The outcome’s type chooses the model and the observed quantity to compare with

You rarely set family by hand — tab_reg() mostly detects it:

That last column is the measure the model estimates, not a ceiling: any other measure can still be reported from the same fit, and measure = is how you ask. Four arguments chain — family says what kind of number the outcome is, link which measure the model estimates, measure which one is reported, effect where that number comes from — and each follows from the one before unless you say otherwise. The full grid is in What a model column reports below.

With empirical = TRUE, each model column is joined by a crude/observed companion column showing the observed, unadjusted (univariable) effect — the effect you would see with no controls at all, “all things being unequal”, for that predictor. The two columns are the same column twice, one estimand computed with one predictor and with all of them: same colour ladder, same layout, one legend. Each cell prints the effect with the level it sits on beside it — the observed percentage or mean on the crude side, the model-adjusted prediction on the model side — so the two effects sit next to each other in the middle and the comparison is read straight across.

One rule covers every case: the observed effect is the effect of that predictor alone. With a categorical predictor that is exactly the observed contrast between levels (a percentage difference, an odds ratio computed from the raw percentages). With a continuous predictor there is no such shortcut, so it is the univariable slope — which assumes the effect is linear on the model’s scale, something worth checking (with cut(), or splines) before trusting it.

• 2 levels logistic (binomial) → observed %, and observed odd-ratios
• 3+ levels logistic (multinomial) → the observed ORs are shown as a tooltip on the model cells in html exports
• gaussian (linear) → group means and their difference
• poisson (counts) → observed rate and observed rate ratio

A continuous predictor has no levels, so its cell shows the effect alone — there is no observed percentage or mean to put beside it — and its distribution (mean, standard deviation, and the mean in each outcome group) is shown in the html tooltip instead.

On weighted data, one thing is worth knowing from the start: an observed column is always measured exactly like the model column beside it, so its confidence interval accounts for the weighting (and, under a survey design, for the whole design). A tab() cross-table does not, by default — add design_effect = TRUE there if you want its percentages directly comparable with these. See Weighted and survey data below.

Every outcome has an observed counterpart

empirical = TRUE answers one question — “how much of this association survives once I adjust for the rest?” — and it works for every kind of outcome. The rule behind it is a single sentence:

The observed effect is the model’s own effect, fitted with one predictor.

That is worth saying plainly, because it is what makes the comparison fair. The crude number is not “a percentage” and the modelled one “a coefficient” — they are the same quantity, computed the same way, on the same people (the same complete cases), and shown on the same scale. Only the list of predictors differs. So the distance between them measures adjustment, and nothing else.

The “same people” part is the default, not a hope: na = "drop_by_outcome" gives every model of an outcome one complete-case population, so a compared model cannot be fitted on rows the observed columns do not cover. If you ask for na = "drop_by_model" instead, a model on a different population gets no observed effect at all — better an empty cell than a “gap” that is really listwise deletion.

A word on how to read it. The colour says how much two numbers differ, and the greying whether that difference exceeds noise. It does not say “this is a confounder”: the 10% first threshold is a convention, not a decision rule, and part of an odds-ratio gap is arithmetic rather than confounding (see One warning about odds ratios, below). “This effect was attenuated by adjustment” is the safe reading; “explained by” is not.

tab_reg(gss_simple, "rincome", c("race", "relig"),
        empirical = TRUE, color = c(TRUE, "adjustment"))

tab_reg(gss_simple, "rincome", c("race", "relig"), measure = "difference",
        empirical = TRUE, display = "est_base", color = c(TRUE, "adjustment"))

tab_reg(gss_simple, "party3", c("race", "relig"), family = "multinomial",
        empirical = TRUE, color = c(TRUE, "adjustment"))

tab_reg(tea, "tea_where", c("sex", "SPC", "Sport"),
        family = "binomial", trials = length(tea_where_vars),
        empirical = TRUE, color = c(TRUE, "adjustment"))

tab_reg(gss_simple, "party3", c("race", "relig"), family = "multinomial",
        measure = "difference", empirical = TRUE, color = c(TRUE, "adjustment"),
        color_signif = "grey_non_signif")

The four arguments, in plain words

effect’s three values name three quantities:

By hand you would get them with coef(glm(...)) (exponentiated when the measure is a ratio), with marginaleffects::avg_comparisons(model), and with the same call on a one-row profile where every other predictor sits at its reference level or its mean. You will rarely type any of them: "auto" picks "conditional" whenever the reported measure is the model’s own — which is exactly when a coefficient exists — and "marginal" otherwise. "conditional" earns its keep as an assertion: ask for it where no coefficient can carry your measure and the table says so, naming the two cures, instead of quietly giving you something else.

