HierPoolPrev: Estimation of prevalence based on presence/absence tests on...

In PoolTestR: Prevalence and Regression for Pool-Tested (Group-Tested) Data

Estimation of prevalence based on presence/absence tests on pooled samples in a hierarchical sampling frame

Description

Estimation of prevalence based on presence/absence tests on pooled samples in a hierarchical sampling frame

Usage

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)
)

Arguments

data	A data.frame with one row for each pooled sampled and columns for the size of the pool (i.e. the number of specimens / isolates / insects pooled to make that particular pool), the result of the test of the pool. It may also contain additional columns with additional information (e.g. location where pool was taken) which can optionally be used for splitting the data into smaller groups and calculating prevalence by group (e.g. calculating prevalence for each location)
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 lower-level 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. A-1, A-2... for sites in village A and B-1, B-2... 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 prior.alpha = prior.beta = 1/2. Another popular uninformative choice is prior.alpha = prior.beta = 1, i.e. a uniform prior. prior.absent is included for consistency with PoolPrev, but is currently ignored
hyper.prior.sd	Scale for the half-Cauchy hyper-prior 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 adapt_delta which is set to the more conservative value of 0.9. See stan for details.

Value

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.

See Also

PoolPrev, getPrevalence

Examples

# 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)

PoolTestR documentation built on July 1, 2022, 9:06 a.m.