LogNormModVar {rdecision}

A model variable whose uncertainty follows a log Normal distribution

Description

An R6 class representing a model variable with log Normal uncertainty.

Details

A model variable for which the uncertainty in the point estimate can be modelled with a log Normal distribution. One of seven parametrizations defined by Swat et al can be used. Inherits from ModVar.

Super class

rdecision::ModVar -> LogNormModVar

Methods

Public methods

• LogNormModVar$new()

• LogNormModVar$is_probabilistic()

• LogNormModVar$clone()

Inherited methods

• rdecision::ModVar$SD()
• rdecision::ModVar$description()
• rdecision::ModVar$distribution()
• rdecision::ModVar$get()
• rdecision::ModVar$is_expression()
• rdecision::ModVar$mean()
• rdecision::ModVar$mode()
• rdecision::ModVar$quantile()
• rdecision::ModVar$r()
• rdecision::ModVar$set()
• rdecision::ModVar$units()

Method new()

Create a model variable with log normal uncertainty.

Usage

LogNormModVar$new(description, units, p1, p2, parametrization = "LN1")

Arguments

description

A character string describing the variable.

units

Units of the quantity; character string.

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 LogNormModVar object.

Method is_probabilistic()

Tests whether the model variable is probabilistic, i.e., a random variable that follows a distribution, or an expression involving random variables, some of which follow distributions.

Usage

LogNormModVar$is_probabilistic()

Returns

TRUE if probabilistic

Method clone()

The objects of this class are cloneable with this method.

Usage

LogNormModVar$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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, doi:10.1093/bioinformatics/btw170.

See Also

LogNormDistribution.