ea_count: Exposure assessment from count data

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

Description

Parametric modelling of exposure concentration from count data.

Usage

ea_count(x, q = 1, data,
         model = c("poisson", "p",
                   "negbin", "nb",
                   "poislognorm", "pln",
                   "poisinvgauss", "pig",
                   "poisgeninvgauss", "pgig"),
         ...)

Arguments

x	The observed counts.
q	The quantities (e.g., volumes, masses) in which the counts were observed; defaults to 1.
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_count is called.
model	A character string naming the count model. See details below.
...	Arguments to be passed to mle

Details

Distributions available:

• Poisson: model = "poisson" or "p"

• Negative Binomial: model = "negbin" or "nb"

• Poisson-LogNormal: model = "poislognorm" or "pln"

• Poisson-Inverse Gaussian: model = "poisinvgauss" or "pig"

• Poisson-Generalized Inverse Gaussian: model = "poisgeninvgauss" or "pgig"

Value

An object of class "ea".

Author(s)

[e-mail]

References

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

See Also

ea_presence, for modelling exposure from presence/absence data
ea_conc, for modelling exposure from concentration data

Examples

ea_count(x = counts, q = volume, model = "negbin", data = crypto)

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