `glm`, `standardized = "refit"` z-scores numeric predictors only and refits the model — the response stays on its observed scale because the link function is fixed. This is the “x-standardization” convention (Long and Freese 2014 §4.3.4). The other algebraic methods (`"posthoc"`, `"basic"`, `"smart"`) apply X-only scaling using the same algebra as in the `lm` case.

`standardized = "pseudo"` (`glm` only) is the Menard (2004, 2011) fully standardised coefficient, scaling by `SD(X) / SD(Y*)` where `Y*` is the latent variable on the link scale and `SD(Y*) = sqrt(var(linear-predictor) + var_link)` with `var_link` = π²/3 for logit, 1 for probit, π²/6 for cloglog. Defined for binomial families; non-binomial returns NA with a `spicy_caveat`:

    table_regression(fit, standardized = "pseudo")
    #> Logistic regression: smoking
    #>
    #>  Variable                 │    B       β     SE       95% CI        p
    #> ──────────────────────────┼─────────────────────────────────────────────
    #>  (Intercept)              │   -1.11    —    0.29  [-1.67, -0.55]  <.001
    #>  sex:                     │
    #>    Female (ref.)          │     —      —     —          —          —
    #>    Male                   │   -0.04  -0.02  0.14  [-0.32,  0.25]   .800
    #>  age                      │    0.01   0.05  0.00  [-0.00,  0.02]   .214
    #>  education:               │
    #>    Lower secondary (ref.) │     —      —     —          —          —
    #>    Tertiary               │   -0.91  -0.49  0.20  [-1.29, -0.52]  <.001
    #>    Upper secondary        │   -0.48  -0.26  0.17  [-0.82, -0.14]   .005
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175
    #>  R² (McFadden)            │    0.02
    #>  R² (Nagelkerke)          │    0.03
    #>  AIC                      │ 1200.9
    #>
    #> Note. Logistic regression.
    #> Std. errors: classical (MLE inverse Hessian).
    #> β = standardised coefficient.

### Average marginal effects: probability units, not log-odds

For `glm`, AME is computed as the average of `dE[Y|X] / dx` over the observed sample — on the response scale, not the link scale. For logistic regression this means the displayed AME is in probability units, not log-odds: an AME of `-0.15` for `education = Tertiary` reads “on average, holding sex and age constant, a respondent with tertiary education is 15 percentage points less likely to be a current smoker than one with lower secondary education”. This is almost always the quantity a substantive reader wants from a logistic regression, and it is the reason AME is increasingly recommended over odds ratios for communicating logistic effects (Mood 2010; Long and Freese 2014 §5.3).

    table_regression(fit, show_columns = c("b", "p", "ame", "ame_ci", "ame_p"))
    #> Logistic regression: smoking
    #>
    #>  Variable                 │    B       p     AME       95% CI        p
    #> ──────────────────────────┼──────────────────────────────────────────────
    #>  (Intercept)              │   -1.11  <.001
    #>  sex:                     │
    #>    Female (ref.)          │     —     —       —          —          —
    #>    Male                   │   -0.04   .800  -0.01  [-0.05,  0.04]   .800
    #>  age                      │    0.01   .214   0.00  [-0.00,  0.00]   .214
    #>  education:               │
    #>    Lower secondary (ref.) │     —     —       —          —          —
    #>    Tertiary               │   -0.91  <.001  -0.15  [-0.22, -0.09]  <.001
    #>    Upper secondary        │   -0.48   .005  -0.09  [-0.16, -0.02]   .007
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175
    #>  R² (McFadden)            │    0.02
    #>  R² (Nagelkerke)          │    0.03
    #>  AIC                      │ 1200.9
    #>
    #> Note. Logistic regression.
    #> Std. errors: classical (MLE inverse Hessian).
    #> AME = average marginal effect.

Point estimate and inference are delegated to [`marginaleffects::avg_slopes()`][marginaleffects::avg_slopes]. Under cluster-robust variance, the Satterthwaite-df handling described in the Robust variance section applies to `glm` AME as well.

