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-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.