similar to: NaN causes "error in fitter" with cph.calibrate from pkg Design

Hi, I have a list of fit objects (fit objects from HMISC functions) I create elements in the list in this way lrm.sumtot <- lrm( ae7bepn ~ trarm + sumtot , data=sd.fix) lrm.list[['lrm.sumtot']] <- lrm.sumtot And I can run (anova(lrm.sumtot)) The following also gives the anova I'd expect zz <- lrm.list[['lrm.sumtot']];anova(zz) And similarly for the summary

I have been trying to specify a different color palette to the image method in plot.Design. My model has crossed two rcs() arguments and one two-level gender argument. The goal which appears to have been mostly achieved is to produce separate bivariate plots for men and women The call to plot does produce a level plot but it appears only with the default color palette despite various

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

Hi! How can I exclude the legends from calibration plots  generated by calibrate.cph regards, Salvo [[alternative HTML version deleted]]

While sm4.6ll<-fit.mult.impute(Surv(agesi, si)~partner+ in.love+ pubty+ FPA+ strat(gender),fitter = cph, xtrans = dated.sexrisk2.i, data = dated.sexrisk2, x=T,y=T,surv=T, time.inc=16) runs perfectly using Hmisc, Design and mice under R11 run via Sciviews-K, with library(Design) library(mice) ds2d<-datadist(dated.sexrisk2) options(datadist="ds2d")

Hi, List, I am using function 'cph' in package 'Design'. I have run into this error message but could not find documentation after looking for a long time. Could someone help me out? What kind of problem it is in my data set and how to fix it? Thanks a lot! Auston Error in fitter(X, Y, strata = Strata, offset = offset, weights = weights, :NA/NaN/Inf in foreign function

I am trying to build a model to aid a clinical decision. Certain patients have a blood marker measured at each visit - a rise of this may indicate recurrence of the cancer after treatment (endpoint is "clinical recurrence", censored). In a proportion (up to 30%), this rise is a false positive - hence I wish to correlate factors at the time of the rising test to clinical recurrence,

Hi, I have fitted a cox model with time-varying covariates (counting process style) using the cph function of the Design package. Now I want to know the survival probabilities at each time point given the history of a single individual. I know the survest function, but I am not sure how to interpretet its output when using time-varying covariates. Does it just give the probabilities as if it

Hi, I am trying to run a conditional logistic model on a nested case-control study using cph() and then estimate survival based on the model. The data came from Prof Bryan Langholz website where he also has the SAS code to this, so I am trying to replicate the SAS results. The data attached. Basically, the variables are: rstime: risk set age rsentry: fake entry time, just before rstime setno:

Sorry everybody, temporary dyslexia. Sent from my BlackBerry wireless smartphone

You wrote: "It may be best to either write to the package maintainer (me, as you did) or post to the group but not both." This is just a note that I disagree wrt my own packages: I go on vacation or trips, or have other projects so won't always answer Other folks on the list often have good ideas that I'd miss My preferred standard is "ask the list, with a copy

Hi all! I'm trying to plot validate.cph. I have a problem because I'm collating several plots but I can't reduce the size of the plots otherwise the labels below the plot overlap.  If I remove the footnotes, I can add it in the main text.  How can I remove the footnotes i.e. stuff like black:observed gray:optimism ... Salvo [[alternative HTML version deleted]]

Hi all, I am trying to get bootstrap resampled estimates of covariates in a Cox model using cph (Design library). Using the following I get the error: > ddist2.abr <- datadist(data2.abr) > options(datadist='ddist2.abr') > cph1.abr <- cph(Surv(strt3.abr,loc3.abr)~cov.a.abr+cov.b.abr, data=data2.abr, x=T, y=T) > boot.cph1 <- bootcov(cph1.abr, B=100, coef.reps=TRUE,

Dear all, I used cph() function from Frank harrell's Design package to create a survival model, then used functions 'Function' and 'sascode' to generate prediction equation based on the saved survival model. But it failed. I included a stratified variable in the model. If I removed the stratification, they were working well. Does that mean that function 'Function'

Hello, I am a beginner in R and statistics, so my question may be trivial. Sorry in advance. I performed a Cox proportion hazard regression with 2 categorical variables with cph{design}. Then an anova on the results. the output is > anova(cph(surv(survival, censor) ~ plant + leaf.age + plant*leaf.age, > Mpnymph) Wald Statistics Response: Surv(survival, censored)

Dear R users, I know, this is the second time i return on this topic. Sorry, but this analysis is of great value for me, and i hope someone can help me. I need to model a time-varying effect in a Cox model. Briefly explained here: http://books.google.com/books?id=9kY4XRuUMUsC&lpg=PP1&hl=it&pg=PA147#v=onepage&q&f=false

Hi all (and especially Frank), I'm trying to use x=T, y=T in order to run a validated stepwise cox regression in rsm, having multiply imputed using mice. I'm coding model.max<-fit.mult.impute(baseform,cph,miced2,dated.sexrisk2,x=T,y=T) baseform is baseform<-Surv(si.age,si=="Yes")~ peer.press + copy.press + excited + worried + intimate.friend + am.pill.times +

Dear all: I have used the cph function in the rms package. log10 was used to transform the variables, as follows: fit<-cph(pfsurv~log10(x1)+log10(x2),x=T,y=T,surv=T) after I run the nomogram function. I found "variable limits and transformations are such that an infinite axis value has resulted." How to add variable limits in the nomogram function? Thanks a lot *Yao Zhu*