⚠ One clause qualifies "auto": it never lands on a predicted odds ratio. A marginal odds ratio is a specialist quantity (Karlson & Jann 2023), so on a prediction route "auto" steps back to the outcome’s own measure — “x times as likely” for a percentage. Name measure = "odds_ratio" to get it.

effect = "at_reference" evaluates the effect at the reference profile — the abstract individual combining the reference level of every predictor — instead of averaging it over the sample. For a multinomial model it also unlocks a contrast the other two do not have: the odds ratio of each outcome category versus all the others at that profile.

tab_reg(gss_simple, "married", c("race", "age"), effect = "at_reference")

Which model an outcome can be fitted with

link is the value set of models. Its words are measure’s own words, because a link is a measure — the one the model estimates directly — so the statistician’s vocabulary never surfaces. † marks the family’s own link, which is what link = "auto" resolves to.

family = "quasipoisson" has exactly the row of poisson — it changes the variance assumption, not what is estimated.

What each measure reports, from any of them

Which measures exist is decided by what a cell is: a percentage has an identity, a log and a logit, so all three; a mean or a count has no odds, so no odds ratio; and a rank — an ordered outcome’s cell — is compared by pairs of people rather than by shares, which is what lets an ordinal model report in one column. Every cell below is the acronym the combination puts in the column header, after the constant Model_ prefix.

The "conditional" column is the one reg_measures() shows per link; the other two are the same for every link the family fits, which is why it lists them once.

* a coefficient exists only where that measure is the link — see the previous table. Ask for one that is not, and the call is refused naming its two cures: drop effect, or fit the model that estimates it.

¹ a predicted odds ratio needs a percentage and its complement, so a 3+ category outcome has to be asked “versus what?” first: on a multinomial outcome it exists only as the versus the rest contrast at effect = "at_reference", and on an ordinal one not at all.

² a rank’s measures compare two people drawn from the population, and one profile holds only one.

reg_measures(data, outcome) prints this grid for your outcome, and prints only what can be built: a not defined combination has no row, and the message above the table says why. Its two refusals are not the same one — a quantity has no odds to take a ratio of, whatever anyone implements, while not offered means tabxplor does not build it and the error lists what this outcome does offer, at this model and at the others.

A further state exists only at run time: a link that does not converge on your data. The call says so, and for the risk difference it falls back to the linear probability model.

measure = "raw_coefficient" is not a fourth column: it is the model’s own coefficient, the estimand shown un-transformed. Where the reported measure is multiplicative that is its log, and the header says which one it logs (Model_log(OR), Model_log(IRR), Model_log(RoM)); where the model is already additive there is nothing to un-exponentiate, and the coefficient IS the additive estimate the column already shows. So it answers for every family — which is what lets a table mixing a logistic outcome and a linear one be asked for its coefficients at all. Being the fit’s own number it is always the conditional one; ask it with effect = "marginal" and the call says so and names the cure. ("coefficient", "coef", "log", "log_odds", "log_risk" and "log_rate" are accepted spellings; the last three pin which base.)

What the header words mean

A header names the measure; the contrast is a marker on it. So there is one acronym to look up per quantity, and one marker to read on top of it.

The observed companion carries the measure without a marker (Obs_RR beside Model_mRR): it is a univariable effect, and where its levels come from the counts themselves a marginal and a conditional contrast are the same number.

