Constructor for class 'IndSampling'.

Description

Creates an object of the class 'IndSampling'. For given survey parameters (passed to the function as an object of the class SurveyData) indSampling() computes the the number of herds to test, the expected total number of animals to test, the expected total cost of a survey using limited sampling with a given herd sensitivity herdSensitivity, as well as a lookup table for the number of animals to test per herd, depending on the herd size.

Usage

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indSampling(survey.Data, herdSensitivity, nSampleFixVec = NULL, 
	probVec = NULL)

Arguments

survey.Data

Object of class SurveyData. Created by using the function surveyData.

herdSensitivity

Numeric between 0 and 1. Desired herd sensitivity.

nSampleFixVec

Numeric vector containing some NAs (optional argument). For risk groups for which the sample size is fixed specify the sample size. For the risk groups for which the sample size should be computed set NA (order of the risk groups must be the same order as in survey.Data@riskValueData).

probVec

Numeric vector. For those risk groups for which the sample size should be computed sample probabilities must be specified. The vector must have the same length as the number of NA entries in nSampleFixVec or if nSampleFixVec is not specified, probVec must have the same length as the number of rows in survey.Data@riskValueData.

Value

The function returns an object of the class IndSampling.

Author(s)

Ian Kopacka <ian.kopacka@ages.at>

References

A.R. Cameron and F.C. Baldock, "A new probablility formula to substantiate freedom from disease", Prev. Vet. Med. 34 (1998), pp. 1-17.

A.R. Cameron and F.C. Baldock, "Two-stage sampling surveys to substantiate freedom from disease", Prev. Vet. Med. 34 (1998), pp. 19-30.

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

See IndSampling and SurveyData for additional details.

Examples

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data(sheepData)
sheepData$size <- ifelse(sheepData$nSheep < 30, "small", "large")
riskValueData <- data.frame(riskGroup = c("small", "large"), 
    riskValues = c(1,2))
mySurvey <- surveyData(nAnimalVec = sheepData$nSheep,
		riskGroupVec = sheepData$size,
		riskValueData = riskValueData,
		populationData = sheepData, designPrevalence = 0.002,
		alpha = 0.05, intraHerdPrevalence = 0.13,
		diagSensitivity = 0.9, costHerd = 30, costAnimal = 7.1)
## Individual sampling without risk groups:    
myIndSampling <- indSampling(survey.Data = mySurvey, 
    herdSensitivity = 0.7)
## Individual sampling with risk groups:    
myIndSamplingRG <- indSampling(survey.Data = mySurvey, 
    herdSensitivity = 0.7, nSampleFixVec = NULL, probVec = c(1,4))