Description Usage Arguments Value
View source: R/modelAveraging.R
Estimate the model-averaged BMD using numeric optimization techniques
1 | optimizeBmd(weights, modelResults, nIterations = 400, naiveApproach = FALSE)
|
weights |
numeric vector, estimated weights as returned by calculateWeights() |
modelResults |
list, with results for each model, same length as weights. For each model a list with at least npar, loglik, x, y, model.ans, regr.par, CES and ces.ans; these are by default included in result from f.proast(). Eventually contains also fct1 and fct2 if factors are included for the model parameters. |
nIterations |
integer, number of iterations for numeric optimization; default value is 400 |
naiveApproach |
boolean, TRUE if the model-averaged BMD is estimated as the weighted average of bmd values, FALSE if the model-averaged BMD is estimated based on weighted average of response values; default value is FALSE |
numeric, the model-averaged BMD; error is returned if the numeric
procedure did not converge after nIterations
iterations
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