eggSim | R Documentation |
Simulate and summarise egg reduction rate data
eggSim(
reduction,
budget = 600,
second_slide_cost = 0.621,
max_screen = 0.9,
community_mean = c(24, 48),
cv_between = c(1.5),
cv_within = 0.75,
cv_slide = 0.25,
cv_reduction = 0,
true_prevalence = 1,
count = TRUE,
log_constant = if (count) 1 else 0,
screen_threshold = 0,
grams = 1/24,
R = 10^3,
design = c("NS", "NS2", "NS3", "SS", "SSR1", "SSR2", "SSR3"),
summarise = TRUE,
type = "gamma",
parallelise = TRUE,
max_vec = 5e+06
)
reduction |
The arithmetic mean reduction (may be vectorised) |
budget |
The total budget to use for each design |
second_slide_cost |
The cost of a second examination (e.g. Kato-Katz slide) from the same faecal sample, relative to the cost of an entirely independent sample |
max_screen |
The maximum proportion of the budget to use on screening |
community_mean |
A vector of arithmetic mean pre-treatment EPG in each community |
cv_between |
A vector of CV reflecting variation in EPG between individuals in each community |
cv_within |
Day-to-day variation in EPG within an individual |
cv_slide |
Variation between faecal samples from the same individual and day |
cv_reduction |
Variation in efficacy between individuals |
true_prevalence |
The true prevalence of infected individuals, i.e. one minus the zero inflation at individual level |
count |
Logical flag to base means on count data or the underlying rates |
log_constant |
A constant to add to the count data before calculating geometric means (ignored if count==FALSE) |
screen_threshold |
The threshold count on which to screen individuals |
grams |
The volume of faeces actually examined for eggs (1/24 grams is standard for Kato-Katz) |
R |
The number of replicate datasets |
design |
The survey design(s) to be examined |
summarise |
Should the simulation results be summarised? |
type |
One of gamma or lognormal - at present only the former is suppoered |
parallelise |
Option to parallelise simulations (can also be an integer giving the number of cores to use, or a cluster pre-created using the parallal package) |
max_vec |
The maximum number of data frame rows to use for vectorisation (affects memory usage) |
A data frame containing the simulated data
means <- eggSim(c(0.2,0.1), cv_reduction=0, R=10^2, design = c('NS','SS'), parallelise=FALSE)
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