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From |
Olivier Francois <ofrancois@arec.umd.edu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: problem 0 likelihood ml |

Date |
Fri, 16 Nov 2012 15:55:15 +0000 |

Nick, Maarten, Thank you very much for clarifying that. Apologies for not spotting this problem, even though I have struggled with if before. The probit now works, but I still have the problem if I list more than one technique. The code has become the following: //creating the data clear set more off set seed 12345 set obs 1000 matrix C = I(4) drawnorm z v e x, corr(C) replace z = sqrt(2)*z scalar a0 = 2 scalar a1 = -1 gen D = (a0 + a1*z > v) gen Y = 0.5 - 0.5*x + e //Probit model apture program drop myprobit program myprobit version 11.2 args lnf xgam quietly { replace `lnf' = ln(normal(-`xgam')) if $ML_y1 == 0 replace `lnf' = ln(normal(`xgam')) if $ML_y1 == 1 } end ml model lf myprobit (xgam: D = z), vce(robust) technique(nr bhhh) ml check /*It works fine with just one technique (any), but come back with the following error if I combine more than one technique: opt__eval_cycle(): 3301 subscript invalid opt__eval(): - function returned error _optimize_evaluate(): - function returned error _mopt__evaluate(): - function returned error mopt__check_test1(): - function returned error _moptimize_check(): - function returned error Mopt_check(): - function returned error <istmt>: - function returned error */ ________________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Maarten Buis [maartenlbuis@gmail.com] Sent: Friday, November 16, 2012 10:08 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: problem 0 likelihood ml On Fri, Nov 16, 2012 at 3:45 PM, Olivier Francois wrote: > I needed to create my own likelihood function and have been running into problems. I have been trying to use program and the ml commands, but there is something I must not do correctly. <snip> > if $ML_y1 == 0 { > replace `lnf' = ln(normal(-`xgam')) > } > > else if $ML_y1 == 1 { > replace `lnf' = ln(normal(`xgam')) > } The way you have used -if- means that it takes a scalar, in this case the first observation of the dependent variable, and checks whether it is 0 or 1, and applies the likelihood computation for all observations based on that test, compare -help if- with -help ifcmd-. What you want is to tell Stata to check for each observation whether the dependent variable is 0 or 1 and choose the appropriate log likelihood function accordingly. You can do that as folows: replace `lnf' = ln(normal(-`xgam')) if $ML_y1 == 0 replace `lnf' = ln(normal( `xgam')) if $ML_y1 == 1 alternatively: replace `lnf' = $ML_y1*ln(normal(`xgam') + (1-$ML_y1)*ln(normal(-`xgam')) The latter will allow you to estimate a probit and a fractional probit. Hope this helps, Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * 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/ * * 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 0 likelihood ml***From:*Olivier Francois <ofrancois@arec.umd.edu>

**Re: st: problem 0 likelihood ml***From:*Maarten Buis <maartenlbuis@gmail.com>

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