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
)either "oneway" or "twoway"
psa object
string vector with the name(s) of the parameter of interest. Defaults to all.
vector of strategies to consider. The default (NULL) is that all strategies are considered.
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.
if outcome is NHB or NMB (or the associated loss), must provide the willingness-to-pay threshold
type of metamodel
order of polynomial for the linear regression metamodel. Default: 2
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. k should not be less than the dimension of the
null space of the penalty for the term (see
null.space.dimension), but will be reset if
it is. See choose.k for further information.
A metamodel object, which contains a list of metamodels and other relevant information.
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.