Linear regression metamodeling
This function estimates a linear regression metamodel for a given decision-analytic model by using the results of a probabilistic sensitivity analysis (PSA)
metamodel(
analysis = c("oneway", "twoway", "multiway"),
psa,
params = NULL,
strategies = NULL,
outcome = c("eff", "cost", "nhb", "nmb", "nhb_loss", "nmb_loss", "nhb_loss_voi",
"nmb_loss_voi"),
wtp = NULL,
type = c("linear", "gam", "poly"),
poly.order = 2,
k = -1
)analysis |
either "oneway" or "twoway" |
psa |
psa object |
params |
string vector with the name(s) of the parameter of interest. Defaults to all. |
strategies |
vector of strategies to consider. The default (NULL) is that all strategies are considered. |
outcome |
either effectiveness ("eff"), cost ("cost"), net health benefit ("nhb"), net monetary benefit ("nmb"), or the opportunity loss in terms of NHB or NMB ("nhb_loss" and "nmb_loss", respectively). "nmb_loss_voi" and "nhb_loss_voi" are only used in internal function calls of metamodel within other VOI functions. |
wtp |
if outcome is NHB or NMB (or the associated loss), must provide the willingness-to-pay threshold |
type |
type of metamodel |
poly.order |
order of polynomial for the linear regression metamodel. Default: 2 |
k |
the dimension of the basis used to represent the smooth term.
The default depends on the number of variables that the smooth is a
function of. |
The most important option is analysis, which can be either "oneway"
or twoway. If analysis == "oneway", a separate metamodel is created
for each combination of the parameters in params and strategies in strategies
(by default, this is all strategies and parameters).
If analysis == "twoway", params must be a vector of two parameters, and a metamodel
is created with these two parameters for each strategy in strategies.
A metamodel object, which contains a list of metamodels and other relevant information.
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