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 = 0.8), lwd=2, gp_conf_int = gpar(lty = 2),
name = "distplot", newpage = TRUE,
pop =TRUE, return_grob = FALSE, …)
```

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.

gp_conf_int

a `"gpar"`

object controlling the grid graphical
parameters of the confidence intervals.

lwd

line width for the fitted line

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`

.

Returns invisibly a data frame containing the counts (`Counts`

),
frequencies (`Freq`

) and other details of the computations used
to construct the plot.

`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.

In these plots, the open points show the observed count metameters;
the filled points show the confidence interval centers, and the
dashed lines show the `conf_level`

confidence intervals for
each point.

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.

# NOT RUN { ## 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) # }