Ord.plot {vcd} | R Documentation |
Ord plots for diagnosing discrete distributions.
Ord.plot(obj, legend = TRUE, estimate = TRUE, tol = 0.1, type = NULL, ylim = NULL, xlab = "Number of occurrences", ylab = "Frequency ratio", main = "Ord plot", ...) Ord.estimate(x, type = NULL, tol = 0.1)
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. |
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. |
... |
further arguments passed to plot . |
x |
a vector giving intercept and slope for the (fitted) line in the Ord plot. |
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!
a vector giving the intercept and slope of the weighted OLS line.
Achim Zeileis
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.
## 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) par(mfrow = c(2,2)) Ord.plot(HorseKicks, main = "Death by horse kicks") Ord.plot(Federalist, main = "Instances of 'may' in Federalist papers") Ord.plot(Butterfly, main = "Butterfly species collected in Malaya") Ord.plot(WomenQueue, main = "Women in queues of length 10") par(mfrow = c(1,1))