On 3/13/2011 7:43 PM, Ravi Varadhan wrote:
Like David, I too thought that `offset' is the way to do this. I was actually
in the midst of testing the differences between using `offset' and `init' when
David's email came.
Here is what I could figure out so far:
1. If you want to fix only a subset of regressors, but let others be
estimated, then you must use `offset'. The `init' approach will not work.
Yes, indeed, this is right.
2. Even when all the regressors are fixed (I have to admit that I do not see
the point of this, like David said), there seems to be a difference in using
`init' and `offset'. First of all, we cannot interpret or use the standard
errors, CIs, abd p-values when iter.max=0. Secondly, there is major
disagreement in the predictions between `offset' and `init' with no iterations.
You can run the following code to verify this:
Of course, indeed, I don't know what Angel has in mind, but there are
some cases where you might want to compute the hessian matrix at
specific values using vcov() (local approximation of the likelihood for
sensitivity analysis), and in this case the `offset' approach will not work.
ans1<- coxph(Surv(time, status) ~ age + ph.karno, data = lung, init = c(0.05,
-0.05), iter.max = 0)
ans2<- coxph(Surv(time, status) ~ offset(0.05*age) + offset(-0.05*ph.karno),
data = lung)
lp1<- predict(ans1, type="lp")
lp2<- predict(ans2, type="lp")
all.equal(lp1, lp2)
all.equal(lp1, lp2)
[1] "Mean relative difference: 1.463598"
in fact both are the same, only in the first one they are centered, e.g.,
ans1$linear.predictors
ans2$linear.predictors - mean(ans2$linear.predictors)
The results from `offset' are correct, i.e. lp2 can be readily verified to be
equal to 0.05 * (age - ph.karno). I don't know how lp1 is computed.
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
----- Original Message -----
From: David Winsemius<dwinsem...@comcast.net>
Date: Sunday, March 13, 2011 2:29 pm
Subject: Re: [R] using pre-calculated coefficients and LP in coxph()?
To: Dimitris Rizopoulos<d.rizopou...@erasmusmc.nl>
Cc: r-help@r-project.org, Angel Russo<angerusso1...@gmail.com>
On Mar 13, 2011, at 1:32 PM, Dimitris Rizopoulos wrote:
>probably you want to use the 'init' argument and 'iter.max'
control-argument of coxph(). For example, for the Lung dataset, we fix
the coefficients of age and ph.karno at 0.05 and -0.05, respectively:
>
>library(survival)
>
>coxph(Surv(time, status) ~ age + ph.karno, data = lung,
> init = c(0.05, -0.05), iter.max = 0)
>
>
>I hope it helps.
>
>Best,
>Dimitris
>
>
>On 3/13/2011 6:08 PM, Angel Russo wrote:
>>I need to force a coxph() function in R to use a pre-calculated set
of beta
>>coefficients of a gene signature consisting of xx genes and the gene
>>expression is also provided of those xx genes.
I would have guessed (and that is all one can do without an example
and better description of what the setting and goal might be) that the
use of the offset capablity in coxph might be needed.
--
David.
>>
>>If I try to use "coxph()" function in R using just the gene
expression data
>>alone, the beta coefficients and coxph$linear.predictors will
change and I
>>need to use the pre-calcuated linear predictor not re-computed
using coxph()
>>function. The reason is I need to compute a quantity that uses as
it's input
>>the coxph() output but I need this output to be pre-calculated
>>beta-coefficients and linear.predictor.
>>
>>Any one can show me how to do this in R?
>>
>>Thanks a lot.
David Winsemius, MD
West Hartford, CT
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
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