DMAR (version 1.0.0)

plot_forest: Forest Plot of Study Effect Sizes With the Pooled Estimate

Description

Draws the meta-analyst's central picture: every study's effect size with its confidence interval, the random effects pooled estimate beneath them, and, by default, the prediction interval showing where the effect of a new study is expected to land. Point sizes are proportional to precision (inverse variance), so the eye weighs the studies the way the model does. Requires ggplot2.

Usage

plot_forest(
  yi,
  vi,
  labels = NULL,
  method = c("reml", "pm", "dl", "fe"),
  hartung_knapp = TRUE,
  conf_level = 0.95,
  show_prediction = TRUE,
  xlab = "Effect size",
  title = NULL,
  palette = "okabe_ito",
  colors = NULL
)

Value

A ggplot object; print it, or add further layers.

Arguments

yi

Numeric vector of study effect sizes.

vi

Sampling variances of yi.

labels

Optional study labels, one per study; defaults to "Study 1", "Study 2", and so on, in the supplied order.

method, hartung_knapp

Passed to meta_es for the pooled row ("reml" and TRUE by default).

conf_level

Confidence level for the per-study and pooled intervals. Defaults to 0.95.

show_prediction

Logical: draw the prediction interval band on the pooled row? Default TRUE (ignored for method = "fe", which has none).

xlab

Label for the effect size axis. Defaults to "Effect size".

title

Optional plot title.

palette

Palette name. Defaults to "okabe_ito", base R's colorblind-safe Okabe-Ito palette; "tableau" is also available.

colors

Optional length-2 vector overriding the palette: the study color and the pooled-estimate color.

Author

Ken Kelley [email protected]

See Also

meta_es, meta_smd, and meta_r for the numbers behind the picture; teacher_expectancy for the example data.

Other meta-analysis: combine_p(), meta_contrast(), meta_es(), meta_r(), meta_smd()

Other plotting: plot_R2(), plot_cfa_k(), plot_ci(), plot_equivalence(), plot_irt_information(), plot_mediation_mbco(), plot_randomization_test(), plot_regions_of_significance(), plot_smd(), plot_trajectories(), plot_trajectories_fitted(), power_equivalence_md_plot()

Examples

# Twelve simulated studies whose true effects vary from study to study
# (between-study standard deviation 0.35), so the prediction interval
# for the effect of a new study is visibly wider than the confidence
# interval for the mean effect.
set.seed(113)
k <- 12
n <- sample(20:100, k)                    # per-group sample sizes
theta <- rnorm(k, mean = 0.4, sd = 0.35)  # true study effects
d <- rnorm(k, mean = theta, sd = sqrt(2 / n))
v <- 2 / n + d^2 / (4 * n)
plot_forest(d, v, xlab = "Standardized mean difference (d)")

# The teacher expectancy literature (Raudenbush, 1984): most studies
# cluster near zero, the estimated between-study variance is zero, and
# the prediction interval nearly coincides with the confidence interval.
data(teacher_expectancy)
d <- teacher_expectancy$d
n_e <- teacher_expectancy$n_experimental
n_c <- teacher_expectancy$n_control
v <- (n_e + n_c) / (n_e * n_c) + d^2 / (2 * (n_e + n_c))
plot_forest(d, v, labels = teacher_expectancy$author,
            xlab = "Standardized mean difference (d)")