search for: reslow

...-1),variable=rep(colnames(Pred1)[-1],each=nrow(Pred1)),Predict=unlist(Pred1[,-1],use.names=FALSE),Actual=unlist(Actual1[,-1],use.names=FALSE),stringsAsFactors=FALSE) dat2New<- dat2[!(is.na(dat2$Predict)|is.na(dat2$Actual)),] ?dat3<- dat2New[order(dat2New$S1,dat2New$Predict),] library(plyr) resLow<-ddply(dat3,.(S1),summarize, cbind(head(Predict,5),head(Actual,5))) resHigh<-ddply(dat3,.(S1),summarize, cbind(head(rev(Predict),5),head(rev(Actual),5))) ?resLow1<-data.frame(Date=resLow[,1],Predict=resLow[,2][,1],Actual=resLow[,2][,2]) ?resHigh1<-data.frame(Date=resHigh[,1],Predict=res...

Hello All, I would like to optimize a (log-)likelihood function subject to a number of linear constraints between parameters. These constraints are equality constraints of the form A%*%theta=c, ie (1,1) %*% 0.8,0.2)^t = 1 meaning that these parameters should sum to one. Moreover, there are bounds on the individual parameters, in most cases that I am considering parameters are bound between zero

...assumptions. This is where I go wrong, but I'm not sure where. Non-Stratified Nomogram: f<-cph(S~A+B+C+D+E+F+H,x=T,y=T,surv=T,time.inc=10*12,method="breslow") srv=Survival(f) srv120=function(lp) srv(10*12,lp) quant=Quantile(f) med=function(lp) quant(.5,lp) at.surv=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9) at.med=c(120,80,60,40,30,20,15,10,8,6,4,2,0) nomogram(f,lp=F, fun=list(srv120, med),funlabel=c("120-mo Survival","Median Survival&qu...