rootogram: Rootograms

Description

Rootograms of observed and fitted values.

Usage

## S3 method for class 'default':
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

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.

References

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

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

Examples

Run this code

## 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)