pp_calcs

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 the
grouping variable must only include only two groups.

formula

The data frame that the data in the formula come from.

data

Optional character vector (of length 1) naming the
reference group to be plotted on the x-axis. Defaults to the highest
scoring group.

ref_group

What color scheme should the lines follow? Defaults to 
mimic the ggplot2 color scheme. Other options come from the 
<a href="https://CRAN.R-project.org/package=viridisLite">viridisLite</a>
package, and must be installed first. These are the same options available
in the package: "viridis", "magma", "inferno", and "plasma". These color 
schemes work well for color blindness and print well in black and white.
Alternatively, colors can be supplied manually through a call to <code>col</code>
(through <code>...</code>).

scheme

Produce calculations necessary for <a rd-options="" href="/link/pp_plot?package=esvis&version=0.2.0" data-mini-rdoc="esvis::pp_plot">pp_plot</a>.

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.

Daniel Anderson

esvis

Visualization and Estimation of Effect Sizes

pp_calcs function

What color scheme should the lines follow? Defaults to 
mimic the ggplot2 color scheme. Other options come from the 
<a href='https://CRAN.R-project.org/package=viridisLite'>viridisLite</a>
package, and must be installed first. These are the same options available
in the package: "viridis", "magma", "inferno", and "plasma". These color 
schemes work well for color blindness and print well in black and white.
Alternatively, colors can be supplied manually through a call to <code>col</code>
(through <code>...</code>).

Produce calculations necessary for <a rd-options='' href='pp_plot'>pp_plot</a>.

pp_calcs: Produce calculations necessary for pp_plot.

Description

Usage

Arguments

Value