rootogram {vcd}R Documentation

Rootograms

Description

Rootograms of observed and fitted values.

Usage

## Default S3 method:
rootogram(x, fitted, names = NULL, scale = c("sqrt", "raw"),
  type = c("hanging", "standing", "deviation"),
  rect_gp = gpar(fill = "lightgray"), lines_gp = gpar(col = "red"),
  points_gp = gpar(col = "red"), pch = 19,
  xlab = NULL, ylab = NULL, ylim = NULL,
  name = "rootogram", newpage = TRUE, pop = TRUE, ...)

Arguments

x

either a vector or a 1-way table of frequencies.

fitted

a vector of fitted frequencies.

names

a vector of names passed to grid_barplot, if set to NULL the names of x are used.

scale

a character string indicating whether the values should be plotted on the raw or square root scale.

type

a character string indicating if the bars for the observed frequencies should be hanging or standing or indicate the deviation between observed and fitted frequencies.

rect_gp

a "gpar" object controlling the grid graphical parameters of the rectangles.

lines_gp

a "gpar" object controlling the grid graphical parameters of the lines.

points_gp

a "gpar" object controlling the grid graphical parameters of the points.

pch

plotting character for the points.

xlab

a label for the x axis.

ylab

a label for the y axis.

ylim

limits for the y axis.

name

name of the plotting viewport.

newpage

logical. Should grid.newpage be called before plotting?

pop

logical. Should the viewport created be popped?

...

further arguments passed to grid_barplot.

Details

The observed frequencies are displayed as bars and the fitted frequencies as a line. By default a sqrt scale is used to make the smaller frequencies more visible.

Author(s)

Achim Zeileis Achim.Zeileis@R-project.org

References

J. W. Tukey (1977), Exploratory Data Analysis. Addison Wesley, Reading, MA.

M. Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.

See Also

grid_barplot

Examples

## Simulated data examples:
dummy <- rnbinom(200, size = 1.5, prob = 0.8)
observed <- table(dummy)
fitted1 <- dnbinom(as.numeric(names(observed)),
                   size = 1.5, prob = 0.8) * sum(observed)
fitted2 <- dnbinom(as.numeric(names(observed)),
                   size = 2, prob = 0.6) * sum(observed)
rootogram(observed, fitted1)
rootogram(observed, fitted2)

## Real data examples:
data("HorseKicks")
HK.fit <- goodfit(HorseKicks)
summary(HK.fit)
plot(HK.fit)
## or equivalently
rootogram(HK.fit)

data("Federalist")
F.fit <- goodfit(Federalist, type = "nbinomial")
summary(F.fit)
plot(F.fit)

[Package vcd version 1.2-12 Index]