The one argument that changes the model: link

Everything else changes what is reported. link changes what is fitted, and that is the whole of the difference — so it is the one argument that can put an assumption in your table you did not intend. Each link buys a directly readable coefficient at the price of one:

What the literature says about the three non-default links:

• The risk ratio is a well-established route, not a workaround. link = "ratio" on a binary outcome fits Zou’s modified Poisson, which exists precisely to avoid the convergence failures of a log-binomial fit and is standard practice in epidemiology. Its own limits are different in kind from the risk difference’s: fitted risks are not bounded above by 1, and in small or sparse samples both the estimate and the sandwich standard error are biased — penalised variants exist for that case.
• The risk difference is the fragile one. An identity link is bounded on neither side, so it can predict impossible risks and can simply fail to converge. When it does, tabxplor fits the linear probability model instead and the footer says so — the two target the same quantity, but they are different estimators and agree only if the model is right.
• The ratio of means is consistent whenever the mean function is right. Poisson pseudo-likelihood does not claim your outcome is a count: it is a device for the log link, and its robust standard error does not require the Poisson variance to be correct. Nothing, however, rescues a wrong mean function — and the outcome must be non-negative.
• The default is not assumption-free either. A logistic coefficient assumes a constant odds ratio, an ordinal one assumes the same odds ratio at every cut (the footer’s Brant test checks it), and the odds ratio is non-collapsible, so it moves when a covariate is added even where there is nothing to confound.

Which route to take. If a risk ratio or a risk difference is what you want to report, the safer default is to leave the model on its family’s own scale and name the measure with measure =. Three reasons, none of them about taste: the logit always converges and can never predict a probability outside 0–100 %, while the log and identity links can do both; a marginal effect imposes no constant-effect assumption on the scale you report, since it averages the effect the fitted model actually implies at each respondent’s own profile; and every measure runs on the same fit, so changing which measure you show never changes what was estimated.

That said, the two are different questions rather than two spellings of one. A coefficient is a conditional effect — “two people alike on every other predictor” — and a marginal effect a population average. Take the link route when the conditional quantity is what you want: to line up with a published conditional estimate, or when “the effect is the same size everywhere on this scale” is the claim you actually mean to make. reg_formulas() tells you which model ran.

Caveats worth knowing

• A binary outcome with family = "poisson" is not a count model, and is refused rather than quietly rewritten. family says what kind of number the outcome is and never picks a link behind your back; the message names the two things that spelling could have meant — link = "ratio" (the modified Poisson’s conditional risk ratio) and measure = "ratio" (the marginal one).
• link = "ratio" on a numeric outcome stops on a negative outcome. A ratio of means is not defined there; the call suggests modelling log() of a positive outcome, or leaving link alone.
• Model_RD and Model_mRD are both percentage points, and are not the same number. Model_RD is a conditional risk difference from an identity-link fit (link = "difference"), Model_mRD a marginal one standardised over the logistic fit (measure = "difference") — which is what the m marker says.
• Conditional odds ratios are non-collapsible, so color = "adjustment" colours the gap on such a column but never tests it (see How to read it — and how not to, above). Ask for measure = "difference" or measure = "ratio" — or, if you must publish odds ratios, measure = "odds_ratio", effect = "marginal", whose marginal odds ratio is collapsible and whose gap the table does test.
• One combination has no observed companion. At effect = "at_reference" the model is conditional on one profile while the observed columns stay marginal over the whole sample, so the two are shown but not compared — color = "adjustment" and {obs} stay empty.
• What needs an extra package, and what has no method. effect = "at_reference" goes through marginaleffects; a survey-weighted multinomial model has no marginal-effects method at all, so there it can only be read on its coefficients — keep the model’s own measure and the call goes through. A survey-weighted ordinal model is the exception: its rank measures (measure = "difference" / "ratio") run on tabxplor’s own g-computation and take the design-based variance straight from the fit, so they work under any design — only the crude/adjusted gap test is unavailable there, and color = "adjustment" then colours without testing. (display itself reaches every column, marginal or not.)

Asking your own outcome

reg_measures() prints this grid for one outcome of your own data — it reads the very table tab_reg() resolves against, so what it prints is what the function does:

reg_measures(gss_simple, "married")

#> # A tibble: 4 × 5
#>   link       measure    effect                header    reads_as                
#>   <chr>      <chr>      <chr>                 <chr>     <chr>                   
#> 1 odds_ratio odds_ratio conditional           Model_OR  odds ratio              
#> 2 (any)      difference marginal|at_reference Model_mRD marginal risk difference
#> 3 (any)      ratio      marginal|at_reference Model_mRR marginal risk ratio     
#> 4 (any)      odds_ratio marginal|at_reference Model_mOR marginal odds ratio

It comes in two blocks. The first is the model itself: its link, and the measure that model’s own coefficients carry. The second is what is read off the model’s predictions — the same whichever link you fit, which is why those rows say link = "(any)". So what link changes is only which measure has a coefficient; every measure stays reportable from any of them.

