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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: problem with predicted probabilities |

Date |
Tue, 04 Feb 2014 08:17:22 -0500 |

My view of the classification table is slightly different; I certainly agree that automatically using a cutoff of .5 is not always a good idea; in particular, if the prevalence of the event in the data is very different from .5 (e.g., .12 or .88 or ...), it is a bad idea; as an alternative, use the cutoff option and start by using the prevalence as a cutoff; substantive experts in your area may suggest other reasonable cutoffs Rich On 2/4/14, 12:04 AM, Witness Chirinda wrote: > Thanks Nick and Richard for you help! > > On Sun, Feb 2, 2014 at 11:23 PM, Richard Williams > <richardwilliams.ndu@gmail.com> wrote: >> Getting every case classified as 0 (or 1) is not unusual. For relatively >> rare events, the highest predicted probability for every case may be less >> than .5, so every case gets classified as 0. My own experience is that the >> classification table tends not to be that helpful, especially for events >> that are very rare or very common. >> >> >> At 04:58 AM 2/2/2014, Witness Chirinda wrote: >>> >>> Dear Statalist >>> I want to obtain some predicted probabilities after logistic >>> regression, as attached. I want to use the predicted probabilities in >>> my next step instead of observed prevalence since the latter are >>> adjusted for other (socio-demographic) factors. >>> My problem is that the when I run - estat classification- it giving 0s >>> for + classification. I am sure I am doing it the wrong way somewhere. >>> Please see below output. All variables used in the model have been >>> recorded to be binary 1/0 >>> >>> Thanks for any help! >>> ------------------ >>> >>> >>> . logistic Health_stat age maried wealth educat place sex >>> >>> Logistic regression Number of obs = >>> 2339 >>> LR chi2(6) >>> = 50.61 >>> Prob > chi2 >>> = 0.0000 >>> Log likelihood = -996.02516 Pseudo R2 = >>> 0.0248 >>> >>> ------------------------------------------------------------------------------ >>> Health_stat | Odds Ratio Std. Err. z P>|z| [95% >>> Conf. Interval] >>> >>> -------------+---------------------------------------------------------------- >>> age | 1.109083 .0342696 3.35 0.001 1.043909 1.178326 >>> maried | 1.2134 .1962535 1.20 0.232 .8837556 1.666004 >>> Wealth | 1.430957 .1784661 2.87 0.004 1.120641 1.827203 >>> educat | 1.670411 .2010455 4.26 0.000 1.319397 2.11481 >>> place | .9334522 .1223134 -0.53 0.599 .7220318 1.206779 >>> sex | 1.129008 .1324642 1.03 0.301 .8970722 1.420911 >>> >>> . estat class >>> >>> Logistic model for poorSRHS >>> -------- True -------- >>> Classified | D ~D | Total >>> -----------+--------------------------+----------- >>> + | 0 0 | 0 >>> - | 370 1969 | 2339 >>> -----------+--------------------------+----------- >>> Total | 370 1969 | 2339 >>> >>> Classified + if predicted Pr(D) >= .5 >>> True D defined as poorSRHS != 0 >>> -------------------------------------------------- >>> Sensitivity Pr( +| D) 0.00% >>> Specificity Pr( -|~D) 100.00% >>> Positive predictive value Pr( D| +) .% >>> Negative predictive value Pr(~D| -) 84.18% >>> -------------------------------------------------- >>> False + rate for true ~D Pr( +|~D) 0.00% >>> False - rate for true D Pr( -| D) 100.00% >>> False + rate for classified + Pr(~D| +) .% >>> False - rate for classified - Pr( D| -) 15.82% >>> -------------------------------------------------- >>> Correctly classified 84.18% >>> -------------------------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: problem with predicted probabilities***From:*Witness Chirinda <witchirinda@gmail.com>

**Re: st: problem with predicted probabilities***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**Re: st: problem with predicted probabilities***From:*Witness Chirinda <witchirinda@gmail.com>

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