| distplot {vcd} | R Documentation |
Diagnostic distribution plots: poissonness, binomialness and negative binomialness plots.
distplot(x, type = c("poisson", "binomial", "nbinomial"),
size = NULL, lambda = NULL, legend = TRUE, xlim = NULL, ylim = NULL,
conf_int = TRUE, conf_level = 0.95, main = NULL,
xlab = "Number of occurrences", ylab = "Distribution metameter",
gp = gpar(cex = 0.5), name = "distplot", newpage = TRUE, pop = TRUE, ...)
x |
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. |
type |
a character string indicating the distribution. |
size |
the size argument for the binomial and negative binomial
distribution.
If set to NULL and type is "binomial", then
size is taken to be the maximum count.
If set to NULL and type is "nbinomial", then
size is estimated from the data. |
lambda |
parameter of the poisson distribution.
If type is "poisson" and lambda is specified a leveled
poissonness plot is produced. |
legend |
logical. Should a legend be plotted? |
xlim |
limits for the x axis. |
ylim |
limits for the y axis. |
conf_int |
logical. Should confidence intervals be plotted? |
conf_level |
confidence level for confidence intervals. |
main |
a title for the plot. |
xlab |
a label for the x axis. |
ylab |
a label for the y axis. |
gp |
a "gpar" object controlling the grid graphical
parameters of the points. |
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.points. |
distplot plots the number of occurrences (counts) against the
distribution metameter of the specified distribution. If the
distribution fits the data, the plot should show a straight line.
See Friendly (2000) for details.
Achim Zeileis Achim.Zeileis@R-project.org
D. C. Hoaglin (1980), A poissonness plot, The American Statistican, 34, 146–149.
D. C. Hoaglin & J. W. Tukey (1985), Checking the shape of discrete distributions. In D. C. Hoaglin, F. Mosteller, J. W. Tukey (eds.), Exploring Data Tables, Trends and Shapes, chapter 9. John Wiley & Sons, New York.
M. Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.
## Simulated data examples:
dummy <- rnbinom(1000, size = 1.5, prob = 0.8)
distplot(dummy, type = "nbinomial")
## Real data examples:
data("HorseKicks")
data("Federalist")
data("Saxony")
distplot(HorseKicks, type = "poisson")
distplot(HorseKicks, type = "poisson", lambda = 0.61)
distplot(Federalist, type = "poisson")
distplot(Federalist, type = "nbinomial", size = 1)
distplot(Federalist, type = "nbinomial")
distplot(Saxony, type = "binomial", size = 12)