By default the first block holds each family’s own model alone. link = "all" adds every other link it can be fitted at — specialist choices — and marks the family’s own with base_link:

reg_measures(gss_simple, "married", link = "all")

#> # A tibble: 6 × 6
#>   link       base_link measure    effect                header    reads_as      
#>   <chr>      <lgl>     <chr>      <chr>                 <chr>     <chr>         
#> 1 odds_ratio TRUE      odds_ratio conditional           Model_OR  odds ratio    
#> 2 ratio      FALSE     ratio      conditional           Model_RR  risk ratio    
#> 3 difference FALSE     difference conditional           Model_RD  risk differen…
#> 4 (any)      NA        difference marginal|at_reference Model_mRD marginal risk…
#> 5 (any)      NA        ratio      marginal|at_reference Model_mRR marginal risk…
#> 6 (any)      NA        odds_ratio marginal|at_reference Model_mOR marginal odds…

Name a link to read the table at that model alone, and a family to read it as that kind of outcome:

reg_measures(gss_simple, "married", link = "ratio")

#> # A tibble: 4 × 5
#>   link  measure    effect                header    reads_as                
#>   <chr> <chr>      <chr>                 <chr>     <chr>                   
#> 1 ratio ratio      conditional           Model_RR  risk ratio              
#> 2 (any) difference marginal|at_reference Model_mRD marginal risk difference
#> 3 (any) ratio      marginal|at_reference Model_mRR marginal risk ratio     
#> 4 (any) odds_ratio marginal|at_reference Model_mOR marginal odds ratio

The same list is in ?tab_reg, section “Which models each outcome offers, and which measures”, generated from the same source.

Logistic regression (a binary factor)

When the outcome is a two-level factor, here “married” versus “not married”, tab_reg() chooses a binomial family to fit a logistic regression and reports odds ratios (the reference level of each predictor shows the neutral value 1).

• Like in every regression model, the effect of a predictor level on the dependent variable reads “all the others chosen predictors being equals”.
• Colors read like any tabxplor table: an odds ratio above 1 (blue) means more likely to be married than the reference level, below 1 (red) means less likely; stars and colors both flag significance.

tab_reg(gss_simple, "married", c("race", "age", "rincome", "relig"))

For a simple logistic regression, empirical = TRUE adds the crude unmodelised odds ratios, each with the raw percentage it is computed from.

tab_reg(gss_simple, "married", c("race", "age", "rincome", "relig"), empirical = TRUE)

The raw percentages in Obs_OR’s brackets are the observed results everything in the model is derived from: “31% of black americans are married, compared to 52% for white americans”.

• The plain difference from reference is colored (31% - 52% = -21 points).
• Cells that are non-significantly different from the reference are greyed out (based on a Newcombe confidence interval for differences of proportions).

The modelised odds-ratios Model_OR directly compared to the observed odds-ratios Obs_OR :

• Comparing the two tells you what adjustment the model did: if a predictor’s model OR is much closer to 1 than its crude OR, the raw association was largely explained by the other predictors.
• Here, “all things being unequal”, black americans have 1/2.43 the odds of being married of white americans. “All things being equal” (more precisely : income, age and religion being equal), they still have 1/2.40. The result holds, it’s not explained by income differences or religious differences.

