I have this discussion fairly often with doctors that I work with. The issue is that you can certainly predict from a model, but you can predict on different scales. Let's consider the simpler case of just 2 outcomes (disease yes/no):
Let's say you have 4 patients that you want to predict their disease status using their symptoms and a model, on the probability scale patient A is predicted to have 5% chance of yes, patient B is 49%, patient C is 51% and patient D is 95% probability of yes. If we collapse this to just a prediction of yes/no then that means that we will treat A and B the same with a prediction of NO and patients C and D the same with a prediction of YES. But does it really make sense to treat B and C so differently (they are only 2 percentage points different) while treating them the same as A or D? If I were one of the patients I would want to know whether my probability of disease was 51% or 95%, not just a yes. With 3 groups wouldn't you want to know the difference between 33%, 33%, 34% and 2%, 8%, 90%? -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.s...@imail.org 801.408.8111 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r- > project.org] On Behalf Of peterfran...@me.com > Sent: Friday, October 01, 2010 8:23 AM > To: Frank Harrell > Cc: r-help@r-project.org > Subject: Re: [R] Interpreting the example given by Frank Harrell in the > predict.lrm {Design} help > > The reason I am trying to assign them is because I have a data set > where i have arrived at the most likely model that describes the data > and now I have another dataset where I know the factors but not the > response. > > Therefore, surely I need to assign the predicted values to a response > in order to say something like: > > Based on the model I believe unknown 1 is good, where as unknown 2 is > very good etc? > > Maybe I am missing something or using the wrong approach but I thought > the main purpose of using the predict function on new data was to > "predict" the response? > > Peter > > On 1 Oct 2010, at 14:51, Frank Harrell <f.harr...@vanderbilt.edu> > wrote: > > > > > Why assign them at all? Is this a "forced choice at gunpoint" > problem? > > Remember what probabilities mean. > > > > Frank > > > > ----- > > Frank Harrell > > Department of Biostatistics, Vanderbilt University > > -- > > View this message in context: > http://r.789695.n4.nabble.com/Interpreting-the-example-given-by-Frank- > Harrell-in-the-predict-lrm-Design-help-tp2883311p2909713.html > > Sent from the R help mailing list archive at Nabble.com. > > > > ______________________________________________ > > R-help@r-project.org mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.