bea_presence: Bayesian exposure assessment from presence/absence data

In brechtdv/QMRA: Parametric Models for Quantitative Microbial Risk Assessment

Description

Bayesian parametric modelling of exposure concentration from presence/absence data.

Usage

bea_presence(x, q = 1, replicates = rep(1, length(x)), data,
             model = c("poisson", "p"),
             inits = NULL, nchains = 2, burnin = 5000, update = 5000,
             verbose = FALSE)

Arguments

x	A vector indicating the number of positive samples per examined quantity.
q	The quantities (e.g., volumes, masses) in which presence/absence was observed; defaults to 1.
replicates	The number of replicates of each quantity q; defaults to rep(1, length(x)).
data	An optional data frame, containing the variables in the model. If not found in data, the variables are taken from the environment from which ea_presence is called.
model	A character string naming the model to be fitted; currently only "poisson" is supported. See details below.
inits	Named list of initial values; defaults to NULL, leading JAGS to generate inits automatically (see jags.model).
nchains	Number of model chains, should be ≥ 2; defaults to 2.
burnin	Number of samples to discard as burn-in; defaults to 5000.
update	Number of samples to retain; defaults to 5000.
verbose	Should JAGS process info be printed to the R console? defaults to FALSE.

Details

Available distributions:

• Poisson: model = "poisson" or "p"

Value

An object of class "bea".

Author(s)

[e-mail]

References

• Haas CN, Rose JB, Gerba CP (1999) Quantitative Microbial Risk Assessment. John Wiley & Sons, Inc.

See Also

bea_count, for modelling exposure from count data
bea_conc, for modelling exposure from concentration data

Examples

## Bayesian exposure assessment from presence/absence data
bea_presence(x = positive, q = volume, rep = replicates, data = coliform)

brechtdv/QMRA documentation built on May 13, 2019, 5:06 a.m.