The observed odds-ratios Obs_OR are the same as those you can calculate from percentages only in a crosstable :

• Colors and significance stars use a Woolf OR confidence interval that matches what is done in the regression model.
• The population of the table must match the complete-cases population of the model, filtering out indivivuals with NA at any involved variable.

gss_simple |> 
  dplyr::filter(dplyr::if_all(all_of(c("race", "age", "rincome", "relig")), ~ !is.na(.) )) |> 
  tab(race, married, pct = "row", na = "drop", 
    display = "{or}", ref = "first", color = "odds_ratio", color_signif = "grey_non_signif"
   )

tab_reg(gss_simple, "married", c("race", "age", "rincome", "relig"),
        measure = "difference", empirical = TRUE)

tab_reg(gss_simple, "married", c("race", "age", "rincome", "relig"),
        measure = "ratio", empirical = TRUE)

tab_reg(gss_simple, "married", c("race", "age", "rincome", "relig"),
        measure = "odds_ratio", effect = "marginal", empirical = TRUE)

tab_reg(gss_simple, "married", c("race", "age", "rincome", "relig"),
        link = "ratio", empirical = TRUE)

reg_formulas(tab_reg(gss_simple, "married", c("race", "age"), measure = "ratio"))

#> # A tibble: 1 × 6
#>   model           outcome family   link       fit                        formula
#>   <chr>           <chr>   <chr>    <chr>      <chr>                      <chr>  
#> 1 01-Married: mRR married binomial odds_ratio "glm(binomial(\"logit\"))" marrie…

reg_formulas(tab_reg(gss_simple, "married", c("race", "age"), link    = "ratio"))

#> # A tibble: 1 × 6
#>   model          outcome family   link  fit                             formula 
#>   <chr>          <chr>   <chr>    <chr> <chr>                           <chr>   
#> 1 01-Married: RR married binomial ratio "svyglm(quasipoisson(\"log\"))" married…

Linear regression (a numeric outcome)

A continuous outcome gives plain linear regression coefficients (here we set family explicitly, because an integer like age is ambiguous — it could also be modelled as a count):

tab_reg(gss_simple, "age", c("race", "marital", "relig", "rincome"), family = "gaussian")

In the case of a linear regression, the empirical/observed counterpart of the model coefficient for a categorical predictor is simply the difference of means : here, the difference of mean age, per level of the predictor, compared to the reference level.

tab_reg(gss_simple, "age", c("race", "marital", "relig", "rincome"), family = "gaussian", empirical = TRUE)

The empirical mean ages, and differences of mean ages from reference, can be computed in a simple table with :

tab(gss_simple, "race", "age", pct = "row", digits = 2, na = "drop",
    color = "difference", ref = 1,  ci_method = c(mean_diff = "ols")
) |> 
  mutate(diff = set_display(age, "diff"))

# ols : the variance pooled over every level of the variable, so the intervals are exactly those a
#  linear regression puts on its coefficients ("student" pools the two compared groups only).

Poisson regression (a count outcome)

tab_reg(gss_simple, "tvhours", c("race", "marital", "relig", "rincome"), family = "poisson")

An incidence-rate ratio (IRR) of 1.5 means “50% more hours of TV per day”. Unweighted Poisson models automatically use dispersion-scaled (quasi-Poisson) standard errors, so over-dispersed counts get honest, wider intervals. Concretely: with an over-dispersed outcome, family = "poisson" returns CIs and p-values identical to family = "quasipoisson" and emits a warning saying so (the footer reports the dispersion); at equidispersion (≈ 1) the scaling is a no-op and the result matches a plain glm(family = poisson) — so a comparison to a hand-fit Poisson glm never surprises you.

In the case of a poisson regression, the empirical/observed counterpart of the model’s exponentiated coefficient for a categorical predictor is the ratio of means : here, the ratio of average TV hours compared to the reference level.

tab_reg(gss_simple, "tvhours", c("race", "marital", "relig", "rincome"), family = "poisson", empirical = TRUE)

The empirical average TV hours per day, and differences of rate of television watching from reference, can be computed in a simple table with :

tab(gss_simple, "race", "tvhours", pct = "row", digits = 2, na = "drop",
    color = "ratio", ref = 1,  ci_method = c(mean_ratio = "quasipoisson")
) |> 
  mutate(IRR = set_display(tvhours, "ratio"))

# the default method is a robust ratio of means (each group's own variance) ;
#  we use "quasipoisson" to match those computed by quasi-poisson regression -- one dispersion
#  estimated over every level (assumption : variance is proportional to mean). 

Grouped-binomial outcomes (a summed score)

When the outcome is a summed score — how many of several yes/no items a respondent answered one way — you model the number of “successes” out of a fixed number of items with trials =. tab_reg() then fits cbind(score, trials - score) as a binomial, so the odds ratios read on the per-item probability.

