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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

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

Subject |
RE: st: keeping likelihood for every observation |

Date |
Sun, 7 Feb 2010 16:50:59 -0000 |

Here is another way to do it. Before your likelihood code, initialise a variable to save the likelihood gen ml_save = . Within your likelihood code, follow each update of `lnf' by replace ml_save = `lnf' After running your likelihood code, investigate the saved variable. This pivots on the simple idea that a permanent variable remains in memory until -drop-ped explicity. Naturally, use a name that does not clash with others, and beware that use of e.g. -preserve- might frustrate this. This will add a bit to execution time. Nick n.j.cox@durham.ac.uk Maarten buis --- On Sun, 7/2/10, Stephane Mahuteau wrote: > Could anyone let me know how I can save the log likelihood > for every observation in my dataset and keep it as a > variable? You can recreate the likelihood relatively simply with the -predictnl- command, as in the example below. Whether that is efficient depends on how you want to use that variable. *--------------------- begin example ----------------------------------- program drop _all program define mynorm_lf args lnf xb ln_sigma qui replace `lnf' = lnnormalden($ML_y1, `xb', exp(`ln_sigma')) end sysuse auto, clear ml model lf mynorm_lf (mu: mpg = foreign rep78 displacement) /ln_sigma ml check ml search ml maximize predictnl double lnf = lnnormalden(mpg,xb(#1),exp(xb(#2))) if e(sample) // check if this is the correct variable sum lnf, meanonly di r(sum) *----------------------- end example ----------------------------------- ( For more on how to use examples I sent to statalist see: http://www.maartenbuis.nl/stata/exampleFAQ.html ) * * 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: keeping likelihood for every observation***From:*"Stephane Mahuteau" <smahutea@efs.mq.edu.au>

**Re: st: keeping likelihood for every observation***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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