granova (version 1.4)

granova.2w: Graphical display of data for two-way analysis of variance

Description

Produces a rotatable graphic (controlled by the mouse) to display all data points for any two way analysis of variance.

Usage

granova.2w(formula = NULL, data.A.B, fit = "linear", ident = FALSE, 
       offset = NULL, ...)

Value

Returns a list with four components:A.effectsReordered factor A effects (deviations of A-level means from grand mean)B.effectsReordered factor B effects (deviations of B-level means from grand mean)CellCounts.ReorderedCell sizes for all A-level, B-level combinations, with rows/columns reordered according to A.effects and B.effects.CellMeans.ReorderedMeans for all cells, i.e., A-level, B-level combinations, with rows/columns reordered according to A.effects and B.effectsanova.summarySummary aov results, based on input data

Details

Function depicts data points graphically in a window using the row by column set-up for a two-way ANOVA; the graphic is rotatable, controlled by the mouse. Data-based contrasts (cf. description for one-way ANOVA: granova.1w) are used to ensure a flat surface -- corresponding to an additive fit (if fit=`linear'; see below) -- for all cells. Points are displayed `vertically' (initially) with respect to the fitting surface. In particular, (dark blue) spheres are used to show data points for all groups. The mean for each cell is shown as a white sphere. The graphic is based on rgl and scatter3d; the graphic display can be zoomed in and out by scrolling, where the mouse is used to rotate the entire figure in a 3d representation where the row and column (factor A and B) effects have been used for spacing of the cells on the margins of the fitting surface. As noted, the first column of the input data frame must be response values (scores); the second and third columns should be integers that identify levels of the A and B factors respectively. Based on the row and column means, factor levels are first ordered (from small to large) separately for the row and column means; levels are assumed not to be ordered at the outset. Function scatter3d is used from Rcmdr (thanks, John Fox). The fit is defaulted to linear whence interactions are depicted as departures of the cell means from a flat surface. It is possible to replace linear with any of quadratic, smooth, or additive; see help for scatter3d for details. The table of counts for the cell means is printed (with respect the the reordered rows and columns); similarly, the table of cell means is printed (also, based on reordered rows and columns). Finally, numerical summary results derived from function aov are also printed. Although the function accommodates the case where cell counts are not all the same, or when the data are unbalanced with respect to the A & B factors, the surface can be misleading, especially in highly unbalanced data. Machine memory for this function has caused problems with some larger data sets. The authors would appreciate reports of problems or successes with larger data sets.

References

Fundamentals of Exploratory Analysis of Variance, Hoaglin D., Mosteller F. and Tukey J. eds., Wiley, 1991.

See Also

granova.1w, granova.contr, granova.ds

Examples

#Random data
resp <- rnorm(80,0,.25) + rep(c(0,.2,.4,.6), ea = 20)
f1 <- rep(1:4, ea = 20)
f2 <- rep(rep(1:5, ea = 4), 4)
rdat1 <- cbind(resp, f1, f2)
granova.2w(data.A.B = rdat1)
#
rdat2 <- cbind(rnorm(64,10,2), sample(1:4, 64, repl = TRUE), 
   sample(1:3, 64, repl = TRUE))
granova.2w(data.A.B = rdat2)
#
granova.2w(formula = breaks ~ wool * tension, data.A.B = warpbreaks)