Constructor for class 'IndSamplingSummary'.

Description

Creates an object of the class 'IndSamplingSummary'. For given survey parameters (passed to the function as an object of the class SurveyData) indSamplingSummary() computes the number of herds to test, the expected total number of animals to test and the expected total cost of a survey using individual sampling with a given sequence of herd sensitivities. This sequence ranges from 0.1 to the sensitivity of the diagnostic test specified in survey.Data. The step size for the herd sensitivities can be specified by the user via the argument stepSize. If no step size is specified a step size of 0.02 is used.

Usage

1
2
indSamplingSummary(survey.Data, stepSize = 0.02,
    nSampleFixVec = NULL, probVec = NULL)

Arguments

survey.Data

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

stepSize

Numeric. A series of parameters is computed for a sequence of herd sensitivities. The argument stepSize specifies the step size used in the discretization of the herd sensitivities (default = 0.02).

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 IndSamplingSummary.

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 IndSamplingSummary and SurveyData for additional details.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
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:    
myIndSamplingSummary <- indSamplingSummary(survey.Data = mySurvey, 
    stepSize = 0.06)
## Individual sampling with risk groups:    
myIndSamplingSummaryRG <- indSamplingSummary(survey.Data = mySurvey, 
    stepSize = 0.06, nSampleFixVec = NULL, probVec = c(1,4))