This is the natural model for a multiple-answer survey question — here the tea data prepared above, its six “where do you drink tea?” items summed into one score by score_from_lv1().

tab_reg(tea, "tea_where", c("sex", "SPC", "Sport"),
        family = "binomial", trials = length(tea_where_vars))

Each odds ratio now reads for any one place: 1/1.44 for men means that, for any of the six places, men are about 1.44 times less likely than women to drink tea there. The model treats the six items as interchangeable trials, so one odds ratio covers them all — which is exactly the assumption to keep in mind before using it.

Grouped-binomial (like Poisson) models report a Pearson dispersion check in the footer, flagging over-dispersed counts.

Ordinal and nominal outcomes (a factor with 3+ levels)

An ordered factor outcome is fit as a proportional-odds (cumulative) logistic model, and it reports in one column: its whole claim is that one number per predictor level is enough, so it never spends a column per outcome category. By default that number is the cumulative odds ratio cumOR — the model’s own coefficient, and the one measure the proportional-odds assumption makes the same at every cut:

tab_reg(gss_simple, "rincome", c("race", "age", "relig"))

A cumulative odds ratio is hard to say out loud, so ask for another measure of deviation and the same fit is read as a probability. measure = "difference" gives Somers’ D — of two people, one from this group and one from the reference group, how often does the one from this group end up higher on the scale? — with that probability itself in brackets, 50 % being a coin flip:

tab_reg(gss_simple, "rincome", c("race", "age", "relig"),
        measure = "difference", display = "est_base")

measure = "ratio" reads the same pair multiplicatively, as a win ratio (wins to losses). Both still take one column, because both read the whole predicted distribution rather than one slice of it — and both are robust where the cumulative odds ratio is not: they barely move when the proportional-odds assumption fails, and, unlike an odds ratio, they do not drift under adjustment when there is nothing to adjust for. The footer names the scale from low to high, since a one-column table shows the outcome’s name and none of its categories.

If you want a number per outcome category — a percentage-point effect on each income band — that is a question the ordering does not help with, and family = "multinomial" answers it without assuming proportional odds.

A nominal outcome with three or more unordered levels is fit as one multinomial logistic model, giving one odds-ratio column per outcome category versus the reference category (also called relative risks ratios) :

tab_reg(gss_simple, "party3", c("race", "age", "rincome", "relig"))

Relative risk ratios may be quite difficult to read because they are relative to two reference levels : not only the reference chosen for the predictor, but also the reference level chosen for the dependent variable. It’s specially difficult when it’s difficult to find a good reference level corresponding to the most common situation (like “married” for matrimonial status).

Most of the time, asking for percentage points instead is easier to interpret, because it makes the second reference level disappear and modelises, for each level of the outcome, the difference of percentages of each predictor level compared to its reference (a less abstract quantity than odds ratios). It is measure = "difference", exactly as on a binary outcome, and the resulting mRD columns are average marginal effects:

tab_reg(gss_simple, "party3", c("race", "age", "rincome", "relig"), measure = "difference", empirical = TRUE) # |> tab_export()

A 3+ level outcome would need one crude column per outcome category, so empirical = TRUE folds the crude effect into the model cell instead — +40.6% (+38.6%), modelled then observed — and the crude percentages and differences appear in html tooltips, at mouse hover of a cell. Ask for the columns anyway with empirical = "column".

Colouring what adjustment did

empirical = TRUE puts the crude effect next to the modelled one, but comparing them cell by cell is tedious on a real table. color = "adjustment" colours the gap between the two, so a whole table of “what did adjusting change?” reads at a glance. Put it on the background channel and the text keeps showing the effect size, so one look answers both questions:

tab_reg(gss_simple, "married", c("race", "rincome", "relig"),
        empirical = TRUE, color = c(TRUE, "adjustment"))

