binned_es

The data frame used for estimation - ideally structured in a tidy 
format.

data

A formula of the type <code>out ~ group</code> where <code>out</code> is
the outcome variable and <code>group</code> is the grouping variable. Note this
variable can include any arbitrary number of groups. Additional variables 
can be included with <code>+</code> to produce separate estimates by the secondary 
or tertiary variables of interest (e.g., <code>out ~ group + characteristic1 
+ characteristic2</code>).

formula

Optional. A character vector or forumla listing the 
reference group levels for each variable on the right hand side of the 
formula, supplied in the same order as the formula. Note that if using the
formula version, levels that are numbers, or include hyphens, spaces, etc., 
should be wrapped in back ticks (e.g., 
<code>ref_group = ~ Active + `Non-FRL`</code>, or <code>ref_group = ~`8`</code>). When 
in doubt, it is safest to use the back ticks, as they will not interfere 
with anything if they are not needed. See examples below for more details.

ref_group

The number of quantile bins to split the data by and 
calculate effect sizes. Defaults to 3 bins (lower, middle, upper).

qtile_groups

The effect size to calculate. Currently the only options are 
"d" or "g".

Logical. Should the column names be relabeled according to
the reference and focal groups. Defaults to <code>TRUE</code>.

rename

A variety of methods are provided to estimate and visualize
distributional differences in terms of effect sizes. Particular emphasis
is upon evaluating differences between two or more distributions across
the entire scale, rather than at a single point (e.g., differences in
means). For example, Probability-Probability (PP) plots display the
difference between two or more distributions, matched by their empirical
CDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowing
for examinations of where on the scale distributional differences are
largest or smallest. The area under the PP curve (AUC) is an effect-size
metric, corresponding to the probability that a randomly selected
observation from the x-axis distribution will have a higher value
than a randomly selected observation from the y-axis distribution.
Binned effect size plots are also available, in which the distributions
are split into bins (set by the user) and separate effect sizes (Cohen's
d) are produced for each bin - again providing a means to evaluate the
consistency (or lack thereof) of the difference between two or more
distributions at different points on the scale. Evaluation of empirical
CDFs is also provided, with built-in arguments for providing annotations
to help evaluate distributional differences at specific points (e.g.,
semi-transparent shading). All function take a consistent argument
structure. Calculation of specific effect sizes is also possible. The
following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g,
(c) percentage above a cut, (d) transformed (normalized) percentage above
a cut, (e) area under the PP curve, and (f) the V statistic (see Ho,
2009; <doi:10.3102/1076998609332755>), which essentially transforms the
area under the curve to standard deviation units. By default, effect sizes
are calculated for all possible pairwise comparisons, but a reference
group (distribution) can be specified.

binned_es: Calculate binned effect sizes

Description

Usage

Arguments

Value