FFD (version 1.0-9)

computeAlphaLimitedSampling: FUNCTION to compute the average alpha-error (= error of first kind) for limited sampling.

Description

For sampling strategy "limited sampling" (see Ziller et al., 2002) the function computes the herd-level alpha-errors (= 1-herd sensitivity) for each stock size, as well as the average herd-level alpha-error.

Usage

computeAlphaLimitedSampling(stockSizeVector, sampleSizeLtd, 
    intraHerdPrevalence, diagSensitivity, 
    diagSpecificity = 1, groupVec = NULL)

Value

List of 3 elements:

alphaDataFrame

Data frame. Variables size = vector of the unique herd sizes in the population, alpha = vector of herd-level alpha errors attained by limited sampling scheme for the different herd sizes.

meanAlpha

Numeric between 0 and 1. Mean alpha-error attained by strategy "limited sampling" for given sample size and herd size distribution.

meanAlphaRiskGroups

If groupVec is specified, the mean alpha error is returned for each group, concatenated into a vector. Otherwise this list element is NULL

Arguments

stockSizeVector

Integer vector. Stock sizes of the herds.

sampleSizeLtd

Integer. Sample size for limited sampling, i.e., for each herd sampleSizeLtd animals are tested, or of the herd contains less than sampleSizeLtd animals the entire herd is tested.

intraHerdPrevalence

Numeric between 0 and 1. Intra-herd prevalence. The number of diseased animals per herd is computed as max(1,round(intraHerdPrevalence*stockSizeVector)) (it is assumed that at least one animal is diseased).

diagSensitivity

Numeric between 0 and 1. Sensitivity (= probability of a testpositive result, given the tested individual is diseased) of the diagnostic test.

diagSpecificity

Numeric between 0 and 1. Specificity (= probability of a testnegative result, given the tested individual is not diseased) of the diagnostic test.

groupVec

Character vector. Optional parameter. If specified it must have the same length as stockSizeVector. Defines the gouping of the data. Mean Alpha is then returned for each group.

Author

Ian Kopacka <[e-mail]>

References

M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, "Analysis of sampling strategies to substantiate freedom from disease in large areas", Prev. Vet. Med. 52 (2002), pp. 333-343.

See Also

computePValue

Examples

data(sheepData)
alphaList <- computeAlphaLimitedSampling(stockSizeVector = 
    sheepData$nSheep, sampleSizeLtd = 7, 
    intraHerdPrevalence = 0.2, diagSensitivity = 0.9, 
    diagSpecificity = 1)