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From |
jpitblado@stata.com (Jeff Pitblado, StataCorp LP) |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: ml methods d1 and d2 and robust / clustered standard errors |

Date |
Tue, 21 Sep 2010 16:42:35 -0500 |

Timothee Carayol <timothee.carayol@gmail.com> has an -ml- method -d1- evaluator program designed prior to Stata 11, and asks why -ml method d1- no longer works with the -vce(robust)- option: > I am left rather confused with Stata's response to my attempts to > obtain robust standard errors using the -ml- command with method d1 or > d2 evaluator in Stata 11. > I tried to reproduce (word by word) the Stata 11 base reference > manual's example on this (page 1081); with no success. > > My input: > > ************ > cap program drop weib1 > program weib1 > args todo b lnf g negH g1 g2 // negH, g1, and g2 are new > tempvar t1 t2 > mleval `t1' = `b', eq(1) > mleval `t2' = `b', eq(2) > local t "$ML_y1" > local d "$ML_y2" > tempvar p M R > quietly gen double `p' = exp(`t2') > quietly gen double `M' = (`t'*exp(-`t1'))^`p' > quietly gen double `R' = ln(`t')-`t1' > mlsum `lnf' = -`M' + `d'*(`t2'-`t1' + (`p'-1)*`R') > if (`todo'==0 | `lnf'>=.) exit > tempname d1 d2 > quietly replace `g1' = `p'*(`M'-`d') /* <-- new */ > quietly replace `g2' = `d' - `R'*`p'*(`M'-`d') /* <-- > new */ > mlvecsum `lnf' `d1' = `g1', eq(1) /* <-- changed */ > mlvecsum `lnf' `d2' = `g2', eq(2) /* <-- changed */ > matrix `g' = (`d1',`d2') > end > > use http://www.stata-press.com/data/r11/cancer, clear > > gen drug2 = drug==2 > gen drug3 = drug==3 > > > ml model d1 weib1 (studytime died = drug2 drug3 age) /s, vce(robust) > ml maximize > ************* > > > Stata's output: > > option vce(robust) is not allowed with evaltype d1 > > > In my understanding, the g1 and g2 variables should allow Stata to > calculate the robust standard errors. It does work in Stata 10 and > 10.1; but fails with Stata 11 and 11.1. So I can fix it by using > -version 10-, but out of curiosity mainly I'd like to get it to work > in versions 11 and 11.1. With the release of Stata 11, -ml-'s optimization engine is implemented in Mata through the -optimize()- routines. With this change, the -d1- and -d2- evaluator methods no longer work with -vce(robust)-, -pweight-s, or -vce(cluster ...)-. The old behavior continues to work only under version control since the original -ml- engine was perserved and is callable only under version control. Although short-lived in the official on-line documentation, the -d1- and -d2- methods supporting equation-level scores were renamed to -e1- and -e2- (see -help ml_11-). Note also that the modern -ml- with -e2- will need the -negh- option because -ml- now assumes that the programmer computed second derivative matrix is the Hessian matrix instead of the negative Hessian matrix (-negh- implies that the evaluator returns the negative Hessian matrix). Timothee can use the -e1- evaluator type in Stata 11 with -weib1- above. Since the release of the updates in Stata 11.1, the official evaluator types that work with equation level scores are named short name long name --------------------------------------------------------------------- lf linearform lf0 linearform0 lf1 linearform1 lf2 linearform2 The -lf- evaluator type is as it was before. Syntactically, the -lf#- evaluators are similar to the -d#- evaluators. -lf0- is like -d0- except the evaluator is expected to return the observation-level log-likelihood values instead of the overall log-likelihood value. -lf1- is like -d1- except the evaluator is expected to return the observation-level log-likelihood values instead of the overall log-likelihood value, and equation-level score (first derivatives) variables instead of the gradient vector. -lf2- is like -d2- except the evaluator is expected to return the observation-level log-likelihood values instead of the overall log-likelihood value, equation-level score (first derivatives) variables instead of the gradient vector, and the Hessian matrix (second derivatives) Here is a version of Timothee's -weib1- program modified to be an -lf1- evaluator: ***** BEGIN: program weib_lf1 version 11.1 args todo b lnf g1 g2 tempvar t1 t2 mleval `t1' = `b', eq(1) mleval `t2' = `b', eq(2) local t "$ML_y1" local d "$ML_y2" tempvar p M R quietly gen double `p' = exp(`t2') quietly gen double `M' = (`t'*exp(-`t1'))^`p' quietly gen double `R' = ln(`t')-`t1' quietly replace `lnf' = -`M' + `d'*(`t2'-`t1' + (`p'-1)*`R') if (`todo'==0) exit quietly replace `g1' = `p'*(`M'-`d') quietly replace `g2' = `d' - `R'*`p'*(`M'-`d') end ***** END: Here is the Stata 11 version of the example Timothee posted using this new evaluator and the 'i.' operator on the -drug- factor variable: ***** BEGIN: . webuse cancer (Patient Survival in Drug Trial) . ml model lf1 weib_lf1 (studytime died = i.drug age) /s, vce(robust) maximize initial: log pseudolikelihood = -744 alternative: log pseudolikelihood = -356.14276 rescale: log pseudolikelihood = -200.80201 rescale eq: log pseudolikelihood = -136.69232 Iteration 0: log pseudolikelihood = -136.69232 (not concave) Iteration 1: log pseudolikelihood = -124.13044 Iteration 2: log pseudolikelihood = -113.99048 Iteration 3: log pseudolikelihood = -110.30732 Iteration 4: log pseudolikelihood = -110.26748 Iteration 5: log pseudolikelihood = -110.26736 Iteration 6: log pseudolikelihood = -110.26736 . ml display Number of obs = 48 Wald chi2(3) = 30.69 Log pseudolikelihood = -110.26736 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- eq1 | drug | 2 | 1.012966 .2801321 3.62 0.000 .4639171 1.562015 3 | 1.45917 .2878814 5.07 0.000 .894933 2.023407 | age | -.0671728 .0189346 -3.55 0.000 -.1042839 -.0300616 _cons | 6.060723 1.023302 5.92 0.000 4.055089 8.066358 -------------+---------------------------------------------------------------- s | _cons | .5573333 .1359451 4.10 0.000 .2908859 .8237808 ------------------------------------------------------------------------------ ***** END: --Jeff jpitblado@stata.com * * 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/

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