mosaic (version 1.1.0)

qdist: Illustrated quantile calculations from distributions

Description

Illustrated quantile calculations from distributions

Usage

qdist(dist = "norm", p, plot = TRUE, verbose = FALSE, invisible = FALSE,
  resolution = 500L, digits = 3L, xlim, ylim, return = c("values",
  "plot"), ..., refinements = list())
xqgamma(...)
xqt(...)
xqchisq(...)
xqf(...)
xqbinom(...)
xqpois(...)
xqgeom(...)
xqnbinom(...)
xqbeta(...)

Arguments

dist

a character discription of a distribution, for example "norm", "t", or "chisq"

p

a vector of probabilities

plot

a logical indicating whether a plot should be created

verbose

a logical

invisible

a logical

resolution

number of points used for detecting discreteness and generating plots. The defaut value of 5000 should work well except for discrete distributions that have many distinct values, especially if these values are not evenly spaced.

digits

the number of digits desired

xlim

x limits. By default, these are chosen to show the central 99.8% of the distribution.

ylim

y limits

return

If "plot", return a plot. If "values", return a vector of numerical values.

...

additional arguments, including parameters of the distribution and additional options for the plot

refinements

A list of refinements to the plot. See ggformula::gf_refine().

Value

a vector of quantiles; a plot is printed as a side effect

Details

The most general function is qdist which can work with any distribution for which a q-function exists. As a convenience, wrappers are provided for several common distributions.

Examples

# NOT RUN {
qdist("norm", seq(.2, .8, by = 0.10))
xqnorm(seq(.2, .8, by = 0.10), mean = 100, sd = 10)
qdist("unif", .5)
xqgamma(.5, shape = 3, scale = 4)
xqgamma(.5, shape = 3, scale = 4, dlwd = 1)
xqbeta(.5, shape1 = .9, shape2 = 1.4, dlwd = 1)
xqchisq(c(.25,.5,.75), df = 3)
xqbinom(c(0.25, 0.85), size = 1000, prob = 0.40)
# displayed as if continuous
xqbinom(c(0.25, 0.85), size = 5000, prob = 0.40)
xpbinom( c(480, 500, 510), size = 1000, prob = 0.48)
xpbinom( c(40, 60), size = 100, prob = 0.5)
xqpois( c(0.25, 0.5, 0.75), lambda = 6, lwd = 3, vlwd = 2)
# }