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
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