pdf.PoissonBinomial: Evaluate the probability mass function of a PoissonBinomial...

In distributions3: Probability Distributions as S3 Objects

Evaluate the probability mass function of a PoissonBinomial distribution

Description

Evaluate the probability mass function of a PoissonBinomial distribution

Usage

## S3 method for class 'PoissonBinomial'
pdf(d, x, drop = TRUE, elementwise = NULL, log = FALSE, verbose = TRUE, ...)

## S3 method for class 'PoissonBinomial'
log_pdf(d, x, drop = TRUE, elementwise = NULL, ...)

Arguments

d	A PoissonBinomial object created by a call to PoissonBinomial().
x	A vector of elements whose probabilities you would like to determine given the distribution d.
drop	logical. Should the result be simplified to a vector if possible?
elementwise	logical. Should each distribution in d be evaluated at all elements of x (elementwise = FALSE, yielding a matrix)? Or, if d and x have the same length, should the evaluation be done element by element (elementwise = TRUE, yielding a vector)? The default of NULL means that elementwise = TRUE is used if the lengths match and otherwise elementwise = FALSE is used.
log, ...	Arguments to be passed to dpbinom or pnorm, respectively.
verbose	logical. Should a warning be issued if the normal approximation is applied because the PoissonBinomial package is not installed?

Value

In case of a single distribution object, either a numeric vector of length probs (if drop = TRUE, default) or a matrix with length(x) columns (if drop = FALSE). In case of a vectorized distribution object, a matrix with length(x) columns containing all possible combinations.

Examples

set.seed(27)

X <- PoissonBinomial(0.5, 0.3, 0.8)
X

mean(X)
variance(X)
skewness(X)
kurtosis(X)

random(X, 10)

pdf(X, 2)
log_pdf(X, 2)

cdf(X, 2)
quantile(X, 0.8)

cdf(X, quantile(X, 0.8))
quantile(X, cdf(X, 2))

## equivalent definitions of four Poisson binomial distributions
## each summing up three Bernoulli probabilities
p <- cbind(
  p1 = c(0.1, 0.2, 0.1, 0.2),
  p2 = c(0.5, 0.5, 0.5, 0.5),
  p3 = c(0.8, 0.7, 0.9, 0.8))
PoissonBinomial(p)
PoissonBinomial(p[, 1], p[, 2], p[, 3])
PoissonBinomial(p[, 1:2], p[, 3])

distributions3 documentation built on Sept. 30, 2024, 9:37 a.m.