predict.fitEmaxB {clinDR}  R Documentation 
The mean/proportion response for different doses estimated from a Bayesian Emax model is computed along with corresponding posterior intervals. The results are computed for a vector of input dose levels. The estimates are posterior means or medians of the MCMC generated means/proportions. For binary outcomes, the estimated response rates are computed on the logit scale and then backtransformed before forming the estimates and posterior intervals.
## S3 method for class 'fitEmaxB'
predict(object, dosevec, clev = 0.9,
int = 1, dref = 0, xvec=NULL, ...)
object 
Output of 
dosevec 
Vector of doses to be evaluated. 
clev 
Level for the posterior intervals about the mean/proportion at each dosevec. 
int 
The index for the protocol (intercept) to use for the predictions 
dref 
Differences in response between 
xvec 
The vector of centered baseline values for the prediction model when

... 
No additonal parameters will be utilized. 
Results computed from simple tabulations of the MCMC parameters evaluated in the Emax function.
If baseline covariates were included in the fit and xvec
is not specified,
then the predicted value is the mean of the predictions for all patients in the
specified protocol. Note that the protocol must be specified, or the
prediction defaults to patients from the first protocol. Note that for binary
data, the distinction between the mean of the predicted values and the predicted
value as the mean of the covariates can be important.
A list with estimated mean/proportion (pred, predMed)
, lower bound, upper
bound, posterior SD, and corresponding values for differences
with the reference dose. One value for each dose in dosevec.
The MCMC response means (proportions for binary data) are in
simResp
, and the residual SD for continuous data are in
sigsim
.
Neal Thomas
fitEmaxB
## 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$sampsize1)*(exdat$sd)^2)/(sum(exdat$sampsize)length(exdat$sampsize))
fitout<fitEmaxB(exdat$rslt,exdat$dose,prior,modType=4,
count=exdat$sampsize,msSat=msSat,mcmc=mcmc)
predout<predict(fitout,dosevec=sort(unique(exdat$dose)))
## End(Not run)