designEst: Optimal group size determination based on minimal MSE when...

In binGroup2: Identification and Estimation using Group Testing

Optimal group size determination based on minimal MSE when estimating an overall prevalence

Description

Find the group size s for a fixed number of groups n and an assumed true proportion p.tr, for which the mean squared error (MSE) of the point estimator is minimal and bias is within a restriction.

Usage

designEst(n, smax, p.tr, biasrest = 0.05)

Arguments

n	integer specifying the fixed number of groups.
smax	integer specifying the maximum group size allowed in the planning of the design.
p.tr	assumed true proportion of the "positive" trait in the population, specified as a value between 0 and 1.
biasrest	a value between 0 and 1 specifying the absolute bias maximally allowed.

Details

Swallow (1985) recommends the use of the upper bound of the expected range of the true proportion p.tr for optimization of the design. For further details, see Swallow (1985). Note that the specified number of groups must be less than n=1020.

Value

A list containing:

call	the function call
result	a data frame containing: mse the mean squared error of the estimator. sout the group size s for which the MSE of the estimator is minimal for the given n and p.tr and for which the bias restriction biasrest is not violated. In the case that the minimum MSE is achieved for a group size s>=smax, the value of smax is returned. exp the expected value of the estimator. varp the variance of the estimator. bias the bias of the estimator.
bias.reached	a logical value indicating whether the bias restriction biasrest was violated.
smax.reached	a logical value indicating whether the maximum group size allowed smax was reached.

Author(s)

This function was originally written by Frank Schaarschmidt as the estDesign function for the binGroup package. Minor modifications were made for inclusion in the binGroup2 package.

References

\insertRef

Swallow1985binGroup2

See Also

designPower for choice of the group testing design according to the power in a hypothesis test.

Other estimation functions: designPower(), gtPower(), gtTest(), gtWidth(), propCI(), propDiffCI()

Examples

# Compare to Table 1 in Swallow (1985):
designEst(n = 10, smax = 100, p.tr = 0.001)
designEst(n = 10, smax = 100, p.tr = 0.01)
designEst(n = 25, smax = 100, p.tr = 0.05)
designEst(n = 40, smax = 100, p.tr = 0.25)
designEst(n = 200, smax = 100, p.tr = 0.30)

binGroup2 documentation built on Nov. 14, 2023, 9:06 a.m.