Generates two samples of (zero-inflated) egg count data
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n |
sample size (number of faecal samples collected pre- and post-treatment) |
preMean |
true number of eggs per gram (epg) (i.e. worm burden) before treatment |
delta |
proportion of epg left after treatment, between 0 and 1. 1 - δ is reduction in mean after treatment, |
kappa |
overdispersion parameter, κ -> ∞ corresponds to Poisson |
phiPre |
pre-treatment prevalence (i.e. proportion of infected animals), between 0 and 1 |
phiPost |
post-treatment prevalence, between 0 and 1 |
f |
correction factor of the egg counting technique, either an integer or a vector of integers with length |
paired |
logical. If true, paired samples are simulated. Otherwise unpaired samples are simulated. |
rounding |
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. |
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
,there expects to be a up to 3-10% positive bias in the mean reduction when μ < 150 and δ < 0.1. Set rounding = FALSE
if one does not wish to have any bias.
A matrix with six columns, namely the observed epg (obs
),
number of eggs counted on microscope slide (master
) and true egg counts (true
) for both pre- and post- treatment.
Michaela Paul michaela.paul@uzh.ch
Craig Wang craig.wang@uzh.ch
fecr_stan
for analyzing faecal egg count data with two samples
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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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