optimizeBmd: Estimate the model-averaged BMD using numeric optimization...

In alfcrisci/bmdModeling: Benchmark dose modeling

Description

Estimate the model-averaged BMD using numeric optimization techniques

Usage

optimizeBmd(weights, modelResults, nIterations = 400, naiveApproach = FALSE)

Arguments

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

Value

numeric, the model-averaged BMD; error is returned if the numeric procedure did not converge after nIterations iterations

alfcrisci/bmdModeling documentation built on May 28, 2019, 12:32 a.m.