# 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 |

`stepSize` |
Numeric. A series of parameters is computed for a
sequence of herd sensitivities. The argument |

`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 |

`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 |

### 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))
``` |

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