R Package for effect size visualization and estimation.
This package is designed to help you very quickly estimate and visualize distributional differences by categorical factors (e.g., the effect of treatment by gender and income category). Emphasis is placed on evaluating distributional differences across the entirety of the scale, rather than only by measures of central tendency (e.g., means).
Install directly from CRAN with
install.packages("esvis")Or the development version (heavily updated, a new CRAN release is planned soon) from GitHub with:
# install.packages("devtools")
devtools::install_github("datalorax/esvis")There are three primary data visualizations: (a) binned effect size
plots, (b) probability-probability plots, and (c) empirical cumulative
distribution functions. All plots use the
ggplot2 package and are fully
manipulable after creation using standard ggplot commands (e.g.,
changing the theme, labels, etc.). These plots were all produced by
first running library(ggplot2); theme_set(theme_minimal()) to produce
the plots with the minimal theme, but no theme structure is imposed on
any of the plots.
At present, the binned effect size plot can only be produced with Cohen’s d, although future development will allow the user to select the type of effect size. The binned effect size plot splits the distribution into quantiles specified by the user (defaults to lower, middle, and upper thirds), calculates the mean difference between groups within each quantile bin, and produces an effect size for each bin by dividing by the overall pooled standard deviation (i.e., not by quantile). For example
library(esvis)
binned_plot(benchmarks, math ~ ell)
Note that in this plot one can clearly see that the magnitude of the
differences between the groups depends upon scale location, as evidence
by the reversal of the effect (negative to positive) for the Non-ELL
(non-English Language Learners) group. We could also change the
reference group, change the level of quantile binning, and evaluate the
effect within other factors. For example, we can look by season
eligibility for free or reduced price lunch, with quintiles binning, and
non-ELL students as the reference group with
binned_plot(benchmarks,
math ~ ell + frl + season,
ref_group = "Non-ELL",
qtile_groups = 5)
The ref_group argument can also supplied as a formula.
Probability-probability plot can be produced with a call to pp_plot
and an equivalent argument structure. In this case, we’re visualizing
the difference in reading achievement by race/ethnicity by season.
pp_plot(benchmarks, reading ~ ethnicity + season)
Essentially, the empirical cummulative distribution function (ECDF) for the reference group (by default, the highest performing group) is mapped against the ECDF for each corresponding group. The magnitude of the achievement gap is then displayed by the distance from the diagonal reference line, representing, essentially, the ECDF for the reference group.
By default, the area under the curve is shaded, which itself is an effect-size like measure, but this is also manipulable.
Finally, the ecdf_plot function essentially dresses up the base
plot.ecdf function, but also adds some nice referencing features
through additional, optional arguments. Below, I have included the
optional hor_ref = TRUE argument such that horizontal reference lines
appear, relative to the cuts provided.
ecdf_plot(benchmarks, math ~ season,
cuts = c(190, 200, 215))
These are the curves that go into the PP-Plot, but occasionally can be
useful on their own.
Compute effect sizes for all possible pairwise comparisons.
coh_d(benchmarks, math ~ season + frl)
#> # A tibble: 30 x 6
#> season_ref frl_ref season_foc frl_foc coh_d coh_se
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Fall FRL Fall Non-FRL 0.744 0.0706
#> 2 Fall FRL Spring FRL 1.32 0.0496
#> 3 Fall FRL Spring Non-FRL 2.01 0.0787
#> 4 Fall FRL Winter FRL 0.625 0.0472
#> 5 Fall FRL Winter Non-FRL 1.30 0.0733
#> 6 Fall Non-FRL Fall FRL -0.744 0.0706
#> 7 Fall Non-FRL Spring FRL 0.550 0.0694
#> 8 Fall Non-FRL Spring Non-FRL 1.14 0.0919
#> 9 Fall Non-FRL Winter FRL -0.127 0.0693
#> 10 Fall Non-FRL Winter Non-FRL 0.501 0.0872
#> # ... with 20 more rowsOr specify a reference group. In this case, I’ve used the formula-based interface, but a string vector specifiying the specific reference group could also be supplied.
coh_d(benchmarks,
math ~ season + frl,
ref_group = ~Fall + `Non-FRL`)
#> # A tibble: 5 x 6
#> season_ref frl_ref season_foc frl_foc coh_d coh_se
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Fall Non-FRL Fall FRL -0.744 0.0706
#> 2 Fall Non-FRL Spring FRL 0.550 0.0694
#> 3 Fall Non-FRL Spring Non-FRL 1.14 0.0919
#> 4 Fall Non-FRL Winter FRL -0.127 0.0693
#> 5 Fall Non-FRL Winter Non-FRL 0.501 0.0872Notice that the reference to Non-FRL is wrapped in back-ticks, which should be used anytime there are spaces or other non-standard characters.
Other effect sizes are estimated equivalently. For example, compute V (Ho, 2009) can be estimated with
v(benchmarks,
math ~ season + frl,
ref_group = ~Fall + `Non-FRL`)
#> # A tibble: 5 x 5
#> season_ref frl_ref season_foc frl_foc v
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 Fall Non-FRL Winter Non-FRL 0.507
#> 2 Fall Non-FRL Spring FRL 0.543
#> 3 Fall Non-FRL Winter FRL -0.112
#> 4 Fall Non-FRL Spring Non-FRL 1.15
#> 5 Fall Non-FRL Fall FRL -0.705or AUC with
auc(benchmarks,
math ~ season + frl,
ref_group = ~Fall + `Non-FRL`)
#> # A tibble: 5 x 5
#> season_ref frl_ref season_foc frl_foc auc
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 Fall Non-FRL Winter Non-FRL 0.640
#> 2 Fall Non-FRL Spring FRL 0.649
#> 3 Fall Non-FRL Winter FRL 0.469
#> 4 Fall Non-FRL Spring Non-FRL 0.792
#> 5 Fall Non-FRL Fall FRL 0.309