Graphical Analysis of Variance
Description
This small collection of functions provides what we call
elemental graphics for display of anova results. The term
elemental derives from the fact that each function is aimed at
construction of graphical displays that afford direct
visualizations of data with respect to the fundamental
questions that drive the particular anova methods. The two main
functions are granova.1w (a graphic for one way anova) and
granova.2w (a corresponding graphic for two way anova). These
functions were written to display data for any number of
groups, regardless of their sizes (however, very large data
sets or numbers of groups can be problematic). For these two
functions a specialized approach is used to construct
data-based contrast vectors for which anova data are displayed.
The result is that the graphics use straight lines, and when
appropriate flat surfaces, to facilitate clear interpretations
while being faithful to the standard effect tests in anova. The
graphic results are complementary to standard summary tables
for these two basic kinds of analysis of variance; numerical
summary results of analyses are also provided as side effects.
Two additional functions are granova.ds (for comparing two
dependent samples), and granova.contr (which provides graphic
displays for a priori contrasts). All functions provide
relevant numerical results to supplement the graphic displays
of anova data. The graphics based on these functions should be
especially helpful for learning how the methods have been
applied to answer the question(s) posed. This means they can be
particularly helpful for students and non-statistician
analysts. But these methods should be quite generally helpful
for work-a-day applications of all kinds, as they can help to
identify outliers, clusters or patterns, as well as highlight
the role of non-linear transformations of data. In the case of
granova.1w and granova.ds especially, several arguments are
provided to facilitate flexibility in the construction of
graphics that accommodate diverse features of data, according
to their corresponding display requirements. See the help files
for individual functions.