mu.varknown returns the required sample size
to reach a desired posterior credible interval length and coverage probability for a normal mean when the variance is known.
mu.varknown(len, lambda, n0, level = 0.95)
The desired total length of the posterior credible interval for the mean
The known precision (reciprocal of variance)
Prior sample size equivalent for the mean
The desired coverage probability of the posterior credible interval (e.g., 0.95)
Assume that a sample will be collected in order to estimate
the mean of a normally distributed random variable with known precision lambda (where the precision is the reciprocal of the variance).
Assume that the mean is unknown, but has
prior information equivalent to n0 previous observations. The function
mu.varknown returns the
required sample size to attain the desired length len and
coverage probability level for the posterior credible interval for the unknown mean.
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 size given the inputs to the function.
The sample size returned by this function is exact.
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.
mu.varknown(len=0.2, lambda=1/4, n0=10)
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