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

 hse_finite R Documentation

## 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.