mudiff.mblalc returns the required sample sizes
to reach a given posterior credible interval length on average for a fixed coverage probability - using a mixed Bayesian/likelihood approach - for the difference between two normal means.
mudiff.mblalc(len, alpha1, beta1, alpha2, beta2, level = 0.95, m = 10000, mcs = 3)
The desired average length of the posterior credible interval for the difference between the two unknown means
First prior parameter of the Gamma density for the precision (reciprocal of the variance) for the first population
Second prior parameter of the Gamma density for the precision (reciprocal of the variance) for the first population
First prior parameter of the Gamma density for the precision (reciprocal of the variance) for the second population
Second prior parameter of the Gamma density for the precision (reciprocal of the variance) for the second population
The desired fixed coverage probability of the posterior credible interval (e.g., 0.95)
The number of points simulated from the preposterior distribution of the data. For each point, the length of the highest posterior density interval of fixed coverage probability level is estimated, in order to approximate the average length. Usually 10000 is sufficient, but one can increase this number at the expense of program running time.
The Maximum number of Consecutive Steps allowed in the same direction in the march towards the optimal sample size, before the result for the next upper/lower bound is cross-checked. In our experience, mcs = 3 is a good choice.
Assume that a sample from each of two populations will be
collected in order to estimate the difference between two independent normal means.
Assume that the precision within each of the two the populations are
unknown, but have prior information in the form of
Gamma(alpha1, beta1) and Gamma(alpha2, beta2) densities, respectively.
mudiff.mblalc returns the required sample sizes to attain the
desired average length len for the posterior credible interval of fixed coverage probability level
for the difference between the two unknown means.
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.
The required sample sizes (n1, n2) for each group given the inputs to the function.
The sample sizes are calculated via Monte Carlo simulations, and therefore may vary from one call to the next.
Lawrence Joseph email@example.com and Patrick Belisle
Joseph L, Belisle P.
Bayesian sample size determination for Normal means and differences between Normal means
The Statistician 1997;46(2):209-226.
mudiff.mblalc(len=0.2, alpha1=2, beta1=2, alpha2=3, beta2=3)
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