plot.emaxsimBobj {clinDR}

Plot dose response from a data set generated by emaxsimB

Description

Plot of population dose response curve, sample dose group means, posterior and posterior predictive intervals, and the model-based estimated (posterior means) dose response curve.

Usage

## S3 method for class 'emaxsimBobj'
plot(
	x, clev=0.9, plotDif=FALSE, 
	plotPop=c('m','3','4'), 
	logScale=FALSE, plotResid=FALSE, 
	plot=TRUE, ... )

Arguments

x

Extracted data object from emaxsimB

clev

Level for posterior intervals

plotDif

When TRUE, the difference with placebo is plotted.

plotPop

When plotPop='m', the mean values at each dose in the designs are joined using linear interpolation. Otherwise, the the population Emax parameters must be supplied with the data generator (see FixedMean or RandEmax). If the Emax parameters are not available, linear interpolation is used.

logScale

Not implemented

plotResid

Not implemented

plot

Return plotting output without plotting.

...

Other plot parameters. See plot.fitEmaxB for details

Note

The estimated curve is the posterior mean evaluated along a grid of dose values.

Examples

## 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)  

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)

plot(D1,id=3)
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)

plot(D1[2])

## End(Not run)