bea_conc: Bayesian exposure assessment from concentration data

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

Description

Bayesian parametric modelling of exposure concentration from (possibly left-censored) concentration data.

Usage

bea_conc(x, d, data,
         model = c("gamma", "g",
                   "lognorm", "ln",
                   "weibull", "w",
                   "invgauss", "ig"),
         inits = NULL, nchains = 2, burnin = 5000, update = 5000,
         verbose = FALSE)

Arguments

x	The observed concentrations.
d	A vector indicating if the observations were left-censored (1) or not (0).
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 bea_conc is called.
model	A character string naming the concentration model. 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

Distributions available:

• Gamma: model = "gamma" or "g"

• Log-Normal: model = "lognorm" or "ln"

• Weibull: model = "weibull" or "w"

• Inverse Gaussian: model = "invgauss" or "ig"

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_presence, for modelling exposure from presence/absence data

Examples

bea_conc(x = x, d = d, data = giardia, model = "weibull")

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