# 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

1 2 | ```
indSampling(survey.Data, herdSensitivity, nSampleFixVec = NULL,
probVec = NULL)
``` |

### Arguments

`survey.Data` |
Object of class |

`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 |

`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 `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

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:
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))
``` |