bdrm: Bayesian parametric dose-response modelling

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

Description

Bayesian parametric modelling of dose-response data.

Usage

bdrm(x, n, dose, data,
     model = c("betapoisson", "bp", 
               "exponential", "exp", 
               "loglogistic", "ll",
               "logprobit", "lp",
               "extremevalue", "ev"),
     inits = NULL, nchains = 2, burnin = 10000, update = 10000,
     verbose = FALSE)

Arguments

x	Vector of infected individuals.
n	Vector of exposed individuals.
dose	Vector of administered doses.
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 bdrm is called.
model	A character string naming the dose-response 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 10000.
update	Number of samples to retain; defaults to 10000.
verbose	Should JAGS process info be printed to the R console? defaults to FALSE.

Details

Available models:

• Beta-Poisson: model = "betapoisson" or "bp"

• Exponential: model = "exponential" or "exp"

• Log-Logistic: model = "loglogistic" or "ll"

• Log-Probit: model = "logprobit" or "lp"

• Extreme Value: model = "extremevalue" or "ev"

Value

An object of class "drm".

Author(s)

[e-mail]

References

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

See Also

drm, for frequentist dose-response modelling

Examples

## Fit Beta-Poisson dose-response model to Campylobacter dataset
bdrm(x = infected, n = total, dose = dose,
     data = campy, model = "bp",
     inits = list(alpha = 1, beta = 1))

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