Description Usage Arguments Details Author(s) References See Also Examples
Plot the probability measures arising from a HDPM of normals model for conditional density estimation. Support provided by the NIH/NCI R01CA75981 grant.
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object |
|
pred |
indicator for the values of the predictors, given by the row pred in xpred, for which the conditional densities must be drawn. |
i |
study indicator. |
r |
indicator for including (0) or not (1) the common measure. |
ask |
logical variable indicating whether the plots must be displayed sequentially or not. |
nfigr |
number of rows in the figure. |
nfigc |
number of columns in the figure. |
... |
further arguments to be passed. |
Must run HDPMcdensity
first to generate posterior
simulations.
Alejandro Jara <atjara@uc.cl>
Peter Mueller <pmueller@mdanderson.org>
Mueller, P., Quintana, F. and Rosner, G. (2004). A Method for Combining Inference over Related Nonparametric Bayesian Models. Journal of the Royal Statistical Society, Series B, 66: 735-749.
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# Data
data(calgb)
attach(calgb)
y <- cbind(Z1,Z2,Z3,T1,T2,B0,B1)
x <- cbind(CTX,GM,AMOF)
z <- cbind(y,x)
# Data for prediction
data(calgb.pred)
xpred <- as.matrix(calgb.pred[,8:10])
# Prior information
prior <- list(pe1=0.1,
pe0=0.1,
ae=1,
be=1,
a0=rep(1,3),
b0=rep(1,3),
nu=12,
tinv=0.25*var(z),
m0=apply(z,2,mean),
S0=var(z),
nub=12,
tbinv=var(z))
# Initial state
state <- NULL
# MCMC parameters
mcmc <- list(nburn=5000,
nsave=5000,
nskip=3,
ndisplay=100)
# Fitting the model
fit1 <- HDPMcdensity(formula=y~x,
study=~study,
xpred=xpred,
prior=prior,
mcmc=mcmc,
state=state,
status=TRUE)
# Posterior inference
fit1
summary(fit1)
# Plot the parameters
# (to see the plots gradually set ask=TRUE)
plot(fit1,ask=FALSE)
# Plot the a specific parameters
# (to see the plots gradually set ask=TRUE)
plot(fit1,ask=FALSE,param="eps",nfigr=1,nfigc=2)
# Plot the measure for each study
# under first values for the predictors, xpred[1,]
predict(fit1,pred=1,i=1,r=1) # pred1, study 1
predict(fit1,pred=1,i=2,r=1) # pred1, study 2
# Plot the measure for each study
# under second values for the predictors, xpred[2,]
predict(fit1,pred=2,i=1,r=1) # pred2, study 1
predict(fit1,pred=2,i=2,r=1) # pred2, study 2
# Plot the idiosyncratic measure for each study
# under first values for the predictors, xpred[1,]
predict(fit1,pred=1,i=1,r=0) # study 1
predict(fit1,pred=1,i=2,r=0) # study 2
# Plot the common measure
# under first values for the predictors, xpred[1,]
predict(fit1,pred=1,i=0)
## End(Not run)
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