SampleSizeMeans (version 1.2.3)

mudiff.varknown: Bayesian sample size determination for differences in normal means when variances are known

Description

The function 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.

Usage

mudiff.varknown(len, lambda1, n01, lambda2, n02, level = 0.95, equal = TRUE)

Value

The required sample sizes (n1, n2) for each group given the inputs to the function.

Arguments

len

The desired total length of the posterior credible interval for the difference between the two unknown means

lambda1

The known precision (reciprocal of variance) for the first population

n01

Prior sample size equivalent for the mean for the first population

lambda2

The known precision (reciprocal of variance) for the second population

n02

Prior sample size equivalent for the mean for the second population

level

The desired coverage probability of the posterior credible interval (e.g., 0.95)

equal

logical. Whether or not the final group sizes (n1, n2) are forced to be equal:

		when equal = TRUE,	final sample sizes n1 = n2;
		when equal = FALSE,	final sample sizes (n1, n2) minimize the posterior variance given a total of n1+n2 observations

Author

Lawrence Joseph and Patrick Bélisle

Details

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.

References

Joseph L, Bélisle P.
Bayesian sample size determination for Normal means and differences between Normal means
The Statistician 1997;46(2):209-226.

See Also

mudiff.acc, mudiff.alc, mudiff.modwoc, mudiff.acc.equalvar, mudiff.alc.equalvar, mudiff.modwoc.equalvar, mudiff.mbl.varknown, mudiff.mblacc, mudiff.mblalc, mudiff.mblmodwoc, mudiff.mblacc.equalvar, mudiff.mblalc.equalvar, mudiff.mblmodwoc.equalvar, mudiff.freq, mu.varknown, mu.acc, mu.alc, mu.modwoc, mu.mbl.varknown, mu.mblacc, mu.mblalc, mu.mblmodwoc, mu.freq

Examples

mudiff.varknown(len=0.2, lambda1=1, n01=10, lambda2=1/1.5, n02=25)