DProc(x,y,fitx=NULL,fity=NULL,ngrid=1000,priorx,priory,
mcmcx,mcmcy,statex,statey,
statusx,statusy,data=sys.frame(sys.parent()),
na.action=na.fail)fitx is missing).fity is missing).TRUE) or the
continuation of a previous analysis (FALSE). In the latter case
the current value of the parameters must be specifieTRUE) or the
continuation of a previous analysis (FALSE). In the latter case
the current value of the parameters must be specifieNAs. The default action (na.fail) causes
DProc to print an error message and terminate if there are any
DProc representing the ROC curve analysis
based on DP mixture of normals models fit. Generic functions such as
print, and plot have methods to show the results of
the fit. The results include the estimated densities, cdf's, and ROC curve.
}
seealso{
code{DPdensity}
}
references{
Escobar, M.D. and West, M. (1995) Bayesian Density Estimation and Inference
Using Mixtures. Journal of the American Statistical Association, 90: 577-588.
Kraemer, H. C. (1992). Evaluating Medical Tests. Sage Publications.
}
examples{
dontrun{
##############################################################
# Fertility data example:
# The following are Sperm Deformity Index (SDI) values from
# semen samples of men in an infertility study. They are
# divided into a "condition" present group defined as those
# whose partners achieved pregnancy and "condition" absent
# where there was no pregnancy.
#
# Aziz et al. (1996) Sperm deformity index: a reliable
# predictor of the outcome of fertilization in vitro.
# Fertility and Sterility, 66(6):1000-1008.
#
##############################################################
"pregnancy"<- c(165, 140, 154, 139, 134, 154, 120, 133,
150, 146, 140, 114, 128, 131, 116, 128,
122, 129, 145, 117, 140, 149, 116, 147,
125, 149, 129, 157, 144, 123, 107, 129,
152, 164, 134, 120, 148, 151, 149, 138,
159, 169, 137, 151, 141, 145, 135, 135,
153, 125, 159, 148, 142, 130, 111, 140,
136, 142, 139, 137, 187, 154, 151, 149,
148, 157, 159, 143, 124, 141, 114, 136,
110, 129, 145, 132, 125, 149, 146, 138,
151, 147, 154, 147, 158, 156, 156, 128,
151, 138, 193, 131, 127, 129, 120, 159,
147, 159, 156, 143, 149, 160, 126, 136,
150, 136, 151, 140, 145, 140, 134, 140,
138, 144, 140, 140)
"nopregnancy"<-c(159, 136, 149, 156, 191, 169, 194, 182,
163, 152, 145, 176, 122, 141, 172, 162,
165, 184, 239, 178, 178, 164, 185, 154,
164, 140, 207, 214, 165, 183, 218, 142,
161, 168, 181, 162, 166, 150, 205, 163,
166, 176)
#########################################################
# Estimating the ROC curve from the data
#########################################################
# Initial state
statex <- NULL
statey <- NULL
# Prior information
priorx <-list(alpha=10,m2=rep(0,1),
s2=diag(100000,1),
psiinv2=solve(diag(5,1)),
nu1=6,nu2=4,
tau1=1,tau2=100)
priory <-list(alpha=20,m2=rep(0,1),
s2=diag(100000,1),
psiinv2=solve(diag(2,1)),
nu1=6,nu2=4,
tau1=1,tau2=100)
# MCMC parameters
nburn<-1000
nsave<-2000
nskip<-0
ndisplay<-100
mcmcx <- list(nburn=nburn,nsave=nsave,nskip=nskip,
ndisplay=ndisplay)
mcmcy <- mcmcx
# Estimating the ROC
fit1<-DProc(x=pregnancy,y=nopregnancy,priorx=priorx,priory=priory,
mcmcx=mcmcx,mcmcy=mcmcy,statex=statex,statey=statey,
statusx=TRUE,statusy=TRUE)
fit1
plot(fit1)
#########################################################
# Estimating the ROC curve from DPdensity objects
#########################################################
fitx<-DPdensity(y=pregnancy,prior=priorx,mcmc=mcmcx,
state=statex,status=TRUE)
fity<-DPdensity(y=nopregnancy,prior=priory,mcmc=mcmcy,
state=statey,status=TRUE)
# Estimating the ROC
fit2<-DProc(fitx=fitx,fity=fity)
fit2
plot(fit2)
}
}
author{
Alejandro Jara email{