Description Usage Arguments Details Value Author(s) References Examples
Average model estimates according to Akaike's Information Criterion.
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Possibly named objects of class |
dose |
Doses for which model-averaged 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"
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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. Springer-Verlag 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), 891-905.
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,
"Log-Normal" = lno,
"Weibull" = wei,
"Inverse Gaussian" = inv)
## Fit several dose-response 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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