Extract a simulation from the output of emaxsimB
Extract a simulated data set from the output of emaxsimB. Data are re-created using the stored random number seed.
## S3 method for class 'emaxsimB' x[i, ...]
x |
Output object from |
i |
Simulation replication to extract |
... |
Parameters passed to other functions (none currently) |
Re-creates the ith simulated data set for subsequent analyses. Also returns all
analyses done for the ith data set in emaxsimB
A list is returned with class(emaxsimBobj) containing:
y |
Response vector |
dose |
Doses corresponding to |
pop |
Population parameters; type of parameter depends on constructor function generating study data. |
popSD |
Vector containing the population SD used to generate
continuous data. |
binary |
When |
modType |
|
predpop |
Population means for each dose group |
dm |
Vector containing dose group means |
dsd |
Vector containing dose group SDs |
fitpred |
Posterior means of the dose groups means |
sepred |
SE (posterior SD) corresponding to the estmates in fitpred |
sedif |
SE (posterior SD) for the differences with placebo |
bfit |
Bayesian fitted model of class |
prior, mcmc |
See |
pVal, selContrast |
P-value and contrast selected from MCP-MOD test |
idmax |
Index of default dose group for comparison to placebo |
Extraction from a simulation object requires re-creation of the simulated data set. If the extracted object is to be used more than once, it is more efficient to save the extracted object than reuse [].
Neal Thomas
## Not run: save.seed<-.Random.seed set.seed(12357) 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) out<-D1[2] .Random.seed<-save.seed ## End(Not run)
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