Summary of output of emaxsimB
Detailed summary of repeated sampling properties of Bayesian Emax estimation and comparison with simple pairwise comparisons.
## S3 method for class 'emaxsimB'
summary(object, testalpha = 0.05,
clev = c('0.9','0.95','0.8'),
seSim = FALSE, ...)object |
Output of |
testalpha |
Alpha level for a one-sided MCP-MOD trend test. |
clev |
Posterior proabilities for reported intervals |
seSim |
If |
... |
Other unspecified parameters (none currently utilized) |
For pairwise comparisons, the 'most favorable pairwise comparison' means the dose with the best difference versus placebo is compared to the population mean response for the selected dose, thus the target value for coverage, bias, and RMSE changes depending on the selected dose.
The function produces annotated output summarizing the properties of the estimation procedures. The summaries are also returned as an invisible list for extracting results.
Neal Thomas
## Not run: ## emaxsimB changes the random number seed nsim<-50 idmax<-5 doselev<-c(0,5,25,50,100) n<-c(78,81,81,81,77) Ndose<-length(doselev) ### population parameters for simulation e0<-2.465375 ed50<-67.481113 dtarget<-100 diftarget<-2.464592 emax<-solveEmax(diftarget,dtarget,log(ed50),1,e0) sdy<-7.967897 pop<-c(log(ed50),emax,e0) meanlev<-emaxfun(doselev,pop) ###FixedMean is specialized constructor function for emaxsim gen<-FixedMean(n,doselev,meanlev,sdy) prior<-emaxPrior.control(epmu=0,epsca=30,difTargetmu=0, difTargetsca=30,dTarget=100,p50=50,sigmalow=0.1, sigmaup=30,parmDF=5) mcmc<-mcmc.control(chains=1,warmup=500,iter=5000,seed=53453,propInit=0.15,adapt_delta = 0.95) D1 <- emaxsimB(nsim,gen, prior, modType=3,mcmc=mcmc,check=FALSE) summary(D1,testalph=0.05,clev='0.95') ## End(Not run)
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