[R-sig-ME] [R] different results from lme and lmer function
Ben Bolker
bbolker at gmail.com
Wed May 27 02:50:23 CEST 2015
I agree that the difference is trivially small/practically
unimportant. The point here is that -- just for those of us who are
interested in the details of the methods -- lme4 and lme are fitting
*exactly* the same model, by similar methods, so in general they
should converge to the same answer (to a somewhat closer tolerance
than this). Generally when they don't it's because the model is
slightly unstable, and I have generally found that lme4 does slightly
better (but I wouldn't rule out the opposite case).
cheers
Ben Bolker
On Tue, May 26, 2015 at 8:47 PM, John Sorkin
<jsorkin at grecc.umaryland.edu> wrote:
> 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
> Geriatric Medicine
>
> Baltimore VA Medical Center
>
> 10 North Greene Street
>
> GRECC (BT/18/GR)
>
> Baltimore, MD 21201-1524
>
> (Phone) 410-605-7119
>
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
>
>
> 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 0.3615 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
>
>
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