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    <title>topic Re: How GCONV work ? proc logistic data.. in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168748#M8815</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Doc: &lt;A href="http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect040.htm"&gt;Receiver Operating Characteristic Curves&lt;/A&gt;see "ROC Computations"&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Thu, 21 Aug 2014 17:40:59 GMT</pubDate>
    <dc:creator>Rick_SAS</dc:creator>
    <dc:date>2014-08-21T17:40:59Z</dc:date>
    <item>
      <title>How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168738#M8805</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Hi,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;I want to learn how gconv model statement option work ? I think it round data but how ? İt's default value 1E-8. What is that mean? Is there anyone to explain me with simple examples?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Thanks.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;I am giving a proc logistic output :&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;The LOGISTIC Procedure&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Model Information&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Data Set TMP1.HSB2&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Response Variable ses&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Number of Response Levels 3&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Number of Observations 200&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Model cumulative logit&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Optimization Technique Fisher's scoring&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Response Profile&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Ordered Total&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Value ses Frequency&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; 1 3 58&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; 2 2 95&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; 3 1 47&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Probabilities modeled are cumulated over the lower Ordered Values.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; &lt;SPAN style="font-weight: inherit; font-style: inherit; font-size: 12pt; font-family: inherit; color: #993366;"&gt;&lt;STRONG style="font-style: inherit; font-family: inherit;"&gt;Model Convergence Status&lt;/STRONG&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&lt;SPAN style="font-weight: inherit; font-style: inherit; font-size: 12pt; font-family: inherit; color: #993366;"&gt;&lt;STRONG style="font-style: inherit; font-family: inherit;"&gt;&amp;nbsp; Convergence criterion (GCONV=1E-8) satisfied.&lt;/STRONG&gt;&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Score Test for the Proportional Odds Assumption&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Chi-Square DF Pr &amp;gt; ChiSq&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; 2.1498 3 0.5419&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Model Fit Statistics&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Intercept&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Intercept and&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Criterion Only Covariates&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;AIC 425.165 399.605&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;SC 431.762 416.096&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;-2 Log L 421.165 389.605&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Testing Global Null Hypothesis: BETA=0&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Test Chi-Square DF Pr &amp;gt; ChiSq&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Likelihood Ratio 31.5604 3 &amp;lt;.0001&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Score 28.9853 3 &amp;lt;.0001&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Wald 29.0022 3 &amp;lt;.0001&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Analysis of Maximum Likelihood Estimates&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Standard Wald&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Parameter DF Estimate Error Chi-Square Pr &amp;gt; ChiSq&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Intercept 3 1 -5.1055 0.9226 30.6238 &amp;lt;.0001&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Intercept 2 1 -2.7547 0.8607 10.2431 0.0014&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;science 1 0.0300 0.0159 3.5838 0.0583&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;socst 1 0.0532 0.0149 12.7778 0.0004&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;female 1 -0.4824 0.2785 3.0004 0.0832&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Odds Ratio Estimates&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;&amp;nbsp; Point 95% Wald&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Effect Estimate Confidence Limits&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;science 1.030 0.999 1.063&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;socst 1.055 1.024 1.086&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;female 0.617 0.358 1.066&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Association of Predicted Probabilities and Observed Responses&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Percent Concordant 68.1 Somers' D 0.368&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Percent Discordant 31.3 Gamma 0.370&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Percent Tied 0.6 Tau-a 0.235&lt;/P&gt;&lt;P style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;Pairs 12701 c 0.684&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 20 Aug 2014 11:19:06 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168738#M8805</guid>
      <dc:creator>Mr_Nobody</dc:creator>
      <dc:date>2014-08-20T11:19:06Z</dc:date>
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    <item>
      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168739#M8806</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;GCONV specifies a relative gradient (a quadratic form involving the Hessian matrix) convergence criterion.&amp;nbsp; The formula can be found in the Shared Concepts and Topics&amp;gt;NLOPTIONS Statement documentation.&amp;nbsp; Essentially, once the relative change in the gradient of the likelihood function stabilizes (change is less than 1e-8 for the default setting), the iterative process stops, and final estimates, tests, etc. are computed.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steve Denham&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 20 Aug 2014 11:26:47 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168739#M8806</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2014-08-20T11:26:47Z</dc:date>
    </item>
    <item>
      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168740#M8807</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thanks for the replay. I have read documentation. But I didint understand &lt;img id="smileysad" class="emoticon emoticon-smileysad" src="https://communities.sas.com/i/smilies/16x16_smiley-sad.png" alt="Smiley Sad" title="Smiley Sad" /&gt;&lt;/P&gt;&lt;P&gt; If the value is less than 1e-8 then it is changed? İf it is true how it is changed? &lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 20 Aug 2014 11:42:38 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168740#M8807</guid>
      <dc:creator>Mr_Nobody</dc:creator>
      <dc:date>2014-08-20T11:42:38Z</dc:date>
    </item>
    <item>
      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168741#M8808</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;If the gradient is zero, then the response surface is at a stationary point (minimum, maximum or saddle-point).&amp;nbsp; The fit of the model is not improved by moving in any direction in the parameter space, and the iterative process stops.&amp;nbsp; No changes or rounding of data.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steve Denham&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 20 Aug 2014 11:46:51 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168741#M8808</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2014-08-20T11:46:51Z</dc:date>
