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. 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. 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
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, > > RobDavid Winsemius, MD West Hartford, CT
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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