### Profile-likelihood CIs: `ci_method = "profile"`

The default is `ci_method = "wald"` (symmetric `estimate ± z × SE`, matching `summary.glm`). For `glm` you can also request `ci_method = "profile"`, which calls `MASS::confint.glm()` to compute the profile-likelihood CI — asymmetric, exact for likelihood-based inference, and the gold standard under sparse data or near-boundary estimates (Venables & Ripley MASS §7.2). The estimate, SE, statistic, and p-value all remain Wald; `"profile"` only refines the CI bounds:

    table_regression(fit, ci_method = "profile")
    #> Logistic regression: smoking
    #>
    #>  Variable                 │    B      SE       95% CI        p
    #> ──────────────────────────┼──────────────────────────────────────
    #>  (Intercept)              │   -1.11  0.29  [-1.68, -0.55]  <.001
    #>  sex:                     │
    #>    Female (ref.)          │     —     —          —          —
    #>    Male                   │   -0.04  0.14  [-0.32,  0.25]   .800
    #>  age                      │    0.01  0.00  [-0.00,  0.02]   .214
    #>  education:               │
    #>    Lower secondary (ref.) │     —     —          —          —
    #>    Tertiary               │   -0.91  0.20  [-1.29, -0.52]  <.001
    #>    Upper secondary        │   -0.48  0.17  [-0.82, -0.14]   .005
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175
    #>  R² (McFadden)            │    0.02
    #>  R² (Nagelkerke)          │    0.03
    #>  AIC                      │ 1200.9
    #>
    #> Note. Logistic regression.
    #> Std. errors: classical (MLE inverse Hessian).

### Hierarchical glm (LRT)

`nested = TRUE` on a list of nested glm fits adds a “Model comparison” block whose default tokens are `c("LRT", "p")` — the APA-conventional layout for hierarchical logistic regression (Hosmer & Lemeshow §3.5; Long & Freese 2014 §3.6). The chi-square statistic comes from `anova(test = "LRT")`. `r2_change` and `F` are not defined for glm and return em-dashes if explicitly requested:

    m1 <- glm(smoking ~ sex,                   data = sh, family = binomial)
    m2 <- glm(smoking ~ sex + age,             data = sh, family = binomial)
    m3 <- glm(smoking ~ sex + age + education, data = sh, family = binomial)
    table_regression(list(m1, m2, m3), nested = TRUE)
    #> Hierarchical logistic regression: smoking
    #>
    #>                                   Model 1                Model 2
    #>                             ────────────────────  ─────────────────────
    #>  Variable                 │    B      SE     p       B       SE     p
    #> ──────────────────────────┼─────────────────────────────────────────────
    #>  (Intercept)              │   -1.29  0.10  <.001    -1.54   0.26  <.001
    #>  sex:                     │
    #>    Female (ref.)          │     —     —     —         —      —     —
    #>    Male                   │   -0.05  0.14   .713    -0.05   0.14   .722
    #>  age                      │                          0.01   0.00   .294
    #>  education:               │
    #>    Lower secondary (ref.) │     —     —     —         —      —     —
    #>    Tertiary               │
    #>    Upper secondary        │
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175                  1175
    #>  R² (McFadden)            │    0.00                  0.00
    #>  R² (Nagelkerke)          │    0.00                  0.00
    #>  AIC                      │ 1217.6                1218.5
    #>  Δχ²                      │     —                   +1.10
    #>  p (change)               │     —                     .294
    #>
    #>                                    Model 3
    #>                             ─────────────────────
    #>  Variable                 │    B       SE     p
    #> ──────────────────────────┼───────────────────────
    #>  (Intercept)              │   -1.11   0.29  <.001
    #>  sex:                     │
    #>    Female (ref.)          │     —      —     —
    #>    Male                   │   -0.04   0.14   .800
    #>  age                      │    0.01   0.00   .214
    #>  education:               │
    #>    Lower secondary (ref.) │     —      —     —
    #>    Tertiary               │   -0.91   0.20  <.001
    #>    Upper secondary        │   -0.48   0.17   .005
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                        │ 1175
    #>  R² (McFadden)            │    0.02
    #>  R² (Nagelkerke)          │    0.03
    #>  AIC                      │ 1200.9
    #>  Δχ²                      │  +21.64
    #>  p (change)               │    <.001
    #>
    #> Note. Logistic regression models.
    #> Std. errors: classical (MLE inverse Hessian).

