Description Usage Arguments Details Value Author(s) References Examples
Average model estimates according to Akaike's Information Criterion.
1 2 
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Possibly named objects of class 
dose 
Doses for which modelaveraged responses need to be obtained. 
Starting from a set of K plausible candidate models, the averaged model estimate \hat{θ} is defined as ∑_{i=1}^{K}{w_i θ_i}. The weights are defined as
w_i = \frac{exp(0.5 Δ_i)}{∑_{j=1}^{K}{exp(0.5 Δ_j)}}
with Δ_i = AIC_i  AIC_{min}
The variance of the averaged model estimate is given by
var(\hat{θ}) = [∑_{i=1}^{K}{w_i √{var(θ_i) + (θ_i  \hat{θ})^2}}]^2
An object of S3 class "avg"
.
Haas CN, Rose JB, Gerba CP (1999) Quantitative Microbial Risk Assessment. John Wiley & Sons, Inc.
Burnham KP, Anderson DR (2002) Model Selection and Multimodel Inference. SpringerVerlag New York, Inc.
Namata H, Aerts M, Faes C, Teunis P (2008). Model averaging in microbial risk assessment using fractional polynomials. Risk analysis, 28(4), 891905.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  ## Exposure assessment from concentration data
gam < ea_conc(x = x, d = d, data = giardia, model = "gamma")
lno < ea_conc(x = x, d = d, data = giardia, model = "lognormal")
wei < ea_conc(x = x, d = d, data = giardia, model = "weibull")
inv < ea_conc(x = x, d = d, data = giardia, model = "invgauss")
## Model averaging
avg_ea("Gamma" = gam,
"LogNormal" = lno,
"Weibull" = wei,
"Inverse Gaussian" = inv)
## Fit several doseresponse models to the Campylobacter dataset
bp < drm(infected, total, dose, campy, "betapoisson")
ll < drm(infected, total, dose, campy, "loglogistic")
lp < drm(infected, total, dose, campy, "logprobit")
ev < drm(infected, total, dose, campy, "extremevalue")
## Model averaging
avg_drm("bp" = bp, "ll" = ll, "lp" = lp, "ev" = ev,
dose = c(1, 10, 100))

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