search for: ymis

...I supplied was a fake dataset. ################################################################ library(mix) n <-100 x1<-rnorm(n) x2<-rnorm(n,2,1.2) x3<-rnorm(n,1,2) x4<-floor(rnorm(n)*3) y <-rnorm(n,1*x1+2*x2+3*x3+4*x4,2) w <-rnorm(n,3,1.2) ymis<-y ymis[floor(runif(10,1,n))]<-NA wmis<-w wmis[floor(runif(10,1,n))]<-NA dat<-as.data.frame(cbind(wmis,ymis,x1,x2,x3,x4)) s <- prelim.mix(dat,3) # do preliminary manipulations thetahat <- em.mix(s) # ML estimate for unrestricted model rngseed...

I am doing a simulation with a relatively large data set (20,000 observations) for which I want to calculate the area under the Receiver Operator Curve (AUC) for many parameter combinations. I am using the ROC library and the following commands to generate each AUC: rocobj=rocdemo.sca(truth = ymis, data = model$fitted.values, rule = dxrule.sca) #generation of observed ROC object aucobj=AUC(rocobj) #pulling out just the observed AUC - trapezoidal not integrated but they are pretty slow. Does anyone know of a faster way to get the AUC? Thanks, Nina [[alternative HTML version d...

...rr/tc.csv","C:/R") #RUN NORM tc <- read.csv("tc.csv", header = TRUE) rngseed(1234567) #set random number generator seed s <- prelim.norm(tc) thetahat <- em.norm(s) #find the MLE for a starting value theta <- da.norm(s,thetahat,steps=20,showits=TRUE,return.ymis=TRUE) #take 20 steps ximp <- imp.norm(s,thetahat,tc) #impute missing data under the MLE

...<-matrix(rnorm(n*p),nrow=n) #create missing data for (j in 1:p) {miss<-rbinom(n,1,prob.miss) for (i in 1:n){if (miss[i]==1) x[i,j]<-NA}} #create imputations seed<-1234 rngseed(seed) s <- prelim.norm(x) thetahat <- em.norm(s,showits=F) da.norm(s,thetahat,steps=1000,showits=F,return.ymis=TRUE)