### Gaussian glm caveat

A `glm()` with `family = gaussian` and `link = "identity"` is mathematically equivalent to `lm()` but lacks the variance- explained effect-size family (`partial_f2 / η² / ω²`) and the Satterthwaite-corrected AME path. Following the transparency over rejection rule, spicy accepts the fit and emits a `spicy_caveat` suggesting a refit with `lm()`.

## Significance stars

Stars are off by default. APA 7 §6.46 explicitly discourages asterisks-only reporting in favour of exact p-values, and ASA’s post-2019 guidance (Wasserstein, Schirm, and Lazar 2019) reinforces the same point. Set `stars = TRUE` for the APA preset (`*** p < .001, ** p < .01, * p < .05`) or pass a named numeric vector for custom thresholds:

    fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth)
    table_regression(fit, stars = TRUE)
    #> Linear regression: wellbeing_score
    #>
    #>  Variable        │     B        SE       95% CI        p
    #> ─────────────────┼─────────────────────────────────────────
    #>  (Intercept)     │   65.20***  1.66  [61.95, 68.45]  <.001
    #>  age             │    0.05     0.03  [-0.01,  0.11]   .130
    #>  sex:            │
    #>    Female (ref.) │     —        —          —          —
    #>    Male          │    3.86***  0.91  [ 2.08,  5.63]  <.001
    #>  smoking:        │
    #>    No (ref.)     │     —        —          —          —
    #>    Yes           │   -1.72     1.11  [-3.89,  0.45]   .121
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n               │ 1175
    #>  R²              │    0.02
    #>  Adj.R²          │    0.02
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> *** p < .001, ** p < .01, * p < .05.

Stars suffix the `B` column (or `β` when standardisation is requested); the threshold mapping is auto-documented in the footer. The `p` column itself remains unstarred so the numeric value stays readable.

## Display knobs

`labels` accepts a named character vector keyed by either formula term labels (`"sex"`) or coefficient names (`"sexMale"`), so individual contrast rows can be relabelled. `intercept_position` switches the intercept to the bottom (Stata convention). `reference_style = "annotation"` lifts the reference level into the factor header (`"sex: [ref: Female]"`) and drops the explicit reference row, for compact tables. `decimal_mark = ","` switches to European decimal notation and automatically changes the CI separator to `";"` to avoid ambiguity:

    fit <- lm(wellbeing_score ~ age + sex + education, data = sochealth)
    table_regression(
      fit,
      labels = c(
        age            = "Age (years)",
        sex            = "Sex",
        education      = "Education"
      ),
      reference_style = "annotation",
      intercept_position = "last",
      decimal_mark = ","
    )
    #> Linear regression: wellbeing_score
    #>
    #>  Variable           │    B      SE       95% CI        p
    #> ────────────────────┼──────────────────────────────────────
    #>  Age (years)        │    0,03  0,03  [-0,03;  0,08]   ,343
    #>  Sex: [ref: Female] │
    #>    Male             │    3,65  0,80  [ 2,09;  5,22]  <,001
    #>  Education:         │
    #>    .L               │   13,80  0,78  [12,28; 15,32]  <,001
    #>    .Q               │   -1,71  0,66  [-3,00; -0,41]   ,010
    #>  (Intercept)        │   64,63  1,46  [61,78; 67,49]  <,001
    #> ╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
    #>  n                  │ 1200
    #>  R²                 │    0,22
    #>  Adj.R²             │    0,22
    #>
    #> Note. Linear regression.
    #> Std. errors: classical (OLS).
    #> Ordered factor `education`: polynomial trends (.L = linear, .Q = quadratic).

## Output formats

The default print method draws an ASCII table to the console. The same content is available as a plain `data.frame` (`output = "data.frame"`), a long broom-style data.frame (`output = "long"`), or as a rich-format table for `gt`, `tinytable`, `flextable`, `excel`, `word`, or `clipboard`:

