different aic and LL in glmer(lme4) and glimmix(SAS)?
Also...R is giving you a model with LESS deviance. So, it's doing a better job...why would you want to reproduce SAS? :) --Adam
On Thu, 1 Jul 2010, Jeffrey Evans wrote:
Good question. They are similar Compare models with nested fixed effects structures Full model = lnsdlmaxd + lnadultssdld + lnsdlmaxd:lnadultssdld Reduced model = lnsdlmaxd + lnadultssdld AIC R SAS Full 1150 1663.9 Reduced 1159 1673.4 ------------------------------- deltaAIC 9 9.5
________________________________ From: almost at gmail.com [mailto:almost at gmail.com] On Behalf Of Andy Fugard Sent: Thursday, July 01, 2010 12:16 PM To: Jeffrey Evans Cc: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] different aic and LL in glmer(lme4) and glimmix(SAS)? Hi Jeff, Can't answer the question, but out of interest, what happens when you compare nested models in R and SAS, e.g., models with and without the lnsdlmaxd:lnadultssdld interaction? Would be interesting to see the log-likehood ratio (and maybe /change/ in AIC and BIC between the models). Cheers, Andy On Thu, Jul 1, 2010 at 18:03, Jeffrey Evans <Jeffrey.Evans at dartmouth.edu> wrote: Hello All, I have read several posts related to this previously, but haven't found any resolution yet. When running the same GLMM in glmer and in SAS PROC GLIMMIX, both programs return comparable parameter estimates, but wildly different likelihoods and AIC values. In SAS I specify use of the Laplace approximation. In R, I believe this is the default (no?). What's the difference, and [how] can I reproduce the SAS -2ll in glmer? Thanks, Jeff \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/ > R_GLMM = glmer(cbind(SdlFinal, SdlMax-SdlFinal) ~ lnsdlmaxd*lnadultssdld + (1|ID),data=sdlPCAdat,family="binomial") > R_GLMM Generalized linear mixed model fit by the Laplace approximation Formula: cbind(SdlFinal, SdlMax - SdlFinal) ~ lnsdlmaxd * lnadultssdld + (1 | ID) Data: sdlPCAdat AIC BIC logLik deviance 1150 1165 -570 1140 <------------------ this line!! Random effects: Groups Name Variance Std.Dev. ID (Intercept) 1.2491 1.1176 Number of obs: 144, groups: ID, 48 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 4.56964 0.43148 10.591 < 2e-16 *** lnsdlmaxd -0.65936 0.05686 -11.595 < 2e-16 *** lnadultssdld -0.64534 0.15861 -4.069 4.73e-05 *** lnsdlmaxd:lnadultssdld 0.07393 0.02166 3.414 0.00064 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) lnsdlm lndlts lnsdlmaxd -0.923 lnadltssdld -0.461 0.479 lnsdlmxd:ln 0.482 -0.508 -0.994 \/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/ title 'SAS GLMM'; proc glimmix data=sdlPCAdat ic=pq noitprint method=laplace; class site id; model sdlfinal/sdlmax = lnsdlmaxd|lnadultssdld/ solution dist=binomial; random ID /; covtest glm/wald; run; ////////////////////////////////////////////////////////////////////// SAS GLMM 19:36 Wednesday, June 30, 2010 88 The GLIMMIX Procedure Data Set WORK.SDLPCADAT Response Variable (Events) SdlFinal Response Variable (Trials) SdlMax Response Distribution Binomial Link Function Logit Variance Function Default Variance Matrix Not blocked Estimation Technique Maximum Likelihood Likelihood Approximation Laplace Degrees of Freedom Method Containment Optimization Information Optimization Technique Dual Quasi-Newton Parameters in Optimization 5 Lower Boundaries 1 Upper Boundaries 0 Fixed Effects Not Profiled Starting From GLM estimates Convergence criterion (GCONV=1E-8) satisfied. Fit Statistics -2 Log Likelihood 1653.90 <------------------ this line!! AIC (smaller is better) 1663.90 AICC (smaller is better) 1664.33 BIC (smaller is better) 1673.25 CAIC (smaller is better) 1678.25 HQIC (smaller is better) 1667.43 Fit Statistics for Conditional Distribution -2 log L(SdlFinal | r. effects) 1436.44 Pearson Chi-Square 908.07 Pearson Chi-Square / DF 6.31 Covariance Parameter Estimates Cov Standard Z Parm Estimate Error Value Pr > Z ID 1.2491 0.2746 4.55 <.0001 Solutions for Fixed Effects Standard Effect Estimate Error DF t Value Pr > |t| Intercept 4.5696 0.4333 47 10.55 <.0001 lnsdlmaxd -0.6594 0.05717 93 -11.53 <.0001 lnadultssdld -0.6453 0.1593 93 -4.05 0.0001 lnsdlmaxd*lnadultssd0.07394 0.02174 93 3.40 0.0010 _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models _______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models