eggCounts (version 2.4)

simData2s: Simulate faecal egg count data (2-sample situation)

Description

Generates two samples of (zero-inflated) egg count data

Usage

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)

Value

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.

Arguments

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, delta = 0.1 indicates a 90% reduction.

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.

Author

Craig Wang
Michaela Paul

Details

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.

See Also

fecr_stan for analyzing faecal egg count data with two samples

Examples

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)
## 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)