The function mu.modwoc calculates conservative sample sizes, in the sense that the desired
posterior credible interval coverage and length for a normal mean are guaranteed over a given proportion of data sets that can arise according to the prior information.
mu.modwoc(len, alpha, beta, n0, level = 0.95, worst.level = 0.95)The required sample size given the inputs to the function.
The desired 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)
Prior sample size equivalent for the mean
The desired fixed coverage probability of the posterior credible interval (e.g., 0.95)
The probability that the length of the posterior credible interval of fixed coverage probability level will be at most len
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. Assume that the mean is unknown, but has
prior information equivalent to n0 previous observations. The function mu.modwoc
returns the required sample size to attain the desired length len
for the posterior credible interval of fixed coverage probability level
for the unknown mean.
The Modified Worst Outcome Criterion used is conservative, in the sense that the posterior credible interval
length len is guaranteed over the worst.level proportion of all
possible data sets that can arise according to the prior information, for a fixed coverage probability level.
This function uses a fully Bayesian approach to sample size determination.
Therefore, the desired coverages and lengths are only realized if the prior distributions input to the function
are used for final inferences. Researchers preferring to use the data only for final inferences are encouraged
to use the Mixed Bayesian/Likelihood version of the function.
Joseph L, Bélisle P.
Bayesian sample size determination for Normal means and differences between Normal means
The Statistician 1997;46(2):209-226.
mu.acc, mu.alc, mu.varknown, mu.mblacc, mu.mblalc, mu.mblmodwoc, mu.mbl.varknown, mu.freq, mudiff.acc, mudiff.alc, mudiff.modwoc, mudiff.acc.equalvar, mudiff.alc.equalvar, mudiff.modwoc.equalvar, mudiff.varknown, mudiff.mblacc, mudiff.mblalc, mudiff.mblmodwoc, mudiff.mblacc.equalvar, mudiff.mblalc.equalvar, mudiff.mblmodwoc.equalvar, mudiff.mbl.varknown, mudiff.freq
mu.modwoc(len=0.2, alpha=2, beta=2, n0=10)Run the code above in your browser using DataLab