mudiff.varknown returns the required sample sizes
to reach a given posterior credible interval length and coverage probability for the difference between two normal means, when variances are known.
The desired total length of the posterior credible interval for the difference between the two unknown means
The known precision (reciprocal of variance) for the first population
Prior sample size equivalent for the mean for the first population
The known precision (reciprocal of variance) for the second population
Prior sample size equivalent for the mean for the second population
The desired coverage probability of the posterior credible interval (e.g., 0.95)
logical. Whether or not the final group sizes (n1, n2) are forced to be equal:
Assume that a sample from each of two populations will be
collected in order to estimate the difference between two independent normal means
when the variances are known. Assume that the means are unknown, but have
prior information equivalent to (n01, n02) previous observations, respectively. The function
mudiff.varknown returns the required sample sizes to attain the
desired length len and coverage probability level for the posterior credible interval
for the difference between the two unknown means.
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.
The required sample sizes (n1, n2) for each group given the inputs to the function.
The sample sizes returned by this function are exact.
Lawrence Joseph firstname.lastname@example.org 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.varknown(len=0.2, lambda1=1, n01=10, lambda2=1/1.5, n02=25)
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