[R] HMisc/rms package questions

[Frank Harrell]

Frank E Harrell Jr   Professor and Chairman        School of Medicine
                      Department of Biostatistics   Vanderbilt University

On Tue, 17 Aug 2010, 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.

Hi Rob,

plsmo in Hmisc uses base graphics, and I have captured its output many 
times using pdf() or postscript().

I'll bet that Hadley Wickham has an example that will help.  For 
lattice there is panel.plsmo.

>
> 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. 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 very simple example might help us with this one.

But odds ratios resulting from categorizing continuous variables are 
invalid.  They do not have the claimed interpretation.  In fact they 
have no interpretation in the sense that their interpretation is a 
function of the entire set of sample values.  You can get whatever 
odds ratios you need (with exact interpretations) using summary or 
contrast.  You can also modify plot to plot relative odds, relative to 
something of your choosing.

Frank

  >
> 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.
>
> Thanks,
>
> Rob
>
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