different results from lme and lmer function
Ben, I doubt the very small difference in log likelihood gives much, if any information about which model is a better fit. Even if we overlook the limited precision of the estimate of the REML criterion, the difference is so small as to me of minimal importance. John
John David Sorkin M.D., Ph.D. Professor of Medicine Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology and
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On May 26, 2015, at 8:03 PM, Ben Bolker <bbolker at gmail.com> wrote: 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.hlx at 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.>> 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
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