plotBdensity {clinDR} | R Documentation |
Density plot displaying Bayes prior or posterior dose response
Description
Density plot over a grid of doses displaying the prior or posterior distribution for the mean dose response computed from simulated input model parameters.
Usage
plotBdensity(dgrid,
parm,
modelFun=emaxfun,
qlevL=c(0.025,0.05,0.10,0.25),
plotDif= FALSE,
logit= FALSE, ...)
Arguments
dgrid |
The Bayes prior or posterior summaries are evaluated and plotted on
the |
parm |
Matrix of simultated parameter values (each row is a
simulated parameter vector). The |
modelFun |
The mean model function. The first argument is a
scalar dose, and the second argument is a matrix of parameter values.
The rows of the matrix are random draws of parameter vectors for the
model. The default function is the 4-parameter Emax function |
qlevL |
Intervals are formed with percentile boundaries at (qlevL,1-qlevL).
|
plotDif |
If TRUE, plot difference between doses and placebo. |
logit |
Default is F. If T, inverse logit transform applied to Emax function output for comparison to dose group sample proportions. |
... |
Parameters passed to generic plot function |
Details
A sample of parameters from the joint prior or posterior distribution
must be supplied (typically produced by BUGS). A density plot with
contours corresponding to the perentiles in qlevL created by function
DRDensityPlot
.
Value
A list containing two matrices with the number of rows equal to the number dose grid points, and columns corresponding to percentiles in qlevL
:
qL |
Lower perentiles from |
qH |
Upper percentiles 1- |
Author(s)
Neal Thomas
References
Spiegelhalter, D., Thomas, A., Best, N., and Lunn, D. (2003), WinBUGS User Manual Version 1.4, Electronic version www.mrc-bsu.cam.ac.uk/bugs
See Also
plot.plotB
, plotD
, plot.fitEmax
,
DRDensityPlot
Examples
## Not run:
data("metaData")
exdat<-metaData[metaData$taid==6 & metaData$poptype==1,]
prior<-emaxPrior.control(epmu=0,epsca=10,difTargetmu=0,difTargetsca=10,dTarget=80.0,
p50=3.75,sigmalow=0.01,sigmaup=20)
mcmc<-mcmc.control(chains=3)
msSat<-sum((exdat$sampsize-1)*(exdat$sd)^2)/(sum(exdat$sampsize)-length(exdat$sampsize))
fitout<-fitEmaxB(exdat$rslt,exdat$dose,prior,modType=4,
count=exdat$sampsize,msSat=msSat,mcmc=mcmc)
parms<-coef(fitout)[,1:4] #use first intercept
dgrid<-seq(0,1,length=100)
pout<-plotBdensity(dgrid,parm=parms)
pout2<-plotBdensity(dgrid,parm=parms,plotDif=TRUE,
xlab='Dose',ylab='Dif with PBO')
## End(Not run)