evppivar.Rd
Calculate the expected value of partial perfect information for an estimation problem. This computes the expected reduction in variance in some quantity of interest with perfect information about a parameter or parameters of interest.
evppivar( outputs, inputs, pars = NULL, method = NULL, nsim = NULL, verbose = TRUE, ... )
outputs | a vector of values for the quantity of interest, sampled from the uncertainty distribution of this quantity that is induced by the uncertainty about the parameters. Typically this will come from a Monte Carlo sample, where we first sample from the uncertainty distributions of the parameters, and then compute the quantity of interest as a function of the parameters. It might also be produced by a Markov Chain Monte Carlo sample from the joint distribution of parameters and outputs. |
---|---|
inputs | Matrix or data frame of samples from the uncertainty
distribution of the input parameters of the decision model. The number
of columns should equal the number of parameters, and the columns should
be named. This should have the same number of rows as there are samples
in |
pars | A character vector giving the parameters of interest, for which a single EVPPI calculation is required. If the vector has multiple element, then the joint expected value of perfect information on all these parameters together is calculated. Alternatively,
|
method | Character string indicating the calculation method. The default methods are based on nonparametric regression:
|
nsim | Number of simulations from the model to use for calculating
EVPPI. The first |
verbose | If |
... | Other arguments to control specific methods. For
For
For
For
For
|
Jackson, C., Presanis, A., Conti, S., & De Angelis, D. (2019). Value of information: Sensitivity analysis and research design in Bayesian evidence synthesis. Journal of the American Statistical Association, 114(528), 1436-1449.