| GammaModVar {rdecision} | R Documentation |
A model variable whose uncertainty follows a Gamma distribution
Description
An R6 class for a model variable with Gamma uncertainty.
Details
A model variable for which the uncertainty in the point estimate can
be modelled with a Gamma distribution. The hyperparameters of the
distribution are the shape (k) and the scale (theta). Note
that although Briggs et al (2006) use the shape, scale formulation,
they use alpha, beta as parameter names. Inherits from
class ModVar.
Super class
rdecision::ModVar -> GammaModVar
Methods
Public methods
Inherited methods
Method new()
Create an object of class GammaModVar.
Usage
GammaModVar$new(description, units, shape, scale)
Arguments
descriptionA character string describing the variable.
unitsUnits of the variable, as character string.
shapeshape parameter of the Gamma distribution.
scalescale parameter of the Gamma distribution.
Returns
An object of class GammaModVar.
Method is_probabilistic()
Tests whether the model variable is probabilistic, i.e., a random variable that follows a distribution, or an expression involving random variables, some of which follow distributions.
Usage
GammaModVar$is_probabilistic()
Returns
TRUE if probabilistic
Method clone()
The objects of this class are cloneable with this method.
Usage
GammaModVar$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Note
The Gamma model variable class can be used to model the uncertainty of
the mean of a count quantity which follows a Poisson distribution. The Gamma
distribution is the conjugate prior of a Poisson distribution, and the shape
and scale relate directly to the number of intervals from which the mean
count has been estimated. Specifically, the shape (k) is equal to the
total count of events in 1/\theta intervals, where \theta is the
scale. For example, if 200 counts were observed in a sample of 100 intervals,
setting shape=200 and scale=1/100 gives a Gamma distribution
with a mean of 2 and a 95% confidence interval from 1.73 to 2.29.
Author(s)
Andrew J. Sims andrew.sims@newcastle.ac.uk
References
Briggs A, Claxton K, Sculpher M. Decision modelling for health economic evaluation. Oxford, UK: Oxford University Press; 2006.