Run an ROC (receiver operating characteristic) analysis and see if your "continuous predictor" is any better than tossing a coin. See if the area under your ROC curve is significantly greater than 0
I think that the function you gave ends up looking at object sizes for character vectors representing the names of your objects, or some such funky S-like thing. This seems to work:<o:p></o:p> <o:p>
The first thing to realize is that there are two sources of deviation between the machine results and the formula. The first is systematic error. In other words, the shape of the formula’s curv
I use this kind of question as a good exercise to train my mind out of for-loop thinking into the vectorized S-plus world. Here I was half-successful, vectorizing the columns but looping over rows. E
As we wrap this up, it's worth pointing out the importance of this particular problem (and its minor variants) in clinical data analysis. The Clinical Data Standards Interchange Committee (CDISC, www
One of the difficulties of diagnosing a problem with clustering is that you have three sub-problems: 1) Choosing the right feature set 2) Choosing the right similarity measure 3) Choosing the right
Does anyone know of an R or S-Plus function to draw scaled rectangle diagrams? This variation on Venn diagrams is described here:<o:p></o:p> <o:p> </o:p> Marshall, RJ. Displaying clinical data relati
You could, if you wish, use names() to get the variable names in your data frame, use cat() with sink() to write them in the form of a formula into a little text file, and then copy/paste them into R
Your point is well taken. When Acton's quote was written, your 300 variables might come from 300 sensors on an aircraft or a chemical reactor, and there was a chance that physical and engineering ins
Dear Kamil,<o:p></o:p> <o:p> </o:p> Let me apologize publicly if you feel personally insulted. If I was warning you away from doing something I thought might be ill-advised, it’s only because I
Note that your equations are non-linear but not inherently multivariate. They can be easily transformed to a univariate form that can be solved with the uniroot() function. The following is quick-an
Uniroot() finds "a" solution in an interval, not necessarily "the" solution that you want. There may be multiple roots, some of which meet your constraints. Since you've got R or S-plus which have gr
<o:p> </o:p> Just about every command calls a function, and you can see these by using the “History” window. It looks like the command you need is:<o:p></o:p> <o:p> </o:p> guiSetOption(
The 1st Annual FDA/DIA Statistics Forum is fast approaching. Program information and registration details can be found on the DIA website at:<o:p></o:p> <o:p> </o:p> http://www.diahome.org/DIAHome/Ed