search for: karno

...are not very familiar, we just happen to have gathered some data that needs this type of analysis. We've done quite a bit of reading, but answers escape us, even though the question below seems simple. Considering the following example from ?survdiff: > survdiff(Surv(time, status) ~ pat.karno, data=lung) Call: survdiff(formula = Surv(time, status) ~ pat.karno, data = lung) n=225, 3 observations deleted due to missingness. N Observed Expected (O-E)^2/E (O-E)^2/V pat.karno=30 2 1 0.658 0.1774 0.179 pat.karno=40 2 1 1.337 0.0847 0.086...

...coxpath(), but I have difficult time understanding the output. Therefore, I would greatly appreciate if anyone can help me understand how to use the function. > data(lung.data) > attach(lung.data) > fit.a <- coxpath(lung.data) > print(fit.a) Call: coxpath(data = lung.data) Step 1 : karno Step 2 : celltype Step 5 : trt Step 6 : prior Step 7 : age Step 8 : diagtime > summary(fit.a) Call: coxpath(data = lung.data) Df Log.p.lik AIC BIC Step 1 0 -505.8840 1011.7679 1011.7679 Step 2 1 -486.0691 974.1382 977.0581 Step 5 2 -484.8520 973.7040 979.5440 Step...

...s: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations, complexity param=0.03665178 mean=305.2325, MSE=44176.93 left son=2 (81 obs) right son=3 (147 obs) Primary splits: pat.karno < 75 to the left, improve=0.03661157, (3 missing) ph.ecog < 1.5 to the right, improve=0.036207...

...ne hazard is no longer monotonic. So ordering based on just the linear predictors would not be appropriate but risk to t should be used (after choosing a t). Am I wrong? Example: lung$time2=lung$time/365 lung2=na.omit(lung) # the usual c-index fit1 <- coxph(Surv(time, status) ~ ph.ecog+ph.karno+pat.karno+meal.cal+wt.loss + age + sex, lung2) survConcordance(Surv(time, status) ~predict(fit1), lung2) # and the corresponding c-index for start, stop data (counting) fit2 <- coxph(Surv(age,age+time2, status) ~ ph.ecog +ph.karno+pat.karno+meal.cal+wt.loss + age + sex, lung2) survConcordance(...

Hi, I am using rpart as a part of my masters' project. I am trying to print out the resulting model using plot() function along with text() function. I am having difficulties with labels being cut-off. In text() function, I am using use.n=T option to get the number of people in each nodes but the on the lower and left part of the plot, the numbers get cut off. Thanks! Linus [[alternative

Hi, I'm trying to using pspline in bic.surv{BMA}. ############################# library(BMA) library(survival) data(veteran) test.bic.surv<- bic.surv(Surv(time,status) ~ karno+pspline(age,df=3)+diagtime+prior, data = veteran, factor.type = TRUE) summary(test.bic.surv, conditional=FALSE, digits=2) ############################# The results are: --------------------------------------------- Call: bic.surv.formula(f = Surv(time, status) ~ karno + pspline(age, df = 3)...

...ng.data$time[131:137], status=lung.data$status[131:137]) > testpred <- predict(fit.a, testall, s=0.99, type='coxph', mode='lambda.fraction') > testpred Call: coxph(formula = Surv(time, status) ~ x, method = object$method) coef exp(coef) se(coef) z p karno -0.00756 0.992 0.0364 -0.208 0.84 Likelihood ratio test=0.89 on 1 df, p=0.344 n= 7 > newd1 <- list(testall$x[1,]) > survtest <- survfit(testpred,newdata=newd1,individual=T) > survtest Call: survfit(formula = testpred, newdata = newd1, individual = T) records n.max n....

Dear all, What is the cumulative distribution (with parameterization) used within survreg with respect to the log-logistic distribution? That is, how are the parameters linked to the survivor function? Best regards, Mario [[alternative HTML version deleted]]

I need to force a coxph() function in R to use a pre-calculated set of beta coefficients of a gene signature consisting of xx genes and the gene expression is also provided of those xx genes. If I try to use "coxph()" function in R using just the gene expression data alone, the beta coefficients and coxph$linear.predictors will change and I need to use the pre-calcuated linear predictor

Dear all, I have a question with respect to counting process formulation of the coxph(survival) model. I have two groups of observations for which I have partitioned each observation into two distinct time intervals, namely, entry day till day 13, and day 13 till death or censorship day (of course the latter only for the observations that survived the first 13 day interval), and added a

I am confused when trying the function survfit. my question is: what does the survival curve given by plot.survfit mean? is it the survival curve with different covariates at different points? or just the baseline survival curve? for example, I run the following code and get the survival curve #### library(survival) fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)

...vel, digits, n, use.n ) { if(use.n) {paste(formatg(yval,digits),"\nn=", n,sep="")} else{paste(formatg(yval,digits))} }) } tst.lst<-list(eval=tst.eval, split=tst.split, init=tst.init) data(lung) fit1 <- rpart(Surv(time, status) ~ age + ph.karno + meal.cal,data=lung,control=rpart.control(minsplit=30, xval=0, cp=.011),method=tst.lst) -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "...

...ng.data$time[131:137], status=lung.data$status[131:137]) > testpred <- predict(fit.a, testall, s=0.99, type='coxph', mode='lambda.fraction') > testpred Call: coxph(formula = Surv(time, status) ~ x, method = object$method) coef exp(coef) se(coef) z p karno -0.00756 0.992 0.0364 -0.208 0.84 Likelihood ratio test=0.89 on 1 df, p=0.344 n= 7 > newd1 <- list(testall$x[1,]) > survtest <- survfit(testpred,newdata=newd1,individual=T) > survtest Call: survfit(formula = testpred, newdata = newd1, individual = T) records n.max n....