t*
*Yao Zhu*
*Department of UrologyFudan University Shanghai Cancer CenterShanghai,
China*
2014/1/5 Michael Friendly
> On 1/4/2014 12:39 AM, zhu yao wrote:
>
>> Dear Sir
>> Many papers calculated the p value of trends for odds ratios of ordered
>> category variables. I hav
Thanks peter.
*Yao Zhu*
*Department of UrologyFudan University Shanghai Cancer CenterShanghai,
China*
2014/1/5 peter dalgaard
>
> On 04 Jan 2014, at 13:56 , zhu yao wrote:
>
> > Thanks for the suggestion.
> > The results is presented in following table. The authors
I have use the plot(summary(cph-fit)) to plot the hazard ratio plot.
*Can I change the reference level in the plot, for example, use [1999,
2002) as the reference level?*
*Thanks*
*Yao Zhu*
*Department of Urology Fudan University Shanghai Cancer CenterShanghai,
China*
[[alternative HTM
Dear Sir
Many papers calculated the p value of trends for odds ratios of ordered
category variables. I have found the tabodds command in Stata. But how to
do it in R?
Thanks
*Yao Zhu*
*Department of UrologyFudan University Shanghai Cancer CenterShanghai,
China*
[[alternative HTML versio
Thank you very much for the reference about methodology.
I will try the GAMLESS package.
*Yao Zhu*
*Department of Urology
Fudan University Shanghai Cancer Center
Shanghai, China*
2013/2/11 David Winsemius
>
> On Feb 10, 2013, at 11:36 PM, zhu yao wrote:
>
> Dear R-users
>
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 del
Thanks Frank
I got the predicted probability.
But can I get the bootstrap corrected probability for individual subject.
for instance, I can get predicted probability from predict(fit,
type="fitted"). Is there similar one to retrieve the bootstrap corrected
probability for individual subject.
TH
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
Nomogram is user-friendly, but the equation is also acceptable. It should be
kept in mind that the process of model development really counts.
BTW: You can calculate C-index (AUC) in SPSS. Calibration plot can also be
plotted (may be manually) from the result of SPSS.
*Yao Zhu*
*Department of Uro
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*
error in pre[sam[[j]], i] <- predict(fit, data = dat[sam[[j]], ]) :
*
Yao Zhu*
*Department of Urology
Fudan University Shanghai Cancer Center
Shanghai, China*
2011/6/20 Jim Holtman
> what was the error?
>
> Sent from my iPad
>
> On Jun 20, 2011, at 1:34, zhu yao wrote:
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)
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
e
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('
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,
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
stron
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."
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
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]]
THX
Yao Zhu
Department of Urology
Fudan University Shanghai Cancer Center
Shanghai, China
2010/6/4 Frank E Harrell Jr
> On 06/03/2010 11:32 AM, Joris Meys wrote:
>
>> You're right, it is the same. using I() won't work for the same reason
>> sqrt
>> don't, so :
>>
>> x2<- x^2
>>> lrm(y~x+x2)
Dear r users
I have a question in coding continuous variables in logistic regression.
When "rcs" is used in transforming variables, sometime it gives implausible
associations with the outcome although the model x2 is high.
So what's your tips and tricks in coding continuous variables.
P.S. How
Totally agreed.
Nomogram (or rank table, score table) is just a nice presentation of the
model. The development and evaluation of the model is the one that matters.
In order to develop a good model, you have to know how to manipulate the
data, how to make your model better and suitable. These work
epartment of Urology
Fudan University Shanghai Cancer Center
Shanghai, China
Yao Zhu
Department of Urology
Fudan University Shanghai Cancer Center
Shanghai, China
2010/1/21 zhu yao
> Hi, everyone:
>
> I ask for help about translating a stata program into R.
>
> The program perf
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
x27;s Republic of
> China.936;LC_MONETARY=Chinese (Simplified)_People's Republic of
> China.936;LC_NUMERIC=C;LC_TIME=Chinese (Simplified)_People's Republic
> of China.936
>
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
>
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 versi
of free-range humans) but that risk then doubles each decade between
> ages 45 and 60 but the rise tapers off somewhat at the extremes of age.
> Those features are typical for human mortality and would not require as much
> commentary.
>
> --
> David
>
> On Aug 1, 2009, at 8:53
Thx for your reply.
In this example, age was transformed with rcs. So the output was different
between f and summary(f).
If I need to publicate the results, how do I explation the hazard ratio of
age?
2009/8/1 David Winsemius
>
> On Jul 31, 2009, at 11:24 PM, zhu yao wrote:
>
> C
Could someone explain the summary(cph.object)?
The example is in the help file of cph.
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n,
rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='F
http://i3.6.cn/cvbnm/a9/c8/8b/c01aad248a0b4ae6ef677600614bd4fa.jpg
2009/7/26 Frank E Harrell Jr
>
> zhu yao wrote:
>>
>> 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.
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 o
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