The function mu.mblalc returns the required sample size
to reach a given posterior credible interval length on average - using a mixed Bayesian/likelihood approach - for a fixed coverage probability for a normal mean.
mu.mblalc(len, alpha, beta, level = 0.95)The required sample size given the inputs to the function.
The desired average length of the posterior credible interval for the mean
First prior parameter of the Gamma density for the precision (reciprocal of the variance)
Second prior parameter of the Gamma density for the precision (reciprocal of the variance)
The desired fixed coverage probability of the posterior credible interval (e.g., 0.95)
Lawrence Joseph lawrence.joseph@mcgill.ca and Patrick Bélisle
Assume that a sample will be collected in order to estimate
the mean of a normally distributed random variable. Assume that the precision (reciprocal of the variance) of
this random variable is unknown, but has prior information in the form of a
Gamma(alpha, beta) density.
The function mu.mblalc returns the
required sample size to attain the desired average length len
for the posterior credible interval of fixed coverage probability level
for the unknown mean.
This function uses a Mixed Bayesian/Likelihood (MBL) approach.
MBL approaches use the prior information to derive the predictive distribution of the data, but use only the likelihood function for final inferences.
This approach is intended to satisfy investigators who recognize that prior information is important for planning purposes but prefer to base final inferences only on the data.
Joseph L, Bélisle P.
Bayesian sample size determination for Normal means and differences between Normal means
The Statistician 1997;46(2):209-226.
mu.mblacc, mu.mblmodwoc, mu.mbl.varknown, mu.acc, mu.alc, mu.modwoc, mu.varknown, mu.freq, mudiff.mblacc, mudiff.mblalc, mudiff.mblmodwoc, mudiff.mblacc.equalvar, mudiff.mblalc.equalvar, mudiff.mblmodwoc.equalvar, mudiff.mbl.varknown, mudiff.acc, mudiff.alc, mudiff.modwoc, mudiff.acc.equalvar, mudiff.alc.equalvar, mudiff.modwoc.equalvar, mudiff.varknown, mudiff.freq
mu.mblalc(len=0.2, alpha=2, beta=2)Run the code above in your browser using DataLab