1d:T75c,# Print simulation output from emaxsimB Prints key summary variables of Emax estimation peformance for each simulation. Can be used to identify simulated data sets yielding unusual estimates. ```r ## S3 method for class 'emaxsimB' print(x, nprint = min(nsim, 20), id = x$idmax, digits = 3, ...) ``` ## Arguments - `x`: Output of `emaxsimB` - `nprint`: Number of simulations to print. If a vector of length 2, `nprint` is the range of simulations to print. - `id`: Output includes the stdBias for the dose with index `id` vs placebo - `digits`: Number of decimal digits to print for Z and p-values - ``...``: Other print parameters (none currently implemented) ## Returns Printed output returned as invisible matrix. ## Author(s) Neal Thomas ## Note The stdBias printed is the difference between the posterior mean of the dose response at the dose with index `id` and its population value. The difference is divided by the SE (posterior SD). ## See Also `emaxsimB`, `summary.emaxsimB`, `plot.emaxsimB` ## Examples ```r ## 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) print(D1) ## End(Not run) ```

print.emaxsimB function