powerparameter: Computes empirical Bayes estimate of power parameter

In wiesenfa/ESS: Quantification of prior informativeness in terms of prior effective and effective current sample size

Description

Computes empirical Bayes estimate of power parameter (Gravestock&Held, 2017).

Usage

## S3 method for class 'betaMix'
powerparameter(prior, n, r, p.prior.a = 1, p.prior.b = 1, ...)
## S3 method for class 'normMix'
powerparameter(prior, n, m, sigma, ...)

Arguments

prior	An RBesT mixture object with a single mixture component
n	sample size
r	Number of successes
m	Sample mean
sigma	Sample standard deviation
p.prior.a	in case of binary outcome, shape1 parameter of initial beta prior for successes.
p.prior.b	in case of binary outcome, shape2 parameter of initial beta prior for successes.
...

Author(s)

Manuel Wiesenfarth

References

Gravestock, I. and Held, L. (2017). Adaptive power priors with empirical bayes for clinical trials. Pharmaceutical statistics, 16(5):349-360.

See Also

postmix.powerprior,

package StudyPrior

Examples

# Normal Outcome
  # standard deviation
    sigma=1
  # prior with nominal prior ESS=50
    info <-mixnorm(informative=c(1, 0,1/sqrt(50)), sigma=sigma)
  
  n=10 # sample size

  m=0 #data mean equal to prior mean
  powerparameter(info,m=m,n=n,sigma=sigma)
  m=2 #prior-data conflict
  powerparameter(info,m=m,n=n,sigma=sigma)

wiesenfa/ESS documentation built on June 19, 2019, 4:19 p.m.