similar to: How to retrieve bias-corrected probability from calibrate.rms

Dear R users: Recently, I tried to write a program to calculate cross-validated predicted value. My sources are as follows. However, the R reported an error. Could you please check the sources? Thanks. set.seed(100) x<-rnorm(100) y<-sample(rep(0:1,50),replace=T) dat<-data.frame(x,y) library(rms) fito<-lrm(y~x) preo<-predict(fito) pre<-matrix(NA,nrow=100,ncol=200) for (i in

Dear R users: I tried to use val.surv to give an internal validation of survival prediction model. I used the sample sources. # Generate failure times from an exponential distribution set.seed(123) # so can reproduce results n <- 1000 age <- 50 + 12*rnorm(n) sex <- factor(sample(c('Male','Female'), n, rep=TRUE, prob=c(.6, .4))) cens <- 15*runif(n) h

After upgrading R to 2.9.2, I can't use the anova() fuction. It says "could not find function "Varcov" ". What's wrong with my computer? Help needed, thanks! Yao Zhu Department of Urology Fudan University Shanghai Cancer Center No. 270 Dongan Road, Shanghai, China [[alternative HTML version deleted]]

Hi, everyone: I ask for help about translating a stata program into R. The program perform cross validation as it stated. #1. Randomly divide the data set into 10 sets of equal size, ensuring equal numbers of events in each set #2. Fit the model leaving out the 1st set #3. Apply the fitted model in (2) to the 1st set to obtain the predicted probability of a prostate cancer diagnosis. #4. Repeat

Hi, everyone. I know it may be a basic statistical question. But I can't find a good answer. I have a question raised by one of the reviewers. Factor A expression was strongly correlated with B expression (chi-square) in this series. Prior reports by the same authors showed that B expression strongly correlated with survival (Log-rank). Please provide an explanation why then were the results

Dear R users: I want to externally validate a model with val.surv. Can I use only calculated survival (at 1 year) and actual survival? Or I needed the survival function and actual survival. Thanks *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

Dear experts: I am a newbie to R. Recently, I try to make prediction models with R and the Design library. I have read Prof. Harrell's excellent book. But I did not quite understand. I have two problems about the validation and calibration of prediction models: 1. Can someone explain the results outputted by the validate() function? How to get 95% of c-value of validate? 2. How to add 95% ci

Dear R-users I want to generate a growth chart like this: http://php.med.unsw.edu.au/embryology/images/9/9a/WHO-XX_length_birth_to_2_years.png can you give some suggestions about the functions I should use? Thanks *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

Dear R users: In stat, there is a "stpower" function for power analysis and sample-size determination in survival models. Is there a counterpart in R? Thanks Yao Zhu Department of Urology Fudan University Shanghai Cancer Center Shanghai, China [[alternative HTML version deleted]]

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*

Dear R users: Recently, I learn to use penalized logistic regression. Two packages (penalized and glmnet) have the function of lasso. So I write these code. However, I got different results of coef. Can someone kindly explain. # lasso using penalized library(penalized) pena.fit2<-penalized(HRLNM,penalized=~CN+NoSus,lambda1=1,model="logistic",standardize=TRUE) pena.fit2

Dear R users, I am a new R user and something stops me when I try to write a academic article. I want to make a nomogram to predict the risk of prostate cancer (PCa) using several factors which have been selected from the Logistic regression run under the SPSS. Always, a calibration plot is needed to validate the prediction accuracy of the nomogram. However, I tried many times and read a lot of

Dear useRs: I use pROC package to compute the bootstrap C.I. of AUC. The command was as follows: roc1<-roc(all$D,all$pre,ci=TRUE,boot.n=200) However, the result was: Area under the curve: 0.5903 95% CI: 0.479-0.7016 (DeLong) Why the C.I. was computed by the Delong Method? Yao Zhu Department of Urology Fudan University Shanghai Cancer Center Shanghai, China [[alternative HTML version

Dear R users Function improveProb in the rms library calculate NRI and IDI for predictions of binary outcome. Do anyone extent its use in survival data? Many thanks. *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

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

Dear all, when i do the calibration plot, i put the x label y label , there is some labels are i did not put it , like "resample optimism added ..." i want to get rid of the these label , is any body know how can i get rid of these label. these are the following command i used cal <- calibrate(f, u=12, method=c("boot"), B=100,m=70, data=a1)

Hi, I created a logistic regression model using the glmnet package. This model is of class "glmnet" or "lognet". I wanted to plot a calibration curve for this model using the calibrate() function from rms package, but the objects used are different, rms requires a fit from lrm(). Is there another function for getting the calibration plot for this glmnet object, or can anyone

Dear UseRs: Recently, I have read an article regarding the association between age and lymph node metastases. http://jco.ascopubs.org/content/27/18/2931.long In statistical analysis, the authors stated "Because a nonlinear relationship between age and lymph node involvement was expected based on existing literature, lymph node involvement was also regressed on age using nonparametric

Good afternoon! I need to evaluate the goodness-of-fit (aka calibration) for survival probability estimates from a Cox model. I tried to use 'calibrate' in the Design package but I'm not sure if it should/would produce what I need (ie a chi-sq type statistic with a table of expected vs observed probabilities). Any other functions I should be aware of? Also, has anybody come across

A new version of rms is now available on CRAN for Linux and Windows (Mac will probably be available very soon). Largest changes include latex methods for validate.* and adding the capability to force a subset of variables to be included in all backwards stepdown models (single model or validation by resampling). Recent updates: * In survplot.rms, fixed bug (curves were undefined if