create_legend

Number of lines to produce on the legend.

Labels for the lines in the legend.

leg_labels

Left margin argument. Defaults to 0. Larger numbers push the
legend more to the right.

left_mar

The height of the legend. Counter-intuitively, larger numbers
result in a smaller legend (more squished to the bottom).

height

Primary colors (of the lines, rather than the cut scores)

main_cols

Cut scores (see <a rd-options="" href="/link/pp_plot?package=esvis&version=0.2.0" data-mini-rdoc="esvis::pp_plot">pp_plot</a>).

The color of the lines/points for the cut scores.

cut_cols

Should the lines on the legend be displayed when there
is only one value? Defaults to <code>FALSE</code>, and is relevant when values to
<code>cut</code> are provided.

Additional arguments passed to <a rd-options="graphics" href="/link/lines?package=esvis&version=0.2.0&to=graphics" data-mini-rdoc="graphics::lines">lines</a>.

This is an alternative legend for plots which uses the actual 
plotting environment to create the legend, rather than overlaying it. I 
prefer this legend because it scales better than the base legend. It is
currently only implemented to support lines.

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

create_legend function

Cut scores (see <a rd-options='' href='pp_plot'>pp_plot</a>).

Additional arguments passed to <a rd-options='graphics' href='lines'>lines</a>.

create_legend: Create a legend for a plot

Description

Usage

Arguments