PoolPrev  R Documentation 
Estimation of prevalence based on presence/absence tests on pooled samples
PoolPrev( data, result, poolSize, ..., prior.alpha = NULL, prior.beta = NULL, prior.absent = 0, level = 0.95, reproduce.poolscreen = FALSE, verbose = FALSE, cores = NULL, iter = 2000, warmup = iter/2, chains = 4, control = list(adapt_delta = 0.9) )
data 
A 
result 
The name of column with the result of each test on each pooled sample. The result must be stored with 1 indicating a positive test result and 0 indicating a negative test result. 
poolSize 
The name of the column with number of specimens/isolates/insects in each pool 
... 
Optional name(s) of columns with variables to stratify the data by. If omitted the complete dataset is used to estimate a single prevalence. If included, prevalence is estimated separately for each group defined by these columns 
prior.alpha, prior.beta, prior.absent 
The default prior for the
prevalence is the uninformative Jeffrey's prior, however you can also
specify a custom prior with a beta distribution (with parameters
prior.alpha and prior.beta) modified to have a point mass of zero i.e.
allowing for some prior probability that the true prevalence is exactly
zero (prior.absent). Another popular uninformative choice is

level 
Defines the confidence level to be used for the confidence and credible intervals. Defaults to 0.95 (i.e. 95% intervals) 
reproduce.poolscreen 
(defaults to FALSE). If TRUE this changes the way that likelihood ratio confidence intervals are computed to be somewhat wider and more closely match those returned by Poolscreen. We recommend using the default (FALSE). However setting to TRUE can help to make comparisons between PoolPrev and Poolscreen. 
verbose 
Logical indicating whether to print progress to screen. Defaults to false (no printing to screen). 
cores 
The number of CPU cores to be used. By default one core is used 
iter, warmup, chains 
MCMC options for passing onto the sampling routine. See stan for details. 
control 
A named list of parameters to control the sampler's behaviour.
Defaults to default values as defined in stan, except for

A data.frame
with columns:
PrevMLE
(the Maximum Likelihood Estimate of prevalence)
CILow
and CIHigh
 lower and upper confidence
intervals using the likelihood ratio method
PrevBayes
the (Bayesian) posterior expectation
CrILow
and CrIHigh
– lower and upper bounds
for credible intervals
ProbAbsent
the posterior probability that prevalence
is exactly 0 (i.e. disease marker is absent). NA if using
default Jeffrey's prior or if prior.absent = 0.
NumberOfPools
– number of pools
NumberPositive
– the number of positive pools
If grouping variables are provided in ...
there will be an additional
column for each grouping variable. When there are no grouping variables
(supplied in ...
) then the output has only one row with the
prevalence estimates for the whole dataset. When grouping variables are
supplied, then there is a separate row for each group.
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