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From |
Steven Samuels <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Question on ROC analysis |

Date |
Mon, 23 May 2011 18:28:56 -0400 |

Megan, I suspect an incomplete download of -somersd-. But there could also be a mistake in your code. As the FAQ request, please show us exactly what you typed, as well as what Stata responded. I misread your original question and missed the implication that lower values of x are associated with lower values of y. To get a large AUC, do the analysis with -x. Or indicate that Y is greater than the cutpoint. Either -somersd- or -roctab- will work (ylow vs -x or yhigh vs x), though -somersd- has the advantage, among others, that it takes probability weights. *********************** sysuse auto, clear gen y = 1/mpg gen ylow = y<.05 gen yhigh = 1-ylow gen x = weight gen nx = -x scatter y x roctab ylow nx, graph //graphs the curve for -x roctab ylow nx // produce the AUC for -x roctab yhigh x // same roctab ylow x // AUC for x di .50 + (.50 -r(area)) // AUC for -x /* somersd */ somersd ylow nx, tr(c) // also produces the AUC for -x somersd ylow x, tr(c) // AUC for x di .50 + (.50 - _b[x]) // also AUC for -x ********************** Steve [email protected] On May 23, 2011, at 5:29 PM, Megan Deitchler wrote: Thanks - this helped. I can now graph what I want but am still having trouble calculating the AUC. I am trying to use Roger Newson's somersd package for this, using the c transformation option. I receive the following error: tidotforsomersd(): 3499 tidottree() not found <istmt>: - function returned error Any suggestions? On Thu, May 19, 2011 at 3:19 PM, Steven Samuels <[email protected]> wrote: > > Roger -senspec- from SSC should do what you want. > > Steve > [email protected] > > On May 19, 2011, at 2:46 PM, Megan Deitchler wrote: > > I am interested in carrying out a simple two variable ROC analysis. > > I want to assess how well low values of my x variable predict a low value of > my y variable (e.g. y variable with cutoff less than 200). > > However, if I understand correctly, the conventional ROC analysis in Stata > creates the ROC by using incrementally increasing values of x to predict y. > > How do I adapt the analysis so that the AUC result I obtain will > be consistent with the relationship I am interested in quantifying? > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Question on ROC analysis***From:*Megan Deitchler <[email protected]>

**Re: st: Question on ROC analysis***From:*Steven Samuels <[email protected]>

**Re: st: Question on ROC analysis***From:*Megan Deitchler <[email protected]>

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