bdrm: Bayesian parametric dose-response modelling

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Bayesian parametric modelling of dose-response data.

Usage

1
2
3
4
5
6
7
8
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:

Value

An object of class "drm".

Author(s)

[email protected]

References

See Also

drm, for frequentist dose-response modelling

Examples

1
2
3
4
## 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.