Forest and Estimation Plots with ggestimates()

From a table of estimates

The simplest case: a data frame with an estimate and its confidence bounds. For ratios (odds/hazard/risk ratios) set log.scale = TRUE so the axis is symmetric around the null, and put the reference line at 1.

or <- data.frame(
  term = c("Age (per year)", "Male sex", "BMI", "Current smoker", "Treatment"),
  estimate = c(1.03, 1.45, 0.98, 2.10, 0.62),
  conf.low = c(0.99, 1.05, 0.94, 1.40, 0.45),
  conf.high = c(1.07, 2.00, 1.02, 3.15, 0.85)
)

ggestimates(or, label = "term", ref.line = 1, log.scale = TRUE,
  xlab = "Odds ratio (95% CI)")

Each row shows the point estimate (filled square) and its 95% interval; the right-hand column prints the same numbers, and the dashed line marks the null (no effect). An interval that crosses the line is not significant at that level.

From a fitted model

The usual workflow: fit a model, pull out the coefficients and confidence intervals, and hand them to ggestimates(). Here we model survival on the Titanic with logistic regression and plot the odds ratios – all in base R.

data("titanic.raw", package = "datarium")

fit <- glm(Survived ~ Class + Sex + Age, data = titanic.raw,
  family = binomial)

# Odds ratios = exp(coefficients); 95% CI = exp(confint)
est <- data.frame(
  term = c("2nd class", "3rd class", "Crew", "Female", "Adult"),
  estimate = exp(coef(fit))[-1],
  conf.low = exp(confint.default(fit))[-1, 1],
  conf.high = exp(confint.default(fit))[-1, 2]
)

ggestimates(est, label = "term", ref.line = 1, log.scale = TRUE,
  sort = "estimate", xlab = "Odds ratio of survival (95% CI)",
  title = "Titanic survival")

Read against the reference groups (1st class, male, child): women had far higher odds of survival, while 3rd-class passengers and adults had lower odds. The same recipe works for any model – use exp(coef)/exp(confint) for a Cox or logistic model, or the raw coefficients (below) for a linear one.

Mean differences and linear coefficients

For estimates on the original (additive) scale – mean differences, linear-model coefficients – keep log.scale = FALSE (the default) and put the reference line at 0.

md <- data.frame(
  group = c("Low dose", "Medium dose", "High dose"),
  estimate = c(1.8, 3.6, 5.9),
  conf.low = c(0.4, 2.1, 4.0),
  conf.high = c(3.2, 5.1, 7.8)
)

ggestimates(md, label = "group", color = "group", palette = "jco",
  ref.line = 0, xlab = "Mean difference vs control (95% CI)")

Sorting, banding, and the estimate column

Order the rows by their estimate with sort = "estimate", add zebra banding to guide the eye across a wide figure, and tune the right-hand estimate column with ci.text.title and digits:

ggestimates(or, label = "term",
  ref.line = 1, log.scale = TRUE,
  sort = "estimate", descending = TRUE,
  banding = TRUE,
  ci.text.title = "OR (95% CI)", digits = 2,
  xlab = "Odds ratio (95% CI)")

Turn the estimate column off with ci.text = FALSE, weight the point size by a column (e.g. a study’s sample size) with size =, and see ?ggestimates for the full argument list.

A meta-analysis forest

The same primitive draws a meta-analysis: one row per study plus a pooled estimate. Weight the study markers by their precision with size =.

meta <- data.frame(
  study = c("Study A", "Study B", "Study C", "Study D", "Pooled"),
  estimate = c(0.82, 0.75, 0.91, 0.68, 0.79),
  conf.low = c(0.61, 0.55, 0.70, 0.48, 0.70),
  conf.high = c(1.10, 1.02, 1.18, 0.96, 0.89),
  weight = c(0.8, 1.0, 0.9, 0.7, 2.2)
)

ggestimates(meta, label = "study", size = "weight",
  ref.line = 1, log.scale = TRUE,
  ci.text.title = "RR (95% CI)",
  xlab = "Risk ratio (95% CI)")

The larger pooled marker sits below the individual studies with a tighter interval – the familiar bottom row of a meta-analysis forest.