computeAposterioriError: FUNCTION to compute the a-posteriori error.

In FFD: Freedom from Disease

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")

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).

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.

Value

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

Author(s)

Ian Kopacka <[e-mail]>

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")

FFD documentation built on Nov. 10, 2022, 5:48 p.m.