The optimum sample size for a given willingness to pay is determined either by a simple search over the supplied ENBS estimates for different sample sizes, or by a regression and interpolation method.
enbs_opt(x, pcut = 0.05, smooth = FALSE, smooth_df = NULL, keep_preds = FALSE)A data frame with one row, and the following columns:
ind: An integer index identifying, e.g. the willingness to pay and other common characteristics of the ENBS estimates (e.g. incident population size, decision time horizon). This is copied from x$ind.
enbsmax: the maximum ENBS
nmax: the sample size at which this maximum is achieved
nlower: the lowest sample size for which the ENBS is within
pcut (default 5%) of its maximum value
nupper: the corresponding highest ENBS
Data frame containing a set of ENBS estimates for
different sample sizes, which will be optimised over. Usually
this is for a common willingness-to-pay. The required components
are enbs and n.
Cut-off probability which defines a "near-optimal" sample size.
The minimum and maximum sample size for which the ENBS is within
pcut (by default 5%) of its maximum value will be determined.
If TRUE, then the maximum ENBS is determined after
fitting a nonparametric regression to the data frame x, which
estimates and smooths the ENBS for every integer sample size in the range
of x$n. The regression is done using the default settings of
gam from the mgcv package.
If this is FALSE, then no smoothing or interpolation is done, and
the maximum is determined by searching over the values supplied in
x.
Basis dimension for the smooth regression. Passed as the
k argument to the s() term in gam. Defaults to
6, or the number of unique sample sizes minus 1 if this is lower. Set
to a higher number if you think the smoother does not capture the
relation of ENBS to sample size accurately enough.
If TRUE and smooth=TRUE then the data frame of
predictions from the smooth regression model is stored in the "preds"
attribute of the result.