Text colour = how strong the adjusted odds ratio is. Background = how far it sits from the observed one: one pole means adjustment strengthened the effect (it moved further from “no effect”), the other that it attenuated it (it moved closer). An effect that changed direction under adjustment — crude above 1, modelled below it — counts as attenuated, and strongly so: whatever the raw association said, the model says it is not that. You can see the reversal itself in the pair of cells, whose × and ÷ face opposite ways. The first threshold is ×1.1, the classic “a 10% change in the estimate is worth noticing” convention; the rest are ×1.25, ×1.5 and ×2 (change them with set_color_breaks(adj_ratio = ...)). An additive effect gets an additive ladder instead: percentage points for a marginal effect on a probability (±2 / ±5 / ±10 / ±20), and standard deviations of the outcome for a linear mean difference (±0.05 / ±0.1 / ±0.2 / ±0.4) — so the same model reads the same way whether the outcome is recorded in hours, minutes or days. You do not have to ask for empirical separately — the colour needs the observed effect, so it turns it on for you.

The direction is measured from the null, not up or down. A protective effect (OR below 1) that adjustment pulls toward 1 gets the same colour as a risky effect pulled toward 1, which is what you want to read: “the other variables explained part of this association” is one statement, whichever side of 1 the effect is on.

The same gap is readable as a number when you want the exact values, in the cell or on hover. display = writes the template at build time (part 5, Fine-tuning the display); on a finished table set_display() does the same — the columns that have no observed counterpart simply keep their plain display:

tab_reg(gss_simple, "married", c("race", "rincome"), empirical = TRUE) |>
  set_display("{est} (obs {obs})")

tab_reg(gss_simple, "married", c("race", "rincome", "relig"),
        measure = "ratio", empirical = TRUE, color = c(TRUE, "adjustment"))

tab_reg(gss_simple, "married", c("race", "rincome", "relig"),
        link = "ratio", empirical = TRUE,
        color = c(TRUE, "adjustment"), color_signif = "grey_non_signif")

set.seed(1)
small <- gss_simple[sample(nrow(gss_simple), 2000), ]
tab_reg(small, "married", c("race", "rincome", "relig"),
        link = "ratio", empirical = TRUE,
        color = c(TRUE, "adjustment"), color_signif = "grey_non_signif")

Fine-tuning the display, and scaling a predictor

A few arguments change what each cell shows, or how a predictor is scaled — without changing the model itself.

display chooses the cell layout — the same named layouts tab() offers, in the same {} grammar. "est_ci" shows the confidence interval beside every estimate (any family):

tab_reg(gss_simple, "married", c("race", "age"), display = "est_ci")

"est_base" folds the model-adjusted prediction into the estimate cell — an adjusted probability on a logistic model, an adjusted mean on a linear or a count one, each column answering with its own quantity. "base_est" swaps them, so the table reads as adjusted predictions graded by the effect, and "base" shows the predictions alone:

tab_reg(gss_simple, "married", c("race", "age"), display = "est_base")

Note the difference: display = "est_base" keeps the odds-ratio column and adds the adjusted probability beside it, whereas measure = "difference" (seen above) turns the whole column into a marginal effect in points.

multiplier chooses the unit a continuous predictor’s effect is reported per. One unit is rarely a readable amount: a one-year change in age barely moves the odds, so its odds ratio sits near 1, never crosses a colour threshold, and the row reads as “no effect” — while a whole standard deviation of age multiplies the odds by about 0.66. So the default is per two standard deviations, and the row says which (per 34.6 (2SD)) — the variable is named in the column beside it. Two standard deviations is roughly the span of a binary predictor, which is the point: it puts a continuous row and a two-level row on the same footing, so a whole table can be read down its column:

tab_reg(gss_simple, "married", c("race", "age"))

Pass a single value to change it for every continuous predictor, or a named vector to override chosen ones — "sd", "2sd" (roughly bottom to top of the distribution), or a number of units. multiplier = 1 gives the raw per-one-unit effect:

tab_reg(gss_simple, "married", c("race", "age"), multiplier = c(age = 10))

Everything scales together — the estimate, its interval, the observed Obs_* companion and the model-versus-observed comparison — and the p-value never changes. Two things worth knowing: because the default is not 1, a continuous predictor’s Model_OR will not match exp(coef(glm(...))) unless you ask for multiplier = 1; and the standard deviation is measured once, on the predictors’ complete cases, so the same variable keeps the same unit across several outcomes, compared models and tab_vars groups.

tab_reg(gss_simple, "married", c("race", "age"), ref = c(race = "Black", age = 40))