Survial times of animals in a 3 x 4 factorial experiment involving poisons (3 levels) and various treatments (four levels), as described in Chapter 8 of Box, Hunter and Hunter.

datasets

poison

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.

ORPHANED

granova

Graphical Analysis of Variance

poison function

docType

Box, G. E. P. and D. R. Cox, An Analysis of Transformations (with discussion), Journal of the Royal Statistical Society, Series B, Vol. 26, No. 2, pp. 211 - 254.

poison: Poison data from Biological Experiment

Description

Usage

Arguments

docType

source

References