search for: cox1

Hi, I'm trying the brlr function in a penalized logistic regression function. However, I am not sure why I am encountering errors. I hope to seek your advice here. (output below) Thank you! Your help is truly appreciated. Min-Han #No error here, the glm seems to work fine > genes.cox1.glm1<-glm(as.formula(paste(paste('as.integer(sim.cv.cox.yhat1)~'),paste('sim.dat.tst[',genes.cox1.rows,',]',sep="",collapse='+')))) #Something happened here ... I only substituted brlr for glm > genes.cox1.glm1<-brlr(as.formula(paste(paste('a...

..."t2", "t2"), status = c(NA, "s0", "s1", "s2"), data= S1, trans=tmat, keep=covs) # add 'transition-specific' covariates SLV <- expand.covs(SL, covs, append=TRUE, longnames=TRUE) head(SLV) names(SLV) # fit stratified Markov model cox1 <- coxph(Surv(Tstart, Tstop, status) ~ (v1.1 + v2.1) * v3.1 + (v1.2 + v2.2) * v3.2 + (v1.3 + v2.3) * v3.3 + strata(trans), SLV, method = "breslow") summary(cox1) # 'cloned' data for making predictions: specific covariate 'v3' fixed at 0 (& 1): SLV0 <- SLV[1:...

...mal() is the inverse normal distribution function, and where n is the sample size and _pi = 3.141592654 ... 4. In case of ties in the original xb, substitute average zi's over each of the tied sets. 5. Perform regression (e.g. Cox) on z = z1,...,zn. I can obviously do steps 1 and 2: # step 1 # cox1 <- cph(Surv(stime1,eind1)~adat1+bdat1+c11+c21) c1dat <- cox1$linear.predictors # step 2 # ris <- rank(c1dat,ties.method="minimum") Can anyone advise how I might invoke the inverse normal. I thought 'qnorm' may be an option but I'm lacking the necessary parameters wi...

Hello everyone, I am searching for a covariate selection procedure in a cox model formulated as a counting process. I use intervals, my formula looks like coxph(Surv(start,stop,status)~ x1+x2+...+cluster(id),robust=T) where id is a country code (I study occurence of civil wars from 1962 to 1997). I'd like something not based on p-values, since they have several flaws for this purpose. I turned

Dear Sir/Ma, I Adelabu.A.A, one of the R-users from Nigeria. When am running a coxph command the below error was generated, and have try some idea but not going through. kindly please assist: > cox1 <- coxph(Surv(tmonth,status) ~ sex + age + marital + sumassure, X) Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not converge > summary(cox1, conf.int=0.95, exact = TRUE) Call: coxph(formula = Surv(tmonth, status) ~...

...ion 0.92 have been fixed; notably 1) some problems with predict and newdata 2) plot.gam now works with any model for which predict( ..., type="terms") is appropriate (well, at least several). Examples are lm, glm, gam and coxph models. So for example, if you have fit a Cox model cox1 <- coxph( Surv(Survival, death) ~ Grade + ns(Age,4) + ns(Size,4)) Then plot.gam(cox1, se=T) will produce three plots, one for each term in the model, with standard error bands. 3) I have implemented the fast versions of backfitting for models consisting of all local regression terms (...

...ion 0.92 have been fixed; notably 1) some problems with predict and newdata 2) plot.gam now works with any model for which predict( ..., type="terms") is appropriate (well, at least several). Examples are lm, glm, gam and coxph models. So for example, if you have fit a Cox model cox1 <- coxph( Surv(Survival, death) ~ Grade + ns(Age,4) + ns(Size,4)) Then plot.gam(cox1, se=T) will produce three plots, one for each term in the model, with standard error bands. 3) I have implemented the fast versions of backfitting for models consisting of all local regression terms (...

...1 4 4 2 1 1 45.48 2 0 1 0 0 0 14.84 2 1 2 0 1 1 08.63 A complication here is that units can experience repeated events while previous events are still ongoing. I tried the following: cox1 <- coxph( Surv( dur0, dur1, event) ~ strata(eventn) + x) This works fine under the breslow and efron method. However, since I have a fair number of ties, especially of repeated events while previous events are still ongoing, the exact method seems advisable. The help says that the exact method...

...: 0.3260 Mean :0.9906 Mean :0.5 Mean :0.4997 Mean : 6.8948 3rd Qu.:0.178325 3rd Qu.: 0.1783 3rd Qu.:1.0000 3rd Qu.:1.0 3rd Qu.:1.0000 3rd Qu.: 7.7409 Max. :5.000000 Max. :52.8990 Max. :1.0000 Max. :1.0 Max. :1.0000 Max. :431.2779 > exp((cox1 <- coxph(Surv(t1, d1)~ x1 + z1+ z2, ties="breslow"))[[1]]) #hrs x1 z1 z2 3.3782387 0.4925040 0.4850214 Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not converge #accelerated failure time...