hse_finite: hse_finite

In SVA-SE/freedom: Demonstration of Disease Freedom (DDF)

hse_finite

Description

Herd Sensitivity calculated with the assumption of a finite population

Usage

hse_finite(
  id,
  n_tested,
  N,
  test_Se,
  dp,
  rounding = c("none", "ceiling", "round", "floor")
)

Arguments

id	The herdid.
n_tested	The number tested in each URG
N	The number of units in each of the URG
test_Se	The sensitivity of the test. This may have length == 1 if all URG and all herds have the same test_Se. It may also have length(test_Se) == length(n_tested).
dp	The design prevalence (dp) could be length(dp) == 1 if all URG and herds have the same dp. It could alternatively be length(dp) == length(n_tested) if different design prevalences are to be applied to each URG.
rounding	How should the proportion of animals be rounded? Default value is 'none' which does no rounding. Other options are 'round', 'ceiling', and 'floor'. 'round' rounds the dp * N to the nearest integer and then selects 1 if the value is 0. 'ceiling' takes the ceiling of dp * N, this is consistent with the method in the Rsurveillance package. 'floor' takes the floor of dp * N and makes it 1 if the result is 0.

Details

Calculate the Herd sensitivity when multiple samples from individual units within the herd. The function uses the total population size to adjust the estimates consistent with a finite population. This method for calculation of HSe is typically used when greater than 10

Value

A data.frame. A dataframe is returned with 2 columns: "id" and HSe

Examples

df <- data.frame(id = seq(1:20),
                 n_tested = rpois(20, 5),
                 N = 100,
                 test_Se = 0.3,
                 dp = 0.05)
## Calculate the herd level sensitivity for each of these herds
hse_finite(df$id,
           df$n_tested,
           df$N,
           df$test_Se,
           df$dp)

SVA-SE/freedom documentation built on Feb. 1, 2023, 5:50 p.m.