New version of description for package granovaGG

[rmpruzek]

This collection of functions in granovaGG 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. This package represents a modification of the original granova package; the key change is to use ggplot2, Hadley Wickham's package based on Grammar of Graphics concepts (due to Wilkenson) for graphic constructions. The main function is granovagg.1w (a graphic for one way anova); two other functions (granovagg.ds and granovagg.contr) construct graphics for dependent sample analyses and contrast-based analyses respectively. (The function granova.2w, which entails dynamic displays of data, is not currently part of granovaGG, although it remains viable in the original package) The granovaGG functions display data for any number of groups, regardless of their sizes (however, very large data sets or numbers of groups can be problematic). For granovagg.1w a specialized approach is used to construct data-based contrast vectors for which anova data are displayed. The result is that the graphics use a straight line to facilitate clear interpretations while being faithful to the standard effect test in anova. The graphic results are complementary to standard summary tables; indeed, numerical summary statistics are provided as side effects of the graphic constructions. granovagg.ds and granova.contr provide graphic displays and numerical outputs for a dependent sample and contrast-based analyses. All graphics based on these functions can be helpful for learning how the respective methods work to answer the central anova question(s) that drive the analyses. This means they can be particularly helpful for students and non-statistician analysts. But all methods can be of assistance for work-a-day applications of many 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 granovagg.1w and granovagg.ds 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.

[WilDoane]

Closed by 1a0a0b5f5b3667de354d