Description Objects from the Class Slots Methods Note Author(s) See Also Examples

Contains the parameters and the data necessary for a survey to substantiate freedom from disease using "limited sampling". Additionally to the survey parameters: design prevalence (=prevalence of the disease under the null hypothesis), overall significance level (=1-confidence), intra-herd prevalence, sensitivity of the diagnostic test, cost per tested animal and cost per tested herd, the object contains the mean herd sensitivity, the number of herds to be tested, the mean overall number of animals to be tested and the expected costs.

Objects can be created by calls of the form `new("LtdSampling", ...)`

.

`surveyData`

:Object of class

`"SurveyData"`

. Contains all the necessary data and specifications for the survey.`sampleSizeLtd`

:Object of class

`"numeric"`

. Pre-fixed number of animals to be tested per holding, irrespective of the herd size. If a herd contains fewer animals the entire herd is tested.`meanHerdSensitivity`

:Object of class

`"numeric"`

with values between 0 and 1. Mean herd sensitivity in the population.`meanHerdSensPerRG`

:Object of class

`"numeric"`

with values between 0 and 1. Mean herd sensitivity of each risk group (if population is stratified by risk groups).`nHerds`

:Object of class

`"numeric"`

. Number of herds to be tested according to the herd sensitivity`meanHerdSensitivity`

.`nHerdsPerRiskGroup`

:Object of class

`"numeric"`

. Number of herds to be tested per risk group (if population is stratified by risk groups).`nSampleFixVec`

:Object of class

`"numeric"`

. Numeric vector containing some NAs (optional argument). For risk groups for which the sample size is fixed it specifies the sample size. For the risk groups for which the sample size was computed it was set to NA (order of the risk groups is the same as in`[email protected]`

).`probVec`

:Object of class

`"numeric"`

. Contains the sample probabilities for those risk groups for which the sample size was computed (=NA entries in`nSampleFixVec`

).`nAnimalsMean`

:Object of class

`"numeric"`

. Expected total number of animals to be tested in the survey.`expectedCost`

:Object of class

`"numeric"`

. Expected costs of the survey.

- HTML
`signature(x = "LtdSampling")`

: Creates an html file containing the summary data and the diagnostic plots. Title, file name, output directory, css-file, etc. can additionally be specified using the parameters,`filename`

,`outdir`

,`CSSFile`

,`Title`

, as well as all the other parameters of the R2HTML-function`HTMLInitFile`

.- sample
`signature(x = "LtdSampling", size = c("fixed", "dynamic"))`

: Sample herds using limited sampling. Additionally to the argument`x`

of type`LtdSampling`

the method takes an argument`size`

, which is a character string. For`size == "fixed"`

the fixed number of herds given in`[email protected]`

is sampled using simple random sampling. For`size == "dynamic"`

dynamic sampling is used, i.e., based on real-time computation of the a-posteriori alpha- error the sample is updated until the a-posteriori alpha-error falls below the predefined significance level [email protected] The return value is a list with two items:`indexSample`

is a vector of indices of the sample corresponding to`[email protected]@nAnimalVec`

and`aPostAlpha`

containing the a-posteriori alpha-error of the sample.- show
`signature(object = "LtdSampling")`

: Display structure of the class and content of the slots.- summary
`signature(object = "LtdSampling")`

: Display structure of the class and a summary of the content of the slots.

No notes yet.

Ian Kopacka <[email protected]>

The slot `surveyData`

contains an object of the class
`SurveyData`

which is created using `surveyData`

.
Objects of the class `LtdSampling`

are create using the constructor
`ltdSampling`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## Show the structure of the class:
showClass("LtdSampling")
## Create an object:
data(sheepData)
mySurvey <- surveyData(nAnimalVec = sheepData$nSheep,
populationData = sheepData, designPrevalence = 0.002,
alpha = 0.05, intraHerdPrevalence = 0.13,
diagSensitivity = 0.9, costHerd = 30, costAnimal = 7.1)
myLtdSampling <- ltdSampling(survey.Data = mySurvey, sampleSizeLtd = 7)
## Display results:
summary(myLtdSampling)
## Write results to an html-file:
## Not run:
target <- HTMLInitFile(getwd(), filename = "LtdSampling")
HTML(myLtdSampling)
HTMLEndFile()
## End(Not run)
``` |

FFD documentation built on May 31, 2017, 3:33 a.m.

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