clinDR (version 2.4.1)

"Extract.emaxsimB": Extract a simulation from the output of emaxsimB

Description

Extract a simulated data set from the output of emaxsimB. Data are re-created using the stored random number seed.

Usage

# S3 method for emaxsimB
[(x, i, ...)

Value

A list is returned with class(emaxsimBobj) containing:

y

Response vector

dose

Doses corresponding to y

pop

Population parameters; type of parameter depends on constructor function generating study data.

popSD

Vector containing the population SD used to generate continuous data. NULL for binary data.

binary

When TRUE, binary data modeled on the logit scale

modType

modType=3, 4, for the hyperbolic and sigmoidal Emax models.

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 fitEmaxB.

prior, mcmc

See fitEmax for documentation.

pVal, selContrast

P-value and contrast selected from MCP-MOD test

idmax

Index of default dose group for comparison to placebo

Arguments

x

Output object from emaxsimB

i

Simulation replication to extract

...

Parameters passed to other functions (none currently)

Author

Neal Thomas

Details

Re-creates the ith simulated data set for subsequent analyses. Also returns all analyses done for the ith data set in emaxsimB

See Also

emaxsimB, print.emaxsimBobj, plot.emaxsimBobj

Examples

if (FALSE) {

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
}