similar to: validate survival with val.surv

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

Dear R users: In Prof. Harrell's library rms, calibrate.rms plot the Bias-corrected Probability and Apparent Probability. The latter one can be retrieved from class calibrate.default. But how to retrieve the former one. BW *Yao Zhu* *Department of Urology Fudan University Shanghai Cancer Center Shanghai, China* [[alternative HTML version deleted]]

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

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 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

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

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 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: 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 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]]

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 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

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

在 2018年05月29日 14:58, mayuyu.io 写道: > Any hint on the date of LLVM Social ShangHai? Wu Wei scheduled July 1th http://hellollvm.org/ > > Zhang > >> 在 2018年5月29日，14:22，Leslie Zhai via llvm-dev <llvm-dev at lists.llvm.org> 写道： >> >> Hi Chris, >> >> Thanks for your response! >> >> Wu Wei made this happen! and he is organizing LLVM social in

I try to code the ULS factor analysis descrbied in ftp://ftp.spss.com/pub/spss/statistics/spss/algorithms/ factor.pdf # see PP5-6 factanal.fit.uls <- function(cmat, factors, start=NULL, lower = 0.005, control = NULL, ...) { FAfn <- function(Psi, S, q) { Sstar <- S - diag(Psi) E <- eigen(Sstar, symmetric = TRUE, only.values = TRUE) e <- E$values[-(1:q)] e <-

Hope live broadcast ! <br/><br/>Thanks,<br/>yaoxiao 在 2018-05-29 15:02:30，"Leslie Zhai via llvm-dev" <llvm-dev at lists.llvm.org> 写道： > > >在 2018年05月29日 14:58, mayuyu.io 写道: >> Any hint on the date of LLVM Social ShangHai? >Wu Wei scheduled July 1th http://hellollvm.org/ > > >> >> Zhang >> >>> 在

Dear Rusers, I have some data in .csv file like "2004-8-1" and "2004-10-1", and i need to convert them into days from the origin (January 1, 1960). I have tried the function date.mmddyyyy(), but cannot get it. Anybody can show me how to handle the date data? Thanks very much! My dataset like: time 2004-8-1 2004-10-1 2001-9-1 2002-9-1 -- With Kind Regards,

Dear R users, I hope to explain the concepts of skewness and kurtosis by generating series of distributions with same skewness and different kurtosis or with same kurtosis and different skewness, but it seems that i cannot find the right functions. I have searched the mailing list, but no answers were found. Is it possible to do that in R? Which function could be used? Thanks a lot. --