HierPoolPrev  R Documentation 
Estimation of prevalence based on presence/absence tests on pooled samples in a hierarchical sampling frame
HierPoolPrev( data, result, poolSize, hierarchy, ..., prior.alpha = 0.5, prior.beta = 0.5, prior.absent = 0, hyper.prior.sd = 2, level = 0.95, 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 
hierarchy 
The name of column(s) indicating the group membership. In a nested sampling design with multiple levels of grouping the lowerlevel groups must have names/numbers that differentiate them from all other groups at the same level. E.g. If sampling was performed at 200 sites across 10 villages (20 site per village), then there should be 200 unique names for the sites. If, for instance, the sites are instead numbered 1 to 20 within each village, the village identifier (e.g. A, B, C...) should be combined with the site number to create unique identifiers for each site (e.g. A1, A2... for sites in village A and B1, B2... for the sites in village B etc.) 
... 
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 prior on the prevalence in
each group takes the form of beta distribution (with parameters alpha and
beta). The default is 
hyper.prior.sd 
Scale for the halfCauchy hyperprior for standard deviations of random/group effect terms. Defaults to 2, which is weakly informative since it implies that 50% of random/group effects terms will be within a order of magnitude of each other, and 90% of random/group effects will be within four orders of magnitude of each other. Decrease if you think group differences are are smaller than this, and increase if you think group differences may often reasonably be larger than this 
level 
The confidence level to be used for the confidence and credible intervals. Defaults to 0.95 (i.e. 95% intervals) 
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:
PrevBayes
the (Bayesian) posterior expectation
CrILow
and CrIHigh
– lower and upper bounds
for credible intervals
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.
PoolPrev
,
getPrevalence
# Calculate prevalence for a synthetic dataset consisting of pools (sizes 1, 5, # or 10) taken from 4 different regions and 3 different years. Within each # region specimens are collected at 4 different villages, and within each # village specimens are collected at 8 different sites. #Prevalence for each combination of region and year: #ignoring hierarchical sampling frame within each region PoolPrev(SimpleExampleData, Result, NumInPool, Region, Year) #accounting hierarchical sampling frame within each region HierPoolPrev(SimpleExampleData, Result, NumInPool, c("Village","Site"), Region, Year)
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