varianceGraph: Variance Graphs
In cg: Compare Groups, Analytically and Graphically

Description Usage Arguments Details Value Note Author(s) See Also Examples

Create an equal variance assessment graph of the residuals of a fitted object from the cg package

1	varianceGraph(fit, trend = NULL, cgtheme = TRUE, device = "single", ...)

`fit`	A fit object, typically created by the `fit` generic function.
`trend`	Add a trend line to help assess the trend of the residuals. See specific method written for the `fit` argument.
`cgtheme`	When set to the default `TRUE`, ensures a trellis device is active with limited color scheme. Namely `background`, `strip.shingle`, and `strip.background` are each set to `"white"`.
`device`	Can be one of three values: `"single"` The default, which will put all graphs on the same device page. `"multiple"` Relevant only when multiple fitted models are present in the `fit` object and requested to be plotted. In those cases, a new graphics device is generated to hold each additional plot beyond the first. `"ask"` Relevant only when multiple fitted models are present in the `fit` object and requested to be plotted. In these cases, each plot is portrayed as a single-paneled graph, with the `ask=TRUE` argument specified in `par` so that the user input confirmation is needed before the graphs are drawn.
`...`	Additional arguments, depending on the specific method written for the object. See the method-specific documentation for additional details.

The graphs plot the square root of the absolute value of the residuals against the fitted value. The notion of using the squared root of the absolute residuals is attributed to John Tukey.

varianceGraph returns an invisible NULL. The main purpose is the side effect of graphing to the current device.

Contact cg@billpikounis.net for bug reports, questions, concerns, and comments.

Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]

varianceGraph.cgOneFactorFit

data(canine)
canine.data <- prepareCGOneFactorData(canine, format="groupcolumns",
                                      analysisname="Canine",
                                      endptname="Prostate Volume",
                                      endptunits=expression(plain(cm)^3),
                                      digits=1, logscale=TRUE, refgrp="CC")
canine.fit <- fit(canine.data)

varianceGraph(canine.fit)

varianceGraph(canine.fit, model="olsonly")