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...+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"),fun.at=list(at.surv, at.med)) I get a the following warning: Warning message: In approx(fu[s], xseq[s], fat) : collapsing to unique 'x' values However, a great nomogram is constructed. But then I try to stratify......

...plogis(L), 1, 0) ddist <- datadist(age, blood.pressure, cholesterol, sex) options(datadist='ddist') f <- lrm(y ~ lsp(age,50)+sex*rcs(cholesterol,4)+blood.pressure) nom <- nomogram(f, fun=function(x)1/(1+exp(-x)), # or fun=plogis fun.at=c(.001,.01,.05,seq(.1,.9,by=.1),.95,.99,.999), funlabel="Risk of Death") #Instead of fun.at, could have specified fun.lp.at=logit of ... Thank you for you expain ! truly yours [[alternative HTML version deleted]]

...', 'coxph')" surv10 <- function(lp) surv(10,lp) surv5 <- function(lp) surv(5,lp) quant <- Quantile(f1) at.surv <- c(0.1, 0.3, 0.5, 0.7, 0.9) at.med1<-c(2,3,4, 5,6,7,8, 10,15,20,25, 30) par(cex=0.8) nom<- nomogram(f1, conf.int=F, fun=list(1-surv5, 1-surv10), funlabel=c('5-Year Survival Probability', '10-Year Survival Probability' ), lp=F, fun.at=c(at.surv, at.surv),label.every=1, force.label=FALSE, cex.axis=0.8, verbose=TRUE, cex.var=0.8) I would very much appreciate any assistance in this matter. Thank you Very much. -- View this message...

...x ph model to my data, but have beenreceiving an error when trying to fit a model to the nomogram. Here is the codeand corresponding error: ? ? >nomogramCF = nomogram(cph_age6_40avp4, +????????????????????lp.at= seq(-10,10,by =0.5),lp = TRUE, +?????????????????????? +??????????????????????funlabel="5year survival", +??????????????????????fun=surv5.CFdata5,fun.at= c(0.01,seq(0.1,0.9,by=0.2),0.99)) Error inapprox(fu[s], xseq[s], fat, ties = mean) : ??needat least two non-NA values to interpolate ? ? I have fit similar nomograms based on cox ph models usingsimilar code, so I?m...

...runif(n) < plogis(L), 1, 0) ) > dfr <- data.frame(T.Grade,Sex,Smoking, L, y) > ddist <- datadist(dfr) > options(datadist='ddist') > f<-lrm(y~T.Grade +Sex+Smoking, data=dfr) > nom<-nomogram(f,fun=function(x)1/(1+exp(-x)),fun.at=c(.01,.05,seq(.1,.9,by=.2),.9,1),funlabel="Risk of Death") > plot(nom, xfrac=0.45) load package bootstrap .................the problem.................... > theta.fit <- function(dfr,y){lsfit(dfr,y)} > theta.predict <- function(fit,dfr){cbind(1,dfr)%*%fit$coef} > sq.err <- function(y,yhat) { (y-yhat)^2} &...

...sign', 'coxph')" surv10 <- function(lp) surv(10,lp) surv5 <- function(lp) surv(5,lp) quant <- Quantile(f1) at.surv <- c(0.1, 0.3, 0.5, 0.7, 0.9) at.med1<-c(2,3,4, 5,6,7,8, 10,15,20,25, 30) par(cex=0.8) nom<- nomogram(f1, conf.int=F, fun=list(surv5, surv10), funlabel=c('5-Year Survival Probability', '10-Year Survival Probability' ), lp=F, fun.at=c(at.surv, at.surv),label.every=1, force.label=FALSE, cex.axis=0.8, verbose=TRUE, cex.var=0.8) How can I show failure probability instead of survival probability? I would very much appreciate any ass...