    out <- table_regression(
      lm(wellbeing_score ~ age + sex + smoking, data = sochealth),
      output = "data.frame"
    )
    str(out)
    #> 'data.frame':    11 obs. of  5 variables:
    #>  $ Variable: chr  "(Intercept)" "age" "sex:" "  Female (ref.)" ...
    #>  $ B       : chr  "  65.20" "   0.05" "       " "    —  " ...
    #>  $ SE      : chr  "1.66" "0.03" "    " " —  " ...
    #>  $ 95% CI  : chr  "[61.95, 68.45]" "[-0.01,  0.11]" "              " "      —       " ...
    #>  $ p       : chr  "<.001" " .130" "     " " —   " ...
    #>  - attr(*, "title")= chr "Linear regression: wellbeing_score"
    #>  - attr(*, "note")= chr "Note. Linear regression.\nStd. errors: classical (OLS)."
    #>  - attr(*, "col_spec")=List of 4
    #>   ..$ :List of 6
    #>   .. ..$ col_name     : chr "B"
    #>   .. ..$ display_label: chr "B"
    #>   .. ..$ token        : chr "b"
    #>   .. ..$ model_id     : chr "M1"
    #>   .. ..$ estimate_type: chr "B"
    #>   .. ..$ fields       : chr "estimate"
    #>   ..$ :List of 6
    #>   .. ..$ col_name     : chr "SE"
    #>   .. ..$ display_label: chr "SE"
    #>   .. ..$ token        : chr "se"
    #>   .. ..$ model_id     : chr "M1"
    #>   .. ..$ estimate_type: chr "B"
    #>   .. ..$ fields       : chr "se"
    #>   ..$ :List of 6
    #>   .. ..$ col_name     : chr "95% CI"
    #>   .. ..$ display_label: chr "95% CI"
    #>   .. ..$ token        : chr "ci"
    #>   .. ..$ model_id     : chr "M1"
    #>   .. ..$ estimate_type: chr "B"
    #>   .. ..$ fields       : chr [1:2] "ci_low" "ci_high"
    #>   ..$ :List of 6
    #>   .. ..$ col_name     : chr "p"
    #>   .. ..$ display_label: chr "p"
    #>   .. ..$ token        : chr "p"
    #>   .. ..$ model_id     : chr "M1"
    #>   .. ..$ estimate_type: chr "B"
    #>   .. ..$ fields       : chr "p_value"
    #>  - attr(*, "group_sep_rows")= int 9
    #>  - attr(*, "align")= chr "decimal"
    #>  - attr(*, "decimal_mark")= chr "."
    #>  - attr(*, "structured")=List of 10
    #>   ..$ body              :'data.frame':   11 obs. of  6 variables:
    #>   .. ..$ Variable  : chr [1:11] "(Intercept)" "age" "sex:" "  Female (ref.)" ...
    #>   .. ..$ B         : num [1:11] 65.2009 0.0465 NA NA 3.8558 ...
    #>   .. ..$ SE        : num [1:11] 1.6567 0.0307 NA NA 0.9053 ...
    #>   .. ..$ 95% CI: LL: num [1:11] 61.9504 -0.0137 NA NA 2.0796 ...
    #>   .. ..$ 95% CI: UL: num [1:11] 68.451 0.107 NA NA 5.632 ...
    #>   .. ..$ p         : num [1:11] 1.59e-216 1.30e-01 NA NA 2.22e-05 ...
    #>   ..$ reference_rows    : int [1:2] 4 7
    #>   ..$ factor_header_rows: int [1:2] 3 6
    #>   ..$ fit_stat_rows     : int [1:3] 9 10 11
    #>   ..$ level_rows        : int [1:4] 4 5 7 8
    #>   ..$ outcome_row       : int(0)
    #>   ..$ col_meta          :List of 5
    #>   .. ..$ B         :List of 12
    #>   .. .. ..$ token             : chr "b"
    #>   .. .. ..$ model_id          : chr "M1"
    #>   .. .. ..$ source_field      : chr "estimate"
    #>   .. .. ..$ precision         : int 2
    #>   .. .. ..$ p_style           : NULL
    #>   .. .. ..$ threshold         : NULL
    #>   .. .. ..$ ci_role           : NULL
    #>   .. .. ..$ ci_pair           : NULL
    #>   .. .. ..$ ci_label          : NULL
    #>   .. .. ..$ is_df             : logi FALSE
    #>   .. .. ..$ display_label     : chr "B"
    #>   .. .. ..$ fit_stat_overrides:List of 3
    #>   .. .. .. ..$ :List of 5
