rtrunc: The Truncated Exponential Family

In TruncExpFam: Truncated Exponential Family

The Truncated Exponential Family

Description

Random generation for the truncated exponential family distributions. Please refer to the "Details" and "Examples" section for more information on how to use this function.

Usage

rtruncbeta(n, shape1, shape2, a = 0, b = 1, faster = FALSE)

rtruncbinom(n, size, prob, a = 0, b = size, faster = FALSE)

rtruncchisq(n, df, a = 0, b = Inf, faster = FALSE)

rtrunccontbern(n, lambda, a = 0, b = 1, faster = FALSE)

rtruncexp(n, rate = 1, a = 0, b = Inf, faster = FALSE)

rtruncgamma(n, shape, rate = 1, scale = 1/rate, a = 0, b = Inf, faster = FALSE)

rtruncinvgamma(
  n,
  shape,
  rate = 1,
  scale = 1/rate,
  a = 0,
  b = Inf,
  faster = FALSE
)

rtruncinvgauss(n, m, s, a = 0, b = Inf, faster = FALSE)

rtrunclnorm(n, meanlog, sdlog, a = 0, b = Inf, faster = FALSE)

rtruncnbinom(n, size, prob, mu, a = 0, b = Inf, faster = FALSE)

rtruncnorm(n, mean, sd, a = -Inf, b = Inf, faster = FALSE)

rtruncpois(n, lambda, a = 0, b = Inf, faster = FALSE)

rtrunc(n, family = "gaussian", faster = FALSE, ...)

rtrunc_direct(n, family = "gaussian", parms, a, b, ...)

Arguments

n	sample size
shape1	positive shape parameter alpha
shape2	positive shape parameter beta
a	point of left truncation. For discrete distributions, a will be included in the support of the truncated distribution.
b	point of right truncation
faster	if TRUE, samples directly from the truncated distribution (more info in details)
size	target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer.
prob	probability of success on each trial
df	degrees of freedom for "parent" distribution
lambda	mean and var of "parent" distribution
rate	inverse gamma rate parameter
shape	inverse gamma shape parameter
scale	inverse gamma scale parameter
m	vector of means
s	vector of dispersion parameters
meanlog	mean of untruncated distribution
sdlog	standard deviation of untruncated distribution
mu	alternative parametrization via mean
mean	mean of parent distribution
sd	standard deviation is parent distribution
family	distribution family to use
...	individual arguments to each distribution
parms	list of parameters passed to rtrunc (through the ... element)

Details

One way to use this function is by calling the rtrunc generic with the family parameter of your choice. You can also specifically call one of the methods (e.g. rtruncpois(10, lambda=3) instead of ⁠rtrunc(10, family="poisson", lambda=3)). The latter is more flexible (i.e., easily programmable) and more robust (i.e., it contains better error handling and validation procedures), while the former better conforms with the nomenclature from other distribution-related functions in the ⁠stats' package.

Setting faster=TRUE uses a new algorithm that samples directly from the truncated distribution, as opposed to the old algorithm that samples from the untruncated distribution and then truncates the result. The advantage of the new algorithm is that it is way faster than the old one, particularly for highly-truncated distributions. On the other hand, the sample for untruncated distributions called through rtrunc() will no longer match their stats-package counterparts for the same seed.

Value

A sample of size n drawn from a truncated distribution

vector of one of the ⁠rtrunc_*⁠ classes containing the sample elements, as well as some attributes related to the chosen distribution.

Note

The current sample-generating algorithm may be slow if the distribution is largely represented by low-probability values. This will be fixed soon. Please follow https://github.com/ocbe-uio/TruncExpFam/issues/72 for details.

Author(s)

René Holst, Waldir Leôncio

Examples

# Truncated binomial distribution
sample.binom <- rtrunc(
  100, family = "binomial", prob = 0.6, size = 20, a = 4, b = 10
)
sample.binom
plot(
  table(sample.binom), ylab = "Frequency", main = "Freq. of sampled values"
)

# Truncated Log-Normal distribution
sample.lognorm <- rtrunc(
  n = 100, family = "lognormal", meanlog = 2.5, sdlog = 0.5, a = 7
)
summary(sample.lognorm)

hist(
  sample.lognorm,
  nclass = 35, xlim = c(0, 60), freq = FALSE,
  ylim = c(0, 0.15)
)

# Normal distribution
sample.norm <- rtrunc(n = 100, mean = 2, sd = 1.5, a = -1)
head(sample.norm)
hist(sample.norm, nclass = 25)

# Gamma distribution
sample.gamma <- rtrunc(n = 100, family = "gamma", shape = 6, rate = 2, a = 2)
hist(sample.gamma, nclass = 15)

# Poisson distribution
sample.pois <- rtrunc(n = 10, family = "poisson", lambda = 10, a = 4)
sample.pois
plot(table(sample.pois))

TruncExpFam documentation built on April 11, 2025, 6:11 p.m.