Computes the optimal sample size for a survey to substantiate freedom from disease. The optimal sample size is the smallest sample size that produces an alpha-error less than or equal to a prediscribed value alpha. The population is considered as diseased if at least one individual has a positive test result. The sample size is computed using a bisection method. The function either computes the sample size for a fixed population (lookupTable == FALSE) or a lookup table with sampling sizes depending on the population size for individual sampling (lookupTable == TRUE); see Ziller et al., 2002.
Function works like ls() but it returns the names of the objects in the workspace, together with class, dimension and size in the form of a data frame.
Computes the optimal sample size (for each risk group) for a survey to substantiate freedom from disease for a population stratified into risk groups. The optimal sample size is the smallest sample size that produces an alpha-error less than or equal to a prediscribed value for alpha. The population is considered as diseased if at least one individual has a positive test result. The sample size is computed using a bisection method. The sample size can be fixed for a subset of the risk groups via the input parameter 'nSampleFixVec' (vector containing sample sizes for the risk groups with fixed values and NA for the risk groups for which the sample size is to be computed). For those risk groups for which the sample size is to be computed a vector specifying the proportional distribution among the risk groups ('nSamplePropVec') needs to be specified.
For sampling strategy "limited sampling" (see Ziller et al., 2002) the function computes the herd-level alpha-errors (= 1-herd sensitivity) for each stock size, as well as the average herd-level alpha-error.
For a sampling scheme designed to substantiate freedom from disease the function computes the a-posteriori alpha-error, i.e., the actual alpha-error based on the drawn sample.
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.
Contains the parameters and the data necessery for a survey to substantiate freedom from disease using "individual sampling" or "limited sampling". The parameters are: design prevalence (=prevalence of the disease under the null hypothesis), overall significance level (=1-confidence), intra-herd prevalence, sensitivity of the diagnostic test, as well as cost per tested animal and cost per tested herd.
Contains the parameters and the data necessery 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 for a range of possible sample limits (= fixed number of animals to test per herd).
For a population that is stratified into risk groups the function computes the probability of finding no testpositives in a sample of given size using an imperfect diagnostic test. For each of the risk groups the population size nPopulationVec, the sample size nSampleVec and the relative infection risk nRelRiskVec must be specified. The discussed probability corresponds to the alpha-error (=error of the first kind) of the overall test with null hypothesis: prevalence = design prevalence.
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