An estimation plot puts the effect size and its uncertainty at the centre of the comparison, rather than a p-value. The upper panel shows each group's raw data (jittered) with its mean and confidence interval; the lower panel shows the pairwise mean difference(s) against a reference group, each with a bootstrap confidence interval. The two panels share an aligned outcome axis and are stacked with 'patchwork', so a difference of zero in the lower panel lines up with the reference group's mean above it.
Arguments
- data
A data frame.
- y
The numeric outcome (string or unquoted name).
- group
The grouping variable (string or unquoted name).
- reference
The reference (control) group that the others are compared with; defaults to the first level of
group. The reference is drawn first in the upper panel and sits at a difference of zero in the lower panel.- conf_level
Confidence level for both the group intervals (t-based) and the bootstrap difference intervals.
- n_boot
Number of bootstrap resamples for the difference intervals.
- effsize
Standardised effect size annotated beside each difference:
"hedges_g"(the default, small-sample corrected),"cohens_d", or"none"to omit it. Every contrast against the reference is labelled, so the standardised effect is shown for both the two-group and multi-group cases.- show_points
Whether to draw the raw data behind the group means.
- point_alpha
Transparency of the raw points.
- palette
Colours for the groups; defaults to
depictr_palette().- title, y_lab
Title and outcome-axis label.
- heights
Relative heights of the upper (raw data) and lower (difference) panels, passed to
patchwork::plot_layout().
Value
A 'patchwork' object (printable like a ggplot2::ggplot). The
computed differences and their bootstrap intervals are attached as the
"differences" attribute (a data frame).
Details
With exactly two groups this is the classic Gardner-Altman two-group plot (Gardner & Altman, 1986; Ho et al., 2019): a single mean difference is shown with its bootstrap interval, annotated with a standardised effect size (Cohen's d or, by default, the small-sample corrected Hedges' g; Hedges, 1981). With more than two groups it becomes a Cumming estimation plot (Cumming, 2012): every other group is compared with the reference group, each difference carrying its own bootstrap interval.
The lower-panel interval is a non-parametric bootstrap of the mean
difference: the two groups are resampled with replacement n_boot times and
the requested percentile interval is read off the resampled differences. This
makes no normality assumption about the sampling distribution of the
difference. The bootstrap uses base R only; set a seed beforehand for
reproducibility.
A group with fewer than two observations has no estimable spread, so its confidence interval is omitted (the mean is still drawn) and a difference involving it is shown as a point without a bootstrap interval; a warning is issued in both cases.
References
Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge.
Gardner, M. J., & Altman, D. G. (1986). Confidence intervals rather than P values: Estimation rather than hypothesis testing. BMJ, 292(6522), 746-750. doi:10.1136/bmj.292.6522.746
Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107-128. doi:10.3102/10769986006002107
Ho, J., Tumkaya, T., Aryal, S., Choi, H., & Claridge-Chang, A. (2019). Moving beyond P values: Data analysis with estimation graphics. Nature Methods, 16(7), 565-566. doi:10.1038/s41592-019-0470-3
Examples
set.seed(1)
# n_boot is kept small here for speed; use the default for real work.
estimation_plot(lexical_decision, RT, condition, n_boot = 1000)
# \donttest{
estimation_plot(crop_yield, yield, treatment)
# More than two groups: differences vs a chosen reference
estimation_plot(wellbeing_survey, life_satisfaction, region,
reference = "North")
# }