Ord Plots — Ord_plot • vcd

Ord plots for diagnosing discrete distributions.

Ord_plot(obj, legend = TRUE, estimate = TRUE, tol = 0.1, type = NULL,
  xlim = NULL, ylim = NULL, xlab = "Number of occurrences",
  ylab = "Frequency ratio", main = "Ord plot", gp = gpar(cex = 0.5),
  lwd = c(2,2), lty=c(2,1), col=c("black", "red"),
  name = "Ord_plot", newpage = TRUE, pop = TRUE,
  return_grob = FALSE, ...)
Ord_estimate(x, type = NULL, tol = 0.1)

Arguments

obj: either a vector of counts, a 1-way table of frequencies of counts or a data frame or matrix with frequencies in the first column and the corresponding counts in the second column.
legend: logical. Should a legend be plotted?.
estimate: logical. Should the distribution and its parameters be estimated from the data? See details.
tol: tolerance for estimating the distribution. See details.
type: a character string indicating the distribution, must be one of "poisson", "binomial", "nbinomial" or "log-series" or NULL. In the latter case the distribution is estimated from the data. See details.
xlim: limits for the x axis.
ylim: limits for the y axis.
xlab: a label for the x axis.
ylab: a label for the y axis.
main: a title for the plot.
gp: a "gpar" object controlling the grid graphical parameters of the points.
lwd, lty: vectors of length 2, giving the line width and line type used for drawing the OLS line and the WLS lines.
col: vector of length 2 giving the colors used for drawing the OLS and WLS lines.
name: name of the plotting viewport.
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.points.
x: a vector giving intercept and slope for the (fitted) line in the Ord plot.

Details

The Ord plot plots the number of occurrences against a certain frequency ratio (see Friendly (2000) for details) and should give a straight line if the data comes from a poisson, binomial, negative binomial or log-series distribution. The intercept and slope of this straight line conveys information about the underlying distribution.

Ord_plot fits a usual OLS line (black) and a weighted OLS line (red). From the coefficients of the latter the distribution is estimated by Ord_estimate as described in Table 2.10 in Friendly (2000). To judge whether a coefficient is positive or negative a tolerance given by tol is used. If none of the distributions fits well, no parameters are estimated. Be careful with the conclusions from Ord_estimate as it implements just some simple heuristics!

Value

A vector giving the intercept and slope of the weighted OLS line.

Author

Achim Zeileis Achim.Zeileis@R-project.org

References

J. K. Ord (1967), Graphical methods for a class of discrete distributions, Journal of the Royal Statistical Society, A 130, 232–238.

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

Examples

## Simulated data examples:
dummy <- rnbinom(1000, size = 1.5, prob = 0.8)
Ord_plot(dummy)


## Real data examples:
data("HorseKicks")
data("Federalist")
data("Butterfly")
data("WomenQueue")

if (FALSE) { # \dontrun{
grid.newpage()
pushViewport(viewport(layout = grid.layout(2, 2)))

pushViewport(viewport(layout.pos.col=1, layout.pos.row=1))
Ord_plot(HorseKicks, main = "Death by horse kicks", newpage = FALSE)
popViewport()

pushViewport(viewport(layout.pos.col=1, layout.pos.row=2))
Ord_plot(Federalist, main = "Instances of 'may' in Federalist papers", newpage = FALSE)
popViewport()

pushViewport(viewport(layout.pos.col=2, layout.pos.row=1))
Ord_plot(Butterfly, main = "Butterfly species collected in Malaya", newpage = FALSE)
popViewport()

pushViewport(viewport(layout.pos.col=2, layout.pos.row=2))
Ord_plot(WomenQueue, main = "Women in queues of length 10", newpage = FALSE)
popViewport(2)
} # }

## same
mplot(
  Ord_plot(HorseKicks, return_grob = TRUE, main = "Death by horse kicks"),
  Ord_plot(Federalist, return_grob = TRUE, main = "Instances of 'may' in Federalist papers"),
  Ord_plot(Butterfly, return_grob = TRUE, main = "Butterfly species collected in Malaya"),
  Ord_plot(WomenQueue, return_grob = TRUE, main = "Women in queues of length 10")
)