FFD (version 1.0-9)

computeAposterioriError: FUNCTION to compute the a-posteriori error.

Description

For a sampling scheme designed to substantiate freedom from disease the function computes the a-posteriori alpha-error, i.e., the actual alpha-error based on the drawn sample.

Usage

computeAposterioriError(alphaErrorVector, nPopulation, 
    nDiseased, method = "default")

Value

The return value is the a-posteriori alpha-error based on the sample at hand (numeric scalar).

Arguments

alphaErrorVector

Numeric vector. Alpha-error (between 0 and 1) of each herd in the sample.

nPopulation

Integer. Population size, i.e., total numer of herds in the population.

nDiseased

Integer. Number of diseased herds in the population according to the design prevalence.

method

Character string. "exact" for exact error, "approx" for approximation (recommended for nDiseased > 7).

Author

Ian Kopacka <[e-mail]>

Details

The exact evaluation of the alpha-error is computationally complex, due to combinatirical issues. In order to increase effectivity parts of the code were implemented in C. Still, for nDiseased > 7 the computation may take very long and it is generally not recommended to use the exact calculation. Rather the approximation should be used for nDiseased > 7.

Examples

## Freedom from disease using limited sampling with sampleSizeLtd = 7.
## Data: sheep holdings in state "Steiermark".
######################################################################
data(sheepData)
popVec <- sheepData$nSheep[sheepData$state == 6]
N1 <- length(popVec)
sampleSizeLtd <- 7
intraHerdPrev <- 0.13
designPrev <- 0.002
nDiseased <- round(designPrev*N1)
## Draw the sample:
n1 <- 1560
samplePop <- sample(x = popVec, size = n1, replace = FALSE, prob = NULL)
## Compute alpha-errors for the sample:
alphaList <- computeAlphaLimitedSampling(stockSizeVector = samplePop, 
        sampleSizeLtd = sampleSizeLtd, 
        intraHerdPrevalence = intraHerdPrev, 
        diagSensitivity = 0.9, diagSpecificity = 1)
alphaDataFrame <- merge(x = data.frame(size = samplePop), 
    y = alphaList$alphaDataFrame, by = "size", all.x = TRUE, all.y = FALSE)
## Compute the a-posteriori alpha-error:
alphaAPostApprox <- computeAposterioriError(alphaErrorVector = 
    alphaDataFrame$alpha, nPopulation = N1, nDiseased = nDiseased,
    method = "approx")
alphaAPostExact <- computeAposterioriError(alphaErrorVector = 
    alphaDataFrame$alpha, nPopulation = N1, nDiseased = nDiseased,
    method = "exact")