Have you thought about programming a function builder into the Shiny applet?
It might simplify the process for your students a bit. A highly simplified
version of the page David suggested could be built that generates the R code.
Best of luck,
Alan
-Original Message-
From: Scott Rif
Have you tried plotting obvserved survival against X*Beta? I believe the usual
predictions from a cox model are just monotonic transformations of this.
-Alan
-Original Message-
From: Bonnett, Laura [mailto:l.j.bonn...@liverpool.ac.uk]
Sent: Wed 2/8/2012 1:52 AM
To: 'David Winsemius'
Cc:
I have to disagree with what's been posted, but I think some very interesting
points have been addressed. I'd like to add my two cents.
Consider the pair {X, 1-X} where X is sampled from a uniform(0,1)
distribution. The quantity 1- X also comes from a uniform(0,1) distribution
and therefore is
I will respond off list since the issue is mostly related to SAS.
Alan Mitchell, MSc
Biostatistician
al...@crab.org
-Original Message-
From: Cheryl Johnson [mailto:johnson.cheryl...@gmail.com]
Sent: Sunday, October 30, 2011 5:02 PM
To: r-help@r-project.org
Subject: [R] How to use IML wit
?Hmisc::rcorrp.cens
-Alan
-Original Message-
From: Eik Vettorazzi [mailto:e.vettora...@uke.de]
Sent: Tue 10/11/2011 2:25 AM
To: Yujie Wang
Cc: r-help@r-project.org
Subject: Re: [R] How to test if two C statistics are significantly different?
Hi Yujie,
there is still a lot of work in p
e(x=cos(t),y=sin(t))
par(mar=c(0,0,0,0))
plot(circle$x,circle$y,type='l')
for(idx in 1:length(dist)){
t0 = seq(angle[idx],angle[idx]+10,length.out=50)*pi/180
petal0 = data.frame(x = dist[idx]*cos(t0),y = dist[idx]*sin(t0))
polygon( x=c(0,petal0$x,0),y=c(0,petal0$y,0),col=color.list
I'm not sure if there are any packages that do this, but I've created similar
plots in R. The easiest way I've found is to think in terms of a unit circle
in polar coordinates for drawing the plot.
I haven't tested the code below, but it will give you the idea.
dist=dist/9000
t = seq(0,360
>So in cuminc() function, the argument "fstatus" should be coded like:
0=censored, 1=event of interest, 2=event of competing risk. Then the
function will calculate CI for each of the 2 types of events >(event of
interest and event of competing risk), am I correct?
Correct.
>What about running regu
John,
Since death precludes recurrence, censoring deaths would violate the KM
estimator assumption that additional follow-up would eventually lead to
an event. If your goal is to estimate the probability of recurrence,
then you want CI with deaths as a competing risk. The cuminc function
in the
Hello all,
I am working on converting a set of S+ functions to R.
Can anyone tell me how to extract data from an hclust or dendrogram
object in R that is similar to that generated by
P1 = plclust(tree, plot=F)
Any suggestions would be appreciated.
Alan Mitchell, MSc
Biostati
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