...='ddist1') s1 <- Surv(data1$time, data1$cens) cph.nomo <- cph(s1 ~ A+B, surv=T, x=T, y=T, time.inc=60) survcph <- Survival(cph.nomo, x=T, y=T, time.inc=60, surv=T) surv5 <- function(lp) survcph(60, lp) nomogram(cph.nomo, lp=T, conf.int=F, fun=list(surv5, surv7), funlabel=c("5 yr DFS")) # now have a useful nomogram model, with good discrimination and #calibration when checked with validate and calibrate (not shown) #....move on to validation cohort of n=800 Data2 #Validation data with same predictor variables A, B, cens, time # do I need to put...

...runif(n) < plogis(L), 1, 0) ) > dfr <- data.frame(T.Grade,Sex,Smoking, L, y) > ddist <- datadist(dfr) > options(datadist='ddist') > f<-lrm(y~T.Grade +Sex+Smoking, data=dfr) > nom<-nomogram(f,fun=function(x)1/(1+exp(-x)),fun.at=c(.01,.05,seq(.1,.9,by=.2),.9,1),funlabel="Risk of Death") > plot(nom, xfrac=0.45) load package bootstrap .................the problem.................... > theta.fit <- function(dfr,y){lsfit(dfr,y)} > theta.predict <- function(fit,dfr){cbind(1,dfr)%*%fit$coef} > sq.err <- function(y,yhat) { (y-yhat)^2} &...

...runif(n) < plogis(L), 1, 0) ) > dfr <- data.frame(T.Grade,Sex,Smoking, L, y) > ddist <- datadist(dfr) > options(datadist='ddist') > f<-lrm(y~T.Grade +Sex+Smoking, data=dfr) > nom<-nomogram(f,fun=function(x)1/(1+exp(-x)),fun.at=c(.01,.05,seq(.1,.9,by=.2),.9,1),funlabel="Risk of Death") > plot(nom, xfrac=0.45) load package bootstrap .................the problem.................... > theta.fit <- function(dfr,y){lsfit(dfr,y)} > theta.predict <- function(fit,dfr){cbind(1,dfr)%*%fit$coef} > sq.err <- function(y,yhat) { (y-yhat)^2} &...

...tage+er+cytcyt+Cyt_PCDK2 , data=data11, surv=T, x=T, y=T, time.inc=5) surv<- Survival(f1) surv10 <- function(lp) surv(10,lp) surv5 <- function(lp) surv(5,lp) quant <- Quantile(f1) at.surv <- c(0.1, 0.3, 0.5, 0.7, 0.9) nom<- nomogram(f1, conf.int=F, fun=list(surv5, surv10), funlabel=c('5-Year Survival Probability', '10-Year Survival Probability' ), lp=F, fun.at=c(at.surv, at.surv)) ################# total points ########################## age.p <- lm(nom[[1]]$points ~ nom[[1]]$age) tot.points <- (age*age.p$coef[2]+age.p$coef[1])+ (her2=='Positi...

...^2,data=df.w, weight=weight.cens, subset=failcode==1, x=T, y=T, surv=T, time.inc = 2.5) mod2 ########################## To develop nomogram surv<-Survival(mod) nom.sur<-nomogram(mod,fun=list(function(x) 1-surv(3,x), function(x) 1-surv(5,x), function(x) 1-surv(7,x), function(x) 1-surv(9,x)), funlabel=c("3-year event 1 Prob.", "5-year event 1 Prob.", "7-year event 1 Prob.", "9-year event 1 Prob."), lp=T) #plot(nom.sur, fun.side=list(rep(1,8), c(1,1,1,3,1,3,1,3,1,3,1,3,1,3,1),rep(1,10),rep(1,12))x) plot(nom.sur) #plot(nom.sur, fun.side=list(rep(1,8),c(1,1,1...

dear professor: I have a problem about the nomogram.I have got the result through analysing the dataset "exp2.sav" through multinominal logistic regression by SPSS 17.0. and I want to deveop the nomogram through R-Projject,just like this : > n<-100 > set.seed(10) > T.Grade<-factor(0:3,labels=c("G0", "G1", "G2","G3")) >