R/findOptimalApproxDesign.R

In sabush/designGLMM: Finding Optimal Block Designs for a Generalised Linear Mixed Model

# Construct the optimal continuous one factor completely randomised design - No overdispersion, A Optimality Only

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#' @export
findOptimalApproxDesign<-function(means,silent=FALSE){

  # Find the betas
  b0<-mean(log(means))
  bi<-log(means) - b0

  # Calculate the optimal weights based on Poisson Regression model

  sumbi<- sum(bi)/2
  adjbi<-exp(sumbi - bi/2)
  weights<-adjbi/sum(adjbi)

  weights.table<-as.matrix(treatment=1:length(means),weights)
  # Print output if silent is FALSE
  if(silent==FALSE){
    cat("\n\nThe optimal design weights are:\n")
    print(weights.table)
#     cat(paste("The determinant of the information matrix is: ",round(current,digits=5),"\n",sep=""))
#     cat(paste("Progressvec is: ",progressvec,"\n",sep=""))
  }
  list("design"=weights)
}