Simulates (zero-inflated) egg count data
simData1s(n = 10, mean = 500, kappa = 0.5, phi = 1, f = 50, rounding = TRUE)sample size (number of faeces collected)
true number of eggs per gram (epg)
overdispersion parameter, \(\kappa \to \infty\) corresponds to Poisson
prevalence i.e. proportion of infected animals, between 0 and 1
correction factor of the egg counting technique, either an integer or a vector of integers with length n
logical. If true, the Poisson mean for the raw counts is rounded. The rounding applies since the mean epg is frequently reported as an integer value. For more information, please see Details.
A matrix with three columns, namely the observed epg (obs),
number of eggs counted on the McMaster slide (master) and
true egg counts (true).
The simulation process does not exactly match the proposed models in [ref:paper], however the simulated data resembles the data observed in real world.
In the simulation of raw (master) counts, it follows a Poisson distribution with some mean. The mean is frequently rounded down if it has a very low value and rounding = TRUE, hence there expects to be a negative bias overall when \(\mu\) < 150. Set rounding = FALSE if one does not wish to have any bias in the simulated counts.
fec_stan for analyzing faecal egg count data with one sample
# NOT RUN {
fec <- simData1s(n=10, mean=500, kappa=0.5, phi=0.7)
# }
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