LogNormDistribution: A parametrized log Normal probability distribution

In rdecision: Decision Analytic Modelling in Health Economics

A parametrized log Normal probability distribution

Description

An R6 class representing a log Normal distribution.

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.

Super class

rdecision::Distribution -> LogNormDistribution

Methods

Public methods

• LogNormDistribution$new()

• LogNormDistribution$distribution()

• LogNormDistribution$sample()

• LogNormDistribution$mean()

• LogNormDistribution$mode()

• LogNormDistribution$SD()

• LogNormDistribution$quantile()

• LogNormDistribution$clone()

Inherited methods

• rdecision::Distribution$order()
• rdecision::Distribution$r()
• rdecision::Distribution$varcov()

---

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.

Note

The log normal distribution may be used to model the uncertainty in an estimate of relative risk (Briggs 2006, p90). If a relative risk estimate is available with a 95% confidence interval, the ⁠"LN7"⁠ parametrization allows the uncertainty distribution to be specified directly. For example, if RR = 0.67 with 95% confidence interval 0.53 to 0.84 (Leaper, 2016), it can be modelled with LogNormModVar$new("rr", "RR", p1=0.67, p2=(0.84-0.53)/(2*1.96)), "LN7").

Author(s)

Andrew J. Sims [e-mail]

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. Bioinformatics 2016;32:2719-2721, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btw170")}.

rdecision documentation built on April 3, 2025, 6:09 p.m.