View source: R/sim_power_best_bin_rank.R
| sim_power_best_bin_rank | R Documentation |
Estimates the empirical power to rank the most promising group as the best, based on binomial outcomes, via simulation.
sim_power_best_bin_rank(
noutcomes,
p1,
dif,
weights,
ngroups,
npergroup,
nsim,
conf.level = 0.95
)
noutcomes |
Integer. Number of outcomes to evaluate. |
p1 |
Numeric. Event probability in the first group, i.e. the true best
group (scalar or vector of length |
dif |
Numeric. Amount by which the first (true best) group's probability
exceeds the other |
weights |
Numeric vector. Weights for each outcome. If scalar, applied equally. |
ngroups |
Integer. Number of groups. |
npergroup |
Integer or vector. Sample size per group. |
nsim |
Integer. Number of simulations. |
conf.level |
Numeric. Confidence level for the empirical power estimate#' |
Each outcome is assumed to follow an independent binomial distribution. The
first group is always the true best group: it is simulated with event
probability p1, while the other ngroups - 1 groups share probability
p1 - dif. The function sums weighted ranks across multiple outcomes to
determine the top group, and estimates the empirical power to correctly
identify the first group as the best.
If multiple outcomes are defined, weights can be applied to prioritize some
outcomes over others. Weights are automatically scaled to sum 1. For each
outcome, groups are ranked from lowest (1) to highest (ngroups) observed
proportion; the group with the highest total weighted rank across outcomes
is considered the best. Power is the proportion of simulations in which
that group is the first group.
An S3 object of class empirical_power_result, which contains
the estimated empirical power and its confidence interval. The object can
be printed, formatted, or further processed using associated S3 methods.
See also empirical_power_result.
empirical_power_result
sim_power_best_bin_rank(
noutcomes = 2,
p1 = 0.80,
dif = 0.15,
weights = 1,
ngroups = 3,
npergroup = 30,
nsim = 1000,
conf.level = 0.95)
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