lqas_simulate_population | R Documentation |
Simulate survey data of covered/cases and non-covered/non-cases given a coverage/prevalence proportion
Perform LQAS on simulated data based on specified decision rules
lqas_simulate_population(proportion, pop)
lqas_simulate_run(proportion, pop, n, dLower, dUpper)
lqas_simulate_runs(
pop,
n,
dLower,
dUpper,
pLower = 0,
pUpper = 1,
fine = 0.01,
runs = 50,
cores = parallelly::availableCores(omit = 1)
)
lqas_simulate_test(
pop,
n,
dLower,
dUpper,
pLower = 0,
pUpper = 1,
fine = 0.01,
runs = 50,
replicates = 20,
cores = parallelly::availableCores(omit = 1)
)
proportion |
A numeric value of a coverage/prevalence proportion to simulate on. Values should be between 0 and 1. |
pop |
Population size from which simulated coverage survey data is to be taken from. |
n |
Sample size of actual or test coverage data. |
dLower |
A numeric value for the lower classification threshold proportion. Value should be between 0 and 1. |
dUpper |
A numeric value for the upper classification threshold proportion. Value should be between 0 and 1. |
pLower |
Starting proportion for simulations. Default is 0. |
pUpper |
Ending proportion for simulations. Default is 1. |
fine |
Granularity of simulated proportions. Default is 0.01. |
runs |
Number of simulation runs to perform per coverage proportion. Default is 50 runs. |
cores |
The number of computer cores to use/number of child processes will be run simultaneously. |
replicates |
Number of replicate LQAS simulations to perform. Default is set to 20 replicates. |
A data.frame with 2 variables: id
for unique identifier and case
for numeric vector of cases and non-cases (1s and 0s)
A data.frame with variable cases
for total number of
covered/cases, outcome
for LQAS outcome, and proportion
for the
coverage/prevalence proportion being simulated on. For
[lqas_simulate_test()]
, a 'sleac“ class object.
lqas_simulate_population(proportion = 0.3, pop = 10000)
lqas_simulate_run(
proportion = 0.3, pop = 10000, n = 40, dLower = 0.6, dUpper = 0.9
)
lqas_simulate_runs(
pop = 10000, n = 40, dLower = 0.6, dUpper = 0.9, runs = 10
)
lqas_simulate_test(
pop = 10000, n = 40, dLower = 0.6, dUpper = 0.9, runs = 5, replicates = 5
)
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