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"),
  shade = FALSE, legend = TRUE,
  legend_args = list(x = 0, y = 0.2, height = 0.6), df = NULL,
  rect_gp = NULL, rect_gp_args = list(),
  lines_gp = gpar(col = "red", lwd = 2),
  points_gp = gpar(col = "red"), pch = 19,
  xlab = NULL, ylab = NULL, ylim = NULL,
  main = NULL, sub = NULL,
  margins = unit(0, "lines"),
  title_margins = NULL, legend_width = NULL,
  main_gp = gpar(fontsize = 20),
  sub_gp = gpar(fontsize = 15),
  name = "rootogram", prefix = "",
  keep_aspect_ratio = FALSE,
  newpage = TRUE, pop = TRUE,
  return_grob = FALSE, ...)

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.

shade

logical specifying whether rect_gp should be set to colors corresponding to the pearson residuals, i.e., if a residual-based shading should be applied to the bars.

legend

either a legend-generating function, or a legend function (see details and legends), or a logical. If legend is NULL or TRUE and gp is a funct

legend_args

list of arguments for the legend-generating function, if specified.

degrees of freedom passed to the shading functions used for inference.

rect_gp

a "gpar" object controlling the grid graphical parameters of the rectangles, shading function or a corresponding generating function (see shadings). If unspecified and no shading i

rect_gp_args

list of arguments for the shading-generating function, if specified for rect_gp.

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.

main

either a logical, or a character string used for plotting the main title. If main is a logical and TRUE, the name of the object supplied as x is used.

sub

a character string used for plotting the subtitle. If sub is a logical and TRUE and main is unspecified, the name of the object supplied as x is used.

margins

either an object of class "unit" of length 4, or a numeric vector of length 4. The elements are recycled as needed. The four components specify the top, right, bottom, and left margin of the plot, respectively. When a numeric

title_margins

either an object of class "unit" of length 2, or a numeric vector of length 2. The elements are recycled as needed. The two components specify the top and bottom title margin of the plot, respectively. The default for eac

legend_width

An object of class "unit" of length 1 specifying the width of the legend (if any). Default: 5 lines.

main_gp, sub_gp

object of class "gpar" containing the graphical parameters used for the main (sub) title, if specified.

name

name of the plotting viewport.

keep_aspect_ratio

logical indicating whether the aspect ratio should be fixed or not.

prefix

optional character string used as a prefix for the generated viewport and grob names.

newpage

logical. Should grid.newpage be called before plotting?

pop

logical. Should the viewport created be popped?

return_grob

logical. Should a snapshot of the display be returned as a grid grob?

...

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)

## (Pearson) residual-based shading
data("Federalist")
Fed_fit0 <- goodfit(Federalist, type = "poisson")
plot(Fed_fit0, shade = TRUE)