[R] HMisc/rms package questions

[David Winsemius]

On Aug 17, 2010, at 5:53 PM, Rob James wrote:

> 1) How does one capture the plots from the plsmo procedure? Simply  
> inserting a routing call to a graphical device (such as jpeg, png,  
> etc) and then running the plsmo procedure (and then dev.off()) does  
> not route the output to the file system. 1b) Related to above, has  
> anyone thought of revising the plsmo procedure to use ggplot? I'd  
> like to capture several such graphs into a faceted arrangement.

(I don't use plsmo but here's a thought.) Since the rms/Hmisc combo is  
now using lattice for some of its plotting, I wonder if you need to  
add a print call around that plsmo call?
>
> 2) The 2nd issue is more about communications than software. I have  
> developed a model using lrm() and am using plot to display the  
> model. All that is  fairly easy. However, my coauthors are used to  
> traditional methods, where baseline categories are rather broadly  
> defined (e.g. males, age 25-40, height 170-180cm, BP 120-140, etc)  
> and results are reported as odds-ratios, not as probabilities of  
> outcomes.
>
> Therefore, and understandably, they are finding the graphs which  
> arise from lrm->Predict->plot difficult to interpret. Specifically,  
> in one graph, the adjusted to population is defined one way, and in  
> another graph of the same model (displaying new predictors) there  
> will be a new  "adjusted to" population.

There is an adj.subtitle (at least I think that's its name) that lets  
you leave off those distracting annotations.

> Sometimes the adjusted populations are substantially distinct,  
> giving rise to event rates that vary dramatically across graphs.  
> This can prove challenging when trying to present the set of graphs  
> as parts of a whole.  It all makes sense; it just adds complexity to  
> introducing these new methods.

I generally make the effort to educate my audience a bit. I first get  
then to agree that sharp jumps in risk at arbitrarily defined points  
are biologically and scientifically implausible in the extreme. I then  
show them the estimates from spline fits, and then I offer them  
aggregated counts of events and exposure but emphasize I emphasize  
that the the spline fits are a better description of what happens in  
the real world.

>
> One strategy might be to manually define the baseline population  
> across graphs; this way I could attempt to impose some content- 
> specific coherence to the graphs, by selecting the baseline  
> populations. Clearly this is do-able, but I have yet to see it done.  
> I'd welcome suggestions and comments.
>

I have found the ref.zero parameter to be useful with Predict().

> Thanks,
>
> Rob

David Winsemius, MD
West Hartford, CT