These actually aren't terribly different from each other. I suspect that lmer is slightly closer to the correct answer, because lme reports a "log-likelihood" (really -1/2 times the REML criterion) of 49.30021, while lmer reports a REML criterion of -98.8 -> slightly better fit at -R/2 = 49.4. The residual sds are 0.0447 (lme) vs. 0.0442 (lmer); the intercept sd estimate is 0.016 vs 0.0089, admittedly a bit low, and both month sds are very small. lmer indicates a singular fit (correlation of -1). If you look at the confidence intervals on these estimates (confint(fitted_model) in lme4; intervals(fitted_model) in lme) I think you'll find that the confidence intervals are much wider than these differences (you may even find that lme reports that it can't give you the intervals because the Hessian [curvature] matrix is not positive definite).
Both should be comparable to SAS PROC MIXED results, I think, if you get the syntax right ... On Tue, May 26, 2015 at 7:09 PM, li li <hannah....@gmail.com> wrote: > Hi all, > I am fitting a random slope and random intercept model using R. I > used both lme and lmer funciton for the same model. However I got > different results as shown below (different variance component > estimates and so on). I think that is really confusing. They should > produce close results. Anyone has any thoughts or suggestions. Also, > which one should be comparable to sas results? > Thanks! > Hanna > > ## using lme function >> mod_lme <- lme(ti ~ type*months, random=~ 1+months|lot, na.action=na.omit, > + data=one, control = lmeControl(opt = "optim")) >> summary(mod_lme) > Linear mixed-effects model fit by REML > Data: one > AIC BIC logLik > -82.60042 -70.15763 49.30021 > > Random effects: > Formula: ~1 + months | lot > Structure: General positive-definite, Log-Cholesky parametrization > StdDev Corr > (Intercept) 8.907584e-03 (Intr) > months 6.039781e-05 -0.096 > Residual 4.471243e-02 > > Fixed effects: ti ~ type * months > Value Std.Error DF t-value p-value > (Intercept) 0.25831245 0.016891587 31 15.292373 0.0000 > type 0.13502089 0.026676101 4 5.061493 0.0072 > months 0.00804790 0.001218941 31 6.602368 0.0000 > type:months -0.00693679 0.002981859 31 -2.326329 0.0267 > Correlation: > (Intr) typPPQ months > type -0.633 > months -0.785 0.497 > type:months 0.321 -0.762 -0.409 > > Standardized Within-Group Residuals: > Min Q1 Med Q3 Max > -2.162856e+00 -1.962972e-01 -2.771184e-05 3.749035e-01 2.088392e+00 > > Number of Observations: 39 > Number of Groups: 6 > > > > > ###Using lmer function >> mod_lmer <-lmer(ti ~ type*months+(1+months|lot), na.action=na.omit, >> data=one) >> summary(mod_lmer) > Linear mixed model fit by REML t-tests use Satterthwaite approximations to > degrees of freedom [merModLmerTest] > Formula: ti ~ type * months + (1 + months | lot) > Data: one > > REML criterion at convergence: -98.8 > > Scaled residuals: > Min 1Q Median 3Q Max > -2.1347 -0.2156 -0.0067 0.3615 2.0840 > > Random effects: > Groups Name Variance Std.Dev. Corr > lot (Intercept) 2.870e-04 0.0169424 > months 4.135e-07 0.0006431 -1.00 > Residual 1.950e-03 0.0441644 > Number of obs: 39, groups: lot, 6 > > Fixed effects: > Estimate Std. Error df t value Pr(>|t|) > (Intercept) 0.258312 0.018661 4.820000 13.842 4.59e-05 *** > type 0.135021 0.028880 6.802000 4.675 0.00245 ** > months 0.008048 0.001259 11.943000 6.390 3.53e-05 *** > type:months -0.006937 0.002991 28.910000 -2.319 0.02767 * > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Correlation of Fixed Effects: > (Intr) typPPQ months > type -0.646 > months -0.825 0.533 > type:month 0.347 -0.768 -0.421 > > _______________________________________________ > r-sig-mixed-mod...@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.