simData2s.Rd
Generates two samples of (zero-inflated) egg count data
simData2s(n = 10, preMean = 500, delta = 0.1, kappa = 0.5, deltaShape = NULL, phiPre = 1, phiPost = phiPre, f = 50, paired = TRUE, rounding = TRUE, seed = NULL)
n | positive integer. Sample size. |
---|---|
preMean | numeric. True pre-treatment epg. |
delta | numeric. Proportion of epg left after treatment, between 0 and 1. 1 - \(\delta\) is reduction in mean after treatment, |
kappa | numeric. Overdispersion parameter, \(\kappa \to \infty\) corresponds to Poisson distribution. |
deltaShape | numeric. Shape parameter for the distribution of reductions. If NULL, the same reduction is applied to the latent true epg of each animal. |
phiPre | numeric. Pre-treatment prevalence (i.e. proportion of infected animals), between 0 and 1. |
phiPost | numeric. Post-treatment prevalence, between 0 and 1. |
f | integer or vector of integers. Correction factor of the egg counting technique |
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, see Details. |
seed | an integer that will be used in a call to set.seed before simulation. If NULL, a random seed is allocated. |
A data.frame with six columns, namely the observed epg (obs
),
actual number of eggs counted (master
) and true epg in the sample (true
) for both pre- and post- treatment.
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 some bias in the mean reduction when \(\mu\) < 150 and \(\delta\) < 0.1. Set rounding = FALSE
for not to have any bias.
fecr_stan
for analyzing faecal egg count data with two samples
fec <- simData2s(n = 10, preMean = 500, delta = 0.1, kappa = 0.5) ## show the bias when the true reduction should be 95% fec <- simData2s(n = 1e5, preMean = 150, delta = 0.05, kappa = 0.5, seed = 1) 1 - mean(fec$masterPost)/mean(fec$masterPre)#> [1] 0.9760579## without bias fec <- simData2s(n = 1e5, preMean = 150, delta = 0.05, kappa = 0.5, seed = 1, rounding = FALSE) 1 - mean(fec$masterPost)/mean(fec$masterPre)#> [1] 0.9498498