An R package improving upon the vcd package's distplot() function. It lets
you produce Poissonness, binomialness, and negative binomialness
plots with ggplot2 — or return just the data needed for the plot so you can
draw it with your preferred graphics library.
Ever need to make a Poissonness, binomialness, or negative binomialness plot but
want it to look prettier than the base R output of vcd::distplot()? Look no
further.
# install.packages("remotes")
remotes::install_github("zaynesember/plotness")plotness() takes a vector of counts, a 1-way frequency table, or a two-column
(freq, count) data frame / matrix, and a distribution type:
library(plotness)
# Poissonness plot
plotness(rpois(15, 10), type = "poisson")
# Binomialness plot
plotness(rbinom(15, 10, prob = 0.5), type = "binomial")
# Negative binomialness plot
plotness(rnbinom(15, 10, prob = 0.5), type = "nbinomial")Set plot = FALSE to get the underlying data frame instead of a ggplot, so you
can render it however you like:
plotness(rpois(15, 10), type = "poisson", plot = FALSE)
#> Counts Freq Metameter CI.center CI.width CI.lower CI.upper y_line
#> ...See ?plotness for the full list of arguments (confidence intervals, leveled
Poissonness plots via lambda, custom titles/labels, and more).
plotness_fit() runs the same statistics and returns a plotness_fit object
that exposes the fitted parameter estimates (rather than only printing them on
the plot). plotness() is a thin wrapper around it plus autoplot():
fit <- plotness_fit(rpois(200, 6), type = "poisson")
fit # prints slope/intercept and the parameter estimates
coef(fit) # c(intercept, slope)
fit$estimates$par_estim # exp(slope): the implied rate
ggplot2::autoplot(fit) # the same figure plotness() would drawThe statistical machinery is adapted from vcd::distplot() (Meyer, Zeileis &
Hornik). plotness reskins it with ggplot2 and adds a data-only return mode.
GPL (>= 2) © Zayne Sember. plotness adapts code from vcd::distplot(), which
is GPL-2, so the package is distributed under a compatible GPL license.