On Fri, 19 Sep 2014, Artur Bala wrote: I assume that by "threshold" you mean "the value of P(x'b) at which we shwitch from predicting a 0 to predicting a 1". I haven't looked at the source code, but I'm pretty sure it's 0.5. Why should it be anything else? Try this: <hansl> open mroz87 --quiet logit LFP const WA WE KL6 series pred = $yhat>0.5 xtab LFP pred </hansl> ------------------------------------------------------- Riccardo (Jack) Lucchetti Dipartimento di Scienze Economiche e Sociali (DiSES) Università Politecnica delle Marche (formerly known as Università di Ancona) r.lucchetti(a)univpm.it http://www2.econ.univpm.it/servizi/hpp/lucchetti -------------------------------------------------------

Thank you Jack and Allin, Jack's right asking "Why should it be anything else?" but I do not have the answer actually. I read in the litterature that the 0.5 probability threshold looks somewhat arbitrary and I was guessing may be gretl is performing based any other criteria, such as the ROC Curve, in order to maximize the % of correctly predicted cases. Stata has some in-build commands for this purpose and the ROC curves are generally used to compare the performance of different binary dependent variable models. As an illustration I expanded Jack's script and found that maximizing the % of correctly predicted occurs at a threshold of 0.56. Best, Artur <hansl> open mroz87 --quiet logit LFP const WA WE KL6 # actual values genr actual_0 = sum(LFP=0) genr actual_1 = sum(LFP=1) # compute: sensitivity, specificity, 1-specificity, % of correctly predicted, threshold matrix result=zeros(98,5) cnames = " sensitivity specificity 1-specificity correctly_predicted threshold" colnames(result, cnames) scalar count = 1 loop for (threshold=0.01; threshold<=.99; threshold+=.01) --quiet series predict = $yhat>threshold correct_0 = sum(predict=0 && LFP=0) correct_1 = sum(predict=1 && LFP=1) result[count,1]= correct_1/actual_1 # true positive; sensitivity result[count,2]= correct_0/actual_0 # true negative; specificity result[count,3]= 1-result[count,2] # 1 - specificity result[count,4]= (correct_0 + correct_1)/$nobs # % of correctly predicted result[count,5]= threshold count+=1 endloop gnuplot 1 3 --matrix=result --with-lines --suppress-fitted --output=display {set title 'ROC Curve'; set xrange [-0.01:1.01]; set yrange [-0.01:1.01]; set grid; show grid} gnuplot 4 5 --matrix=result --with-lines --suppress-fitted --output=display {set title 'Correctly predicted %'; set grid; show grid} </hansl> 2014-09-19 19:30 GMT+02:00 Riccardo (Jack) Lucchetti <r.lucchetti(a)univpm.it&gt; :

That's fine! The question will be settled ASAP in the coming days... ... what brings me back to some other old pieces of script I developed for survey data analysis (kinds of "svy_" Stata commands) which can potentially have a new life into a function package. Best, Artur