vcd: distplot – R documentation

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distplot

Diagnostic Distribution Plots

Description

Diagnostic distribution plots: poissonness, binomialness and negative binomialness plots.

Usage

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, ...)

Arguments

`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`.

Details

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.

Value

Returns invisibly a data frame containing the counts (Counts), frequencies (Freq) and other details of the computations used to construct the plot.

Author(s)

Achim Zeileis Achim.Zeileis@R-project.org

References

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.

Examples

## 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)

vcd

Visualizing Categorical Data

v1.4-10

GPL-2

Authors

David Meyer [aut, cre], Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Kurt Hornik [aut], Florian Gerber [ctb], Michael Friendly [ctb]

Initial release