distributions: Probability Density Functions for Probabilistic Uncertainty...

In heemod: Markov Models for Health Economic Evaluations

Probability Density Functions for Probabilistic Uncertainty Analysis

Description

Define a distribution for PSA parameters.

Usage

normal(mean, sd)

lognormal(mean, sd, meanlog, sdlog)

gamma(mean, sd)

binomial(prob, size)

multinomial(...)

logitnormal(mu, sigma)

beta(shape1, shape2)

triangle(lower, upper, peak = (lower + upper)/2)

poisson(mean)

define_distribution(x)

beta(shape1, shape2)

triangle(lower, upper, peak = (lower + upper)/2)

use_distribution(distribution, smooth = TRUE)

Arguments

mean	Distribution mean.
sd	Distribution standard deviation.
meanlog	Mean on the log scale.
sdlog	SD on the log scale.
prob	Proportion.
size	Size of sample used to estimate proportion.
...	Dirichlet distribution parameters.
mu	Mean on the logit scale.
sigma	SD on the logit scale.
shape1	for beta distribution
shape2	for beta distribution
lower	lower bound of triangular distribution.
upper	upper bound of triangular distribution.
peak	peak of triangular distribution.
x	A distribution function, see details.
distribution	A numeric vector of observations defining a distribution, usually the output from an MCMC fit.
smooth	Use gaussian kernel smoothing?

Details

These functions are not exported, but only used in define_psa(). To specify a user-made function use define_distribution().

use_distribution() uses gaussian kernel smoothing with a bandwidth parameter calculated by stats::density(). Values for unobserved quantiles are calculated by linear interpolation.

define_distribution() takes as argument a function with a single argument, x, corresponding to a vector of quantiles. It returns the distribution values for the given quantiles. See examples.

Examples

define_distribution(
  function(x) stats::qexp(p = x, rate = 0.5)
)

# a mixture of 2 gaussians
x <- c(rnorm(100), rnorm(100, 6))
plot(density(x))

use_distribution(x)

heemod documentation built on May 29, 2024, 8:17 a.m.