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    <item>
      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168742#M8809</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I am so sorry for my bad English, The last time help me again. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;suppose that, I have 2 rows of data. if the real_score_col = 0 then &lt;/P&gt;&lt;P&gt;model_score_col &lt;/P&gt;&lt;P&gt;0,006828647300000000&lt;/P&gt;&lt;P&gt;0,001962600300000000&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;then if the real_score_col = 1 then &lt;/P&gt;&lt;P&gt;model_score_col &lt;/P&gt;&lt;P&gt;0,012321732800000000&lt;/P&gt;&lt;P&gt;0,049357227400000000&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;then this values to be compared. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;0,012321732800000000 &amp;lt; or = or &amp;gt; 0,006828647300000000&lt;/P&gt;&lt;P&gt;0,012321732800000000 &amp;lt; or = or &amp;gt; 0,001962600300000000&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;0,049357227400000000 &amp;lt; or = or &amp;gt; 0,006828647300000000&lt;/P&gt;&lt;P&gt;0,049357227400000000 &amp;lt; or = or &amp;gt; 0,001962600300000000&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Then İf the gconv = 1e-8 (default) this how to do this comparison ? how data will be changed ? I am so sorry ,I didn't understant your explanation and want to give an example. &lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 20 Aug 2014 12:16:25 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168742#M8809</guid>
      <dc:creator>Mr_Nobody</dc:creator>
      <dc:date>2014-08-20T12:16:25Z</dc:date>
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      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168743#M8810</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;The data will never be "changed".&amp;nbsp; GCONV has nothing to do with the values you presented, which I see as score values from the logistic regression.&amp;nbsp; It has to do with the derivative with respect to the parameters of the log likelihood function.&amp;nbsp; I think at this point you need to familiarize yourself with the maximum likelihood algorithm and how it works.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steve Denham&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Wed, 20 Aug 2014 12:25:10 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168743#M8810</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2014-08-20T12:25:10Z</dc:date>
    </item>
    <item>
      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168744#M8811</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi again, I searched &lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;maximum likelihood&lt;/SPAN&gt; algorithm. &lt;/P&gt;&lt;P&gt;I want to share a web page about the this problem. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;A href="http://support.sas.com/resources/papers/proceedings11/343-2011.pdf"&gt;http://support.sas.com/resources/papers/proceedings11/343-2011.pdf&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;On page 7 , the author explain why&amp;nbsp; c statistic has different values on basic calculation and proc logistic. He says "The PROC LOGISTIC may round the probabilities in a higher decimal position during pairing and counting. "&lt;/P&gt;&lt;P&gt;Maybe I associate wrong about gconv option. &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I understand this proc logistic rounding data before the compare. (data is for example: &lt;SPAN style="font-family: 'Helvetica Neue', Helvetica, Arial, 'Lucida Grande', sans-serif; background-color: #ffffff;"&gt;0,012321732800000000)&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I want to know this how proc logistic rounding data ? Why basic calculation output different from proc logistic?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Thank you so much.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 21 Aug 2014 10:59:06 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168744#M8811</guid>
      <dc:creator>Mr_Nobody</dc:creator>
      <dc:date>2014-08-21T10:59:06Z</dc:date>
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      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168745#M8812</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;This noves outside of my experience, so I'll defer to those with more practical experience with ROC curves.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Steve Denham&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 21 Aug 2014 11:48:11 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168745#M8812</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2014-08-21T11:48:11Z</dc:date>
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      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168746#M8813</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;This is getting into the ROC computations, not the optimization.&lt;/P&gt;&lt;P&gt;First divide [0,1] into 500 equal-sized bins.&amp;nbsp; By default, PROC LOGISTIC computes the c-statistic (an approximation of the area-under-the-ROC-curve) by taking the model-predicted probabilities and putting them into the appropriate bins, then it makes the concordance calculations in the documentation.&amp;nbsp; Essentially you're rounding the probabilities to the nearest 0.002.&amp;nbsp; You can change the size of the bins with the BINWIDTH= option in the MODEL statement, which will change the value of "c" because of more-or-fewer ties---if you happen to get one observation per bin, then that will give you the true value of c.&amp;nbsp; If you specify BINWIDTH=0, then instead of binning the predicted probabilities, the actual AUC computation is performed (see the "ROC Computations" of the Details section in the documentation for the equation). &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;As Steve explained, GCONV only deals with the optimization.&amp;nbsp; Since you have to search for the maximum likelihood estimator, GCONV is one way to tell when your parameter estimates are "close enough" to the optimum so you can stop the search.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 21 Aug 2014 13:28:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168746#M8813</guid>
      <dc:creator>bobderr</dc:creator>
      <dc:date>2014-08-21T13:28:19Z</dc:date>
    </item>
    <item>
      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168747#M8814</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;5 mins ago I found binwidth option make this &lt;img id="smileyhappy" class="emoticon emoticon-smileyhappy" src="https://communities.sas.com/i/smilies/16x16_smiley-happy.png" alt="Smiley Happy" title="Smiley Happy" /&gt; &lt;img id="smileyhappy" class="emoticon emoticon-smileyhappy" src="https://communities.sas.com/i/smilies/16x16_smiley-happy.png" alt="Smiley Happy" title="Smiley Happy" /&gt; and You wrote here. Thank you so much. I searched how binwidth works ? I will look at document you suggested. If you know the link of document , can you share with me ?&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 21 Aug 2014 13:36:08 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168747#M8814</guid>
      <dc:creator>Mr_Nobody</dc:creator>
      <dc:date>2014-08-21T13:36:08Z</dc:date>
    </item>
    <item>
      <title>Re: How GCONV work ? proc logistic data..</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168748#M8815</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Doc: &lt;A href="http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect040.htm"&gt;Receiver Operating Characteristic Curves&lt;/A&gt;see "ROC Computations"&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 21 Aug 2014 17:40:59 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/How-GCONV-work-proc-logistic-data/m-p/168748#M8815</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2014-08-21T17:40:59Z</dc:date>
    </item>
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