    #>   .. .. .. .. ..$ fit_stat : chr "nobs"
    #>   .. .. .. .. ..$ precision: int 0
    #>   .. .. .. .. ..$ p_style  : NULL
    #>   .. .. .. .. ..$ threshold: NULL
    #>   .. .. .. .. ..$ row      : int 9
    #>   .. .. .. ..$ :List of 5
    #>   .. .. .. .. ..$ fit_stat : chr "r2"
    #>   .. .. .. .. ..$ precision: int 2
    #>   .. .. .. .. ..$ p_style  : NULL
    #>   .. .. .. .. ..$ threshold: NULL
    #>   .. .. .. .. ..$ row      : int 10
    #>   .. .. .. ..$ :List of 5
    #>   .. .. .. .. ..$ fit_stat : chr "adj_r2"
    #>   .. .. .. .. ..$ precision: int 2
    #>   .. .. .. .. ..$ p_style  : NULL
    #>   .. .. .. .. ..$ threshold: NULL
    #>   .. .. .. .. ..$ row      : int 11
    #>   .. ..$ SE        :List of 11
    #>   .. .. ..$ token        : chr "se"
    #>   .. .. ..$ model_id     : chr "M1"
    #>   .. .. ..$ source_field : chr "se"
    #>   .. .. ..$ precision    : int 2
    #>   .. .. ..$ p_style      : NULL
    #>   .. .. ..$ threshold    : NULL
    #>   .. .. ..$ ci_role      : NULL
    #>   .. .. ..$ ci_pair      : NULL
    #>   .. .. ..$ ci_label     : NULL
    #>   .. .. ..$ is_df        : logi FALSE
    #>   .. .. ..$ display_label: chr "SE"
    #>   .. ..$ 95% CI: LL:List of 11
    #>   .. .. ..$ token        : chr "ci"
    #>   .. .. ..$ model_id     : chr "M1"
    #>   .. .. ..$ source_field : chr "ci_low"
    #>   .. .. ..$ precision    : int 2
    #>   .. .. ..$ p_style      : NULL
    #>   .. .. ..$ threshold    : NULL
    #>   .. .. ..$ ci_role      : chr "LL"
    #>   .. .. ..$ ci_pair      : chr "95% CI: UL"
    #>   .. .. ..$ ci_label     : chr "95% CI"
    #>   .. .. ..$ is_df        : logi FALSE
    #>   .. .. ..$ display_label: chr "95% CI"
    #>   .. ..$ 95% CI: UL:List of 11
    #>   .. .. ..$ token        : chr "ci"
    #>   .. .. ..$ model_id     : chr "M1"
    #>   .. .. ..$ source_field : chr "ci_high"
    #>   .. .. ..$ precision    : int 2
    #>   .. .. ..$ p_style      : NULL
    #>   .. .. ..$ threshold    : NULL
    #>   .. .. ..$ ci_role      : chr "UL"
    #>   .. .. ..$ ci_pair      : chr "95% CI: LL"
    #>   .. .. ..$ ci_label     : chr "95% CI"
    #>   .. .. ..$ is_df        : logi FALSE
    #>   .. .. ..$ display_label: chr "95% CI"
    #>   .. ..$ p         :List of 11
    #>   .. .. ..$ token        : chr "p"
    #>   .. .. ..$ model_id     : chr "M1"
    #>   .. .. ..$ source_field : chr "p_value"
    #>   .. .. ..$ precision    : int 3
    #>   .. .. ..$ p_style      : chr "apa"
    #>   .. .. ..$ threshold    : num 0.001
    #>   .. .. ..$ ci_role      : NULL
    #>   .. .. ..$ ci_pair      : NULL
    #>   .. .. ..$ ci_label     : NULL
    #>   .. .. ..$ is_df        : logi FALSE
    #>   .. .. ..$ display_label: chr "p"
    #>   ..$ spanners          : NULL
    #>   ..$ ci_pairs          :List of 1
    #>   .. ..$ :List of 2
    #>   .. .. ..$ label: chr "95% CI"
    #>   .. .. ..$ cols : int [1:2] 4 5
    #>   ..$ format_spec       :List of 9
    #>   .. ..$ decimal_mark      : chr "."
    #>   .. ..$ digits            : int 2
    #>   .. ..$ p_digits          : int 3
    #>   .. ..$ effect_size_digits: int 2
    #>   .. ..$ fit_digits        : int 2
    #>   .. ..$ ic_digits         : int 1
    #>   .. ..$ p_style           : chr "apa"
    #>   .. ..$ p_threshold       : num 0.001
    #>   .. ..$ ci_level          : num 0.95
    #>  - attr(*, "padding")= int 0
    #>  - attr(*, "fit_stats_layout")= chr "first_col"

