Forest plot of effect estimates — forest

Draw a forest plot of effect estimates with confidence intervals, for example to compare a variant across cohorts, several lead variants from one study, or meta-analysis inputs. Estimates and standard errors are turned into confidence intervals; optionally the effects are exponentiated to display odds or hazard ratios.

Usage

forest_plot(
  data,
  effect = "BETA",
  se = "SE",
  label = NULL,
  group = NULL,
  ci = 0.95,
  exponentiate = FALSE,
  ref_line = if (exponentiate) 1 else 0,
  order_by = c("none", "effect", "label"),
  colors = NULL,
  point_size = 2.5,
  x_label = NULL,
  title = NULL
)

Arguments

data: A data.frame (or gwas_data) with effect and standard-error columns.
effect, se: Column names for the effect estimate and its standard error.
label: Column used for the row labels. Defaults to SNP when present, otherwise the row index.
group: Optional grouping column (e.g. cohort); groups are colored and offset within each label.
ci: Confidence level for the intervals (default 0.95).
exponentiate: If TRUE, plot exp(effect) on a log axis (odds/hazard ratios) and default the reference line to 1.
ref_line: Position of the vertical reference line.
order_by: Order rows by "none", "effect", or "label".
colors: Optional colors for groups.
point_size: Point size.
x_label: Optional x-axis label.
title: Plot title.

Value

A ggplot object.

Examples

df <- data.frame(
  SNP = c("rs1", "rs2", "rs3", "rs4"),
  BETA = c(0.20, -0.10, 0.35, 0.05),
  SE = c(0.05, 0.04, 0.08, 0.03)
)
forest_plot(df)


# Odds ratios, ordered by effect
forest_plot(df, exponentiate = TRUE, order_by = "effect")