computeAposterioriError: FUNCTION to compute the a-posteriori error.
In FFD: Freedom from Disease

Description Usage Arguments Details Value Author(s) Examples

View source: R/computeAposterioriError.R

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.

1 2	computeAposterioriError(alphaErrorVector, nPopulation, nDiseased, method = "default")

`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).

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.

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

Ian Kopacka <ian.kopacka@ages.at>

## 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")