LogNormDistribution {rdecision} | R Documentation |
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
,
, where
and
are the mean and standard deviation, both on the log scale.
- LN2
,
, where
and
are the mean and variance, both on the log scale.
- LN3
,
, where
is the median on the natural scale and
is the standard deviation on the log scale.
- LN4
,
, where
is the median on the natural scale and
is the coefficient of variation on the natural scale.
- LN5
,
, where
is the mean on the log scale and
is the precision on the log scale.
- LN6
,
, where
is the median on the natural scale and
is the geometric standard deviation on the natural scale.
- LN7
,
, where
is the mean on the natural scale and
is the standard deviation on the natural scale.
Super class
rdecision::Distribution
-> LogNormDistribution
Methods
Public 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.
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 andrew.sims@newcastle.ac.uk
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.