ggmeta 0.1.0

First CRAN release.

ggmeta extends ‘ggplot2’ to build publication-quality forest and funnel plots from meta objects or tidy data frames. Every plot is an ordinary ggplot, so it can be themed, composed (for example a forest and a funnel plot side by side with patchwork), and saved like any other.

Forest plots

• ggforest() draws a forest plot from a meta object or a tidy data frame, with study confidence intervals and weight-proportional squares, common- and random-effects summary diamonds, prediction intervals, and null-effect and consensus reference lines.
• columns = TRUE adds a meta::forest()-style table of effect-estimate, 95% CI, and weight columns (or a chosen subset), aligned on both linear and log axes.
• add_summary = TRUE pools a tidy data frame of effect sizes on the fly (inverse-variance common effect and DerSimonian-Laird random effects), so a summary diamond can be drawn without the meta package.
• Journal presets layout_jama(), layout_bmj(), and layout_revman5().

Funnel plots

• ggfunnel() draws a funnel plot (study effect against standard error) with pseudo confidence-interval contours, from a meta object or a tidy data frame. Ratio, proportion, rate, and correlation measures are drawn on their analysis scale but labelled with back-transformed values.

Building blocks and customisation

• Composable geometries: geom_forest_ci(), geom_forest_diamond(), geom_forest_ref(), geom_forest_predict(), geom_forest_text(), and geom_funnel_contour(); helpers tidy_meta(), fortify.meta(), and format_effect(); themes theme_forest() and theme_funnel().
• ggforest() and ggfunnel() take per-element styling arguments (for example predict_args, diamond_colours, ci_args, ref_args, point_args, contour_args) to restyle any built-in layer.
• Every summary measure is back-transformed with its correct inverse (exponentiation for ratios, inverse-logit for logit proportions, Fisher’s z for correlations, and so on).