drc (version 1.9-0)

maED: Estimation of ED values using model-averaging

Description

Estimates and confidence intervals for ED values are estimated using model-averaging.

Usage

maED(object, fctList = NULL, respLev, interval = c("none", "buckland", "kang"), 
level = 0.95, display = TRUE, na.rm = FALSE, extended = FALSE)

Arguments

object

an object of class 'drc'.

fctList

a list of non-linear functions to be compared.

respLev

a numeric vector containing the response levels.

interval

character string specifying the type of confidence intervals to be supplied. The default is "none". The choices "buckland" and "kang" are explained in the Details section.

level

numeric. The level for the confidence intervals. The default is 0.95.

display

logical. If TRUE results are displayed. Otherwise they are not (useful in simulations).

na.rm

logical indicating whether or not NA occurring during model fitting should be left out of subsequent calculations.

extended

logical specifying whether or not an extended output (including fit summaries) should be returned.

Value

• A matrix with two or more columns, containing the estimates and the corresponding estimated standard errors and possibly lower and upper confidence limits.

Details

Model-averaging of individual estimates is carried out as described by Buckland et al. (1997) and Kang et al. (2000) using AIC-based weights. The two approaches differ w.r.t. the calculation of confidence intervals: Buckland et al. (1997) provide an approximate variance formula under the assumption of perfectly correlated estimates (so, confidence intervals will tend to be too wide), whereas Kang et al. (2000) use the model weights to calculate confidence limits as weighted means of the confidence limits for the individual fits.

References

Buckland, S. T. and Burnham, K. P. and Augustin, N. H. (1997) Model Selection: An Integral Part of Inference Biometrics 53, 603--618. Kang, Seung-Ho and Kodell, Ralph L. and Chen, James J. (2000) Incorporating Model Uncertainties along with Data Uncertainties in Microbial Risk Assessment, Regulatory Toxicology and Pharmacology 32, 68--72.

See Also

The function mselect provides a summary of fit statistics for several models fitted to the same data.

Examples

ryegrass.m1 <- drm(rootl~conc, data = ryegrass, fct = LL.4())
  
mselect(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)))
  
maED(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)), 
c(10, 50, 90))  
  
maED(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)), 
c(10, 50, 90), "buckland")

maED(ryegrass.m1, list(LL.5(), LN.4(), W1.4(), W2.4(), FPL.4(-1,1), FPL.4(-2,3), FPL.4(-0.5,0.5)), 
c(10, 50, 90), "kang")