briandk / granovaGG

Bob Pruzek and Jim Helmreich's implementation of Elemental Graphics for Analysis of Variance
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man/granovaGG.Rd needs to be updated #85

Closed WilDoane closed 13 years ago

WilDoane commented 13 years ago
\docType{package}
\name{granovaGG}
\alias{"granovaGG-package"}
\alias{granovaGG}
\title{Elemental Graphics for Analysis of Variance Using ggplot2}
\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.
}
\details{
  \tabular{ll}{ Package: \tab granovaGG\cr Version: \tab
  1.0\cr License: \tab GPL (>= 2)\cr }
}
\author{
  Brian A. Danielak \email{brian@briandk.com}

  Robert M. Pruzek \email{RMPruzek@yahoo.com}

  William E. J. Doane \email{wil@drdoane.com}

  James E. Helmreich \email{James.Helmreich@Marist.edu}

  Jason Bryer \email{jason@bryer.org}
}
\seealso{
  \code{\link{granovagg.1w}} \code{\link{granovagg.ds}}
  \code{\link{granovagg.contr}}
}
\keyword{hplot}