    out <- table_regression(
      lm(wellbeing_score ~ age + sex + smoking, data = sochealth),
      output = "long"
    )
    out[, c("model_id", "term", "estimate_type", "estimate",
            "std.error", "p.value")]
    #>   model_id        term estimate_type    estimate  std.error       p.value
    #> 1       M1 (Intercept)             B 65.20085505 1.65670747 1.591088e-216
    #> 2       M1         age             B  0.04649213 0.03069709  1.301575e-01
    #> 3       M1   sexFemale             B          NA         NA            NA
    #> 4       M1     sexMale             B  3.85579323 0.90528970  2.216170e-05
    #> 5       M1   smokingNo             B          NA         NA            NA
    #> 6       M1  smokingYes             B -1.71871310 1.10751281  1.209641e-01

    pkgdown_dark_gt(
      table_regression(
        lm(wellbeing_score ~ age + sex + smoking, data = sochealth),
        output = "gt"
      )
    )

## broom integration

`broom::tidy()` returns a long tibble with one row per `(model_id, term, estimate_type)` and broom-canonical column names (`estimate`, `std.error`, `conf.low`, `conf.high`, `statistic`, `p.value`). `broom::glance()` returns one row per `(model_id, outcome)` with model-level statistics; `df.residual` is preserved as a numeric so cluster-robust Satterthwaite df flows through verbatim:

    fit <- lm(wellbeing_score ~ age + sex + smoking, data = sochealth)
    out <- table_regression(fit, standardized = "refit")

    broom::tidy(out)
    #> # A tibble: 8 × 15
    #>   model_id outcome     term  estimate_type estimate std.error conf.low conf.high
    #>   <chr>    <chr>       <chr> <chr>            <dbl>     <dbl>    <dbl>     <dbl>
    #> 1 M1       wellbeing_… (Int… B              65.2       1.66    62.0      68.5
    #> 2 M1       wellbeing_… (Int… beta           -0.0961    0.0431  -0.181    -0.0116
    #> 3 M1       wellbeing_… age   B               0.0465    0.0307  -0.0137    0.107
    #> 4 M1       wellbeing_… age   beta            0.0439    0.0290  -0.0130    0.101
    #> 5 M1       wellbeing_… sexM… B               3.86      0.905    2.08      5.63
    #> 6 M1       wellbeing_… sexM… beta            0.247     0.0579   0.133     0.360
    #> 7 M1       wellbeing_… smok… B              -1.72      1.11    -3.89      0.454
    #> 8 M1       wellbeing_… smok… beta           -0.110     0.0708  -0.249     0.0290
    #> # ℹ 7 more variables: statistic <dbl>, df <dbl>, p.value <dbl>,
    #> #   test_type <chr>, is_intercept <lgl>, factor_term <chr>, factor_level <chr>
    broom::glance(out)
    #> # A tibble: 1 × 15
    #>   model_id outcome       nobs weighted_nobs r.squared adj.r.squared omega2 sigma
    #>   <chr>    <chr>        <int>         <dbl>     <dbl>         <dbl>  <dbl> <dbl>
    #> 1 M1       wellbeing_s…  1175            NA    0.0190        0.0165 0.0165  15.5
    #> # ℹ 7 more variables: rmse <dbl>, f2 <dbl>, AIC <dbl>, AICc <dbl>, BIC <dbl>,
    #> #   deviance <dbl>, df.residual <dbl>

The long format is the right entry point when the table is one step in a larger pipeline — saving to disk for the manuscript appendix, faceting by subgroup, or feeding a downstream post-estimation analysis. The `tidy()` output keeps the `estimate_type` column so the same data frame can hold rows for B, β, AME, and per-coefficient effect-size estimates without ambiguity.

## See also

- `vignette("table-continuous-lm", package = "spicy")` for the one-predictor-by-many-outcomes counterpart (estimated marginal means, contrast or slope, four effect-size families).
- `vignette("summary-tables-reporting", package = "spicy")` for an end-to-end reporting workflow combining the spicy summary- table helpers.

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