Description Usage Arguments Details Value Examples
Herd Sensitivity calculated with the assumption of a finite population
1 | hse_finite(id, n_tested, N, test_Se, dp)
|
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. |
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
A data.frame. A dataframe is returned with 2 columns: "id" and HSe
1 2 3 4 5 6 7 8 9 10 11 | 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)
|
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