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rdecision (version 1.1.2)

LogNormDistribution: A parametrized log Normal probability distribution

Description

An R6 class representing a log Normal distribution.

Arguments

Author

Andrew J. Sims andrew.sims@newcastle.ac.uk

Super class

rdecision::Distribution -> LogNormDistribution

Methods

Inherited methods

Method new()

Create a log normal distribution.

Usage

LogNormDistribution$new(p1, p2, parametrization = "LN1")

Arguments

p1

First hyperparameter, a measure of location. See Details.

p2

Second hyperparameter, a measure of spread. See Details.

parametrization

A character string taking one of the values "LN1" (default) through "LN7" (see Details).

Returns

A LogNormDistribution object.

Method distribution()

Accessor function for the name of the distribution.

Usage

LogNormDistribution$distribution()

Returns

Distribution name as character string ("LN1", "LN2" etc.).

Method sample()

Draw a random sample from the model variable.

Usage

LogNormDistribution$sample(expected = FALSE)

Arguments

expected

If TRUE, sets the next value retrieved by a call to r() to be the mean of the distribution.

Returns

Updated LogNormDistribution object.

Method mean()

Return the expected value of the distribution.

Usage

LogNormDistribution$mean()

Returns

Expected value as a numeric value.

Method mode()

Return the point estimate of the variable.

Usage

LogNormDistribution$mode()

Returns

Point estimate (mode) of the log normal distribution.

Method SD()

Return the standard deviation of the distribution.

Usage

LogNormDistribution$SD()

Returns

Standard deviation as a numeric value

Method quantile()

Return the quantiles of the log normal distribution.

Usage

LogNormDistribution$quantile(probs)

Arguments

probs

Vector of probabilities, in range [0,1].

Returns

Vector of quantiles.

Method clone()

The objects of this class are cloneable with this method.

Usage

LogNormDistribution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

A parametrized Log Normal distribution inheriting from class Distribution. Swat (2017) defined seven parametrizations of the log normal distribution. These are linked, allowing the parameters of any one to be derived from any other. All 7 parametrizations require two parameters as follows:

LN1

\(p_1=\mu\), \(p_2=\sigma\), where \(\mu\) and \(\sigma\) are the mean and standard deviation, both on the log scale.

LN2

\(p_1=\mu\), \(p_2=v\), where \(\mu\) and \(v\) are the mean and variance, both on the log scale.

LN3

\(p_1=m\), \(p_2=\sigma\), where \(m\) is the median on the natural scale and \(\sigma\) is the standard deviation on the log scale.

LN4

\(p_1=m\), \(p_2=c_v\), where \(m\) is the median on the natural scale and \(c_v\) is the coefficient of variation on the natural scale.

LN5

\(p_1=\mu\), \(p_2=\tau\), where \(\mu\) is the mean on the log scale and \(\tau\) is the precision on the log scale.

LN6

\(p_1=m\), \(p_2=\sigma_g\), where \(m\) is the median on the natural scale and \(\sigma_g\) is the geometric standard deviation on the natural scale.

LN7

\(p_1=\mu_N\), \(p_2=\sigma_N\), where \(\mu_N\) is the mean on the natural scale and \(\sigma_N\) is the standard deviation on the natural scale.

References

Briggs A, Claxton K and Sculpher M. Decision Modelling for Health Economic Evaluation. Oxford 2006, ISBN 978-0-19-852662-9. Leaper DJ, Edmiston CE and Holy CE. Meta-analysis of the potential economic impact following introduction of absorbable antimicrobial sutures. British Journal of Surgery 2017;104:e134-e144. Swat MJ, Grenon P and Wimalaratne S. Ontology and Knowledge Base of Probability Distributions. EMBL-EBI Technical Report (ProbOnto 2.5), 13 January 2017, https://sites.google.com/site/probonto/download.