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Kinga
‎09-01-2015
  • 3 Posts
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Re: Glimmix: Same method with different optimization results different...

by Calcite | Level 5 in Statistical Procedures
‎08-13-2014 06:29 AM
‎08-13-2014 06:29 AM
Thanks for the answer; indeed the decimal places were different. As far as I understood if I want to make them closer I should change the convergence criteria, but as I see it is not possible to define the criteria based on the change in the likelihood value. Am I right or is there any way to set convergence criteria for the change in the likelihood value (such as if it is less than 1e-6, then stop)? Thanks again. ... View more

Re: Glimmix: Same method with different optimization results different...

by Calcite | Level 5 in Statistical Procedures
‎08-12-2014 10:07 AM
‎08-12-2014 10:07 AM
Hi Steve, Thanks for the answer. Indeed I forgot to copythe most important part. Mostly the strange bit is that the log likelihood looks the same. It can be a coindidence, although it is not really possible that you get the same likelihood, but the estimates are different. That why I'm curious what can be the reason. Newton-Raphson:   Fit Statistics -2 Log Likelihood 25015.43 AIC (smaller is better) 25037.43 AICC (smaller is better) 25037.43 BIC (smaller is better) 25015.43 CAIC (smaller is better) 25026.43   Quasi-Newton: Fit Statistics -2 Log Likelihood 25015.43 AIC (smaller is better) 25037.43 AICC (smaller is better) 25037.43 BIC (smaller is better) 25015.43 CAIC (smaller is better) 25026.43 HQIC (smaller is better) 25015.43 Thanks, Kinga ... View more

Glimmix: Same method with different optimization results different est...

by Calcite | Level 5 in Statistical Procedures
‎08-12-2014 07:16 AM
‎08-12-2014 07:16 AM
Hi, I tried to figure out what can be the reason that in proc glimmix using the same ML method results different estimate and variance depending on the optimization technic. The model uses two fixed effects, two random effects and one dummy (class) variable. In both scenarios I used the Laplace method, but different optimization technic (Newton-Raphson, quasi-Newton). proc glimmix data = data method=LAPLACE; class dummy random1 random2; model flag (event ='1') = fixed1 fixed2 dummy /link=probit dist=binomial solution or; output out=glimmixout pred( blup ilink)=PredProb                      pred(noblup ilink)=PredProd_PA; random int /subject= random1 solution; random int /subject= random2 solution; nloptions om=newrap; run; proc glimmix data = data method=LAPLACE; class dummy random1 random2; model flag (event ='1') = fixed1 fixed2 dummy /link=probit dist=binomial solution or; output out=glimmixout pred( blup ilink)=PredProb                      pred(noblup ilink)=PredProd_PA; random int /subject= random1 solution; random int /subject= random2 solution; nloptions om=quanew; run; The results are slightly different: Newton-Raphson:      Intercept               _    -2.65210      0.1795        8     -14.77    0.0000004336      fixed1                  _    -.631697     0.01484    201E3     -42.56    <.0000000001      fixed2                  _    -.097351     0.01102    201E3      -8.83    <.0000000001      Dummy                   1    -.775746     0.09144    201E3      -8.48    <.0000000001      Dummy                   2    -.677288     0.05578    201E3     -12.14    <.0000000001      Dummy                   3    -.422331     0.03932    201E3     -10.74    <.0000000001      Dummy                   4    0.071574     0.02895    201E3       2.47    0.0134390943      Dummy                   5    0.800703     0.03233    201E3      24.77    <.0000000001      Dummy                   6    1.728605     0.07865    201E3      21.98    <.0000000001      Dummy                   7    0.000000           .        .        .       . Quasi-Newton:      Intercept               _    -2.65845      0.1816        8     -14.64    0.0000004659      fixed1                  _    -.632236     0.01485    201E3     -42.58    <.0000000001      fixed2                  _    -.097346     0.01102    201E3      -8.83    <.0000000001      Dummy                   1    -.774482     0.09140    201E3      -8.47    <.0000000001      Dummy                   2    -.677044     0.05579    201E3     -12.13    <.0000000001      Dummy                   3    -.421267     0.03932    201E3     -10.72    <.0000000001      Dummy                   4    0.071657     0.02896    201E3       2.47    0.0133518470      Dummy                   5    0.800642     0.03233    201E3      24.76    <.0000000001      Dummy                   6    1.728683     0.07866    201E3      21.98    <.0000000001      Dummy                   7    0.000000           .        .        .       . Is there any reason why I don’t get the same results for the two scenarios? Thanks ... View more
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