Thanks for the answer.
Unfortunately, I'm not yet skilled enough to do such a thing. I had a look on
the code and I'll try to understand it, as a good exercise.
I thought about sending fake fit objects to nomogram() derived from the
original one :
- orignal : f2<- cph(Surv(d.time,death) ~
sex*(rcs(cholesterol,4)+blood.pressure)
- manually derived :
* fMale : with coef rcs(cholesterol,4) and blood.pressure form f2, no sex effect
* fFemale : with "agregated" coef sex:rcs(cholesterol,4) for cholesterol and
sex:blood.pressure for BP and an obligatory sex effect.
But I failed to fool your function. Had to try though...
Marc
----- Message d'origine ----
De : Frank E Harrell Jr <f.harr...@vanderbilt.edu>
À : Marc Carpentier <marc.carpent...@ymail.com>
Cc : r-help-request Mailing List <r-help@r-project.org>
Envoyé le : Jeu 20 mai 2010, 15h 30min 27s
Objet : Re: Re : Re : [R] Nomogram with multiple interactions (package rms)
On 05/20/2010 01:42 AM, Marc Carpentier wrote:
> Thank you for your responses, but I don't think you're right about the doc...
> I carefully looked at it before posting and ran the examples, looked in
> Vanderbilt Biostat doc, and just looked again example(nomogram) :
> 1st example : categorical*continous : two axes for each sex
> f<- lrm(y ~ lsp(age,50)+sex*rcs(cholesterol,4)+blood.pressure)
Hi Marc,
My apologies; I misread my own example. This will take some digging
into the code. If you have time to do this before I do, code change
suggestions welcomed.
Frank
>
>
> 2nd : continous*continous : one "age" axe for each specified value of
> cholesterol
> g<- lrm(y ~ sex + rcs(age,3)*rcs(cholesterol,3))
>
> 3rd and 4th : categorical*continous : two axes for each sex (4th with fun)
> f<- psm(Surv(d.time,death) ~ sex*age, dist='lognormal')
>
> 5th : categorical*continous : two axes for each sex (with fun)
> g<- lrm(Y ~ age+rcs(cholesterol,4)*sex)
>
> I'm desperately trying to represent a case of
> categorical*(continous+continous) :
> f2<- cph(Surv(d.time,death) ~ sex*(rcs(cholesterol,4)+blood.pressure)
> The best solution I can think of is to draw one nomogram for each sex :
> Assuming 'male' is the ref level of sex :
> 1st nomogram : one axe for rcs(cholesterol,4), one axe for blood.pressure
> 2nd nomogram : one axe for sex:rcs(cholesterol,4), one axe for
> sex:blood.pressure, both shifted because of the sex own effect.
> (I badly draw it in my previous mail)
> I didn't see any example of this "adjustement" of nomogram to 'male' or
> 'female'...
>
> I hope I gave a clearer explanation and I'm not wrong about this unmentioned
> case.
>
> Marc
>
>
>
>
> ----- Message d'origine ----
> De : Frank E Harrell Jr<f.harr...@vanderbilt.edu>
> À : Marc Carpentier<marc.carpent...@ymail.com>
> Cc : r-help-request Mailing List<r-help@r-project.org>
> Envoyé le : Jeu 20 mai 2010, 0h 55min 32s
> Objet : Re: Re : [R] Nomogram with multiple interactions (package rms)
>
> On 05/19/2010 04:36 PM, Marc Carpentier wrote:
>> I'm sorry. I don't understand the "omit" solution, and maybe I mislead you
>> with my explanation.
>>
>> With the data from the "f" exemple of nomogram() :
>> Let's declare :
>> f2<- cph(Surv(d.time,death) ~ sex*(age+blood.pressure))
>> I guess the best (and maybe the only) way to represent it with a nomogram is
>> to plot two nomograms (I couldn't find better).
>> Is there a way to have :
>>
>> Nomogram1 : "male" :
>> - points 1-100 ---------------
>> - age ("men") ---------------
>> - blood.pressure ("men") ---------------
>> - linear predictor ---------------
>>
>> And nomogram2 : "female" :
>> - points 1-100 ---------------
>> - age ("female") ---------------
>> - blood.pressure ("female") ---------------
>> - linear predictor ---------------
>>
>> As I said I tried and failed (nomogram() still wants me to define
>> interact=list(...)) with :
>> plot(nomorgam(f2, adj.to=list(sex="male")) #and "female" for the other one
>>
>> Marc
>
> I think the documentation tells you how to do this. But you failed to
> look at the output from example(nomogram). In one of the examples two
> continuous predictors have two axes each, with male and female in close
> proximity. Or maybe I'm just missing your point.
>
> Frank
>
>>
>>
>>
>> ----- Message d'origine ----
>> De : Frank E Harrell Jr<f.harr...@vanderbilt.edu>
>> À : Marc Carpentier<marc.carpent...@ymail.com>; r-help-request Mailing
>> List<r-help@r-project.org>
>> Envoyé le : Mer 19 mai 2010, 22h 28min 51s
>> Objet : Re: [R] Nomogram with multiple interactions (package rms)
>>
>> On 05/19/2010 03:17 PM, Marc Carpentier wrote:
>>> Dear list, I'm facing the following problem : A cox model with my sex
>>> variable interacting with several continuous variables :
>>> cph(S~sex*(x1+x2+x3)) And I'd like to make a nomogram. I know it's a
>>> bit tricky and one mights argue that nomogram is not a good a
>>> choice... I could use the parameter
>>> interact=list(sex=("male","female"),x1=c(a,b,c))... but with rcs or
>>> pol transformations of x1, x2 and x3, the choice of the
>>> categorization (a,b,c,...) is arbitrary and the nomogram not so
>>> useful... Considering that sex is the problem, I thought I could draw
>>> two nomograms, one for each sex... based on one model. These would be
>>> great. Do you think it's possible ?
>>
>> Yes, you can specify constant predictors not to draw with the omit=
>> argument. But try first to draw everything. Shouldn't you just get 2
>> axes each for x1 x2 x3?
>>
>>>
>>> Taking the exemple of the help of nomogram() (package "rms") : f<-
>>> psm(Surv(d.time,death) ~ sex*age, dist=if(.R.)'lognormal' else
>>> 'gaussian')
>>
>> Drop the if(.R.) which was just corrected in the documentation. Use
>> dist='lognormal'
>>
>> Frank
>>
>>>
>>> Let's add the previously defined blood.pressure effect with an
>>> interaction with sex too (with cph) : f2<- cph(Surv(d.time,death) ~
>>> sex*(age+blood.pressure))
>>>
>>> I thought of the parameter adt.to : plot(nomorgam(f2,
>>> adj.to=list(sex="male")) #and "female" for the other one
>>>
>>> But nomogram() still wants me to define interact=list(...) Thanks for
>>> any advice you might have (with adj.to or any alternative...)
>>>
>>> Marc Carpentier
>>>
>>
>>
>
>
--
Frank E Harrell Jr Professor and Chairman School of Medicine
Department of Biostatistics Vanderbilt University
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