[R-sig-ME] [R] different results from lme and lmer function
Pantelis Z. Hadjipantelis
kalakouentin at gmail.com
Wed May 27 03:45:24 CEST 2015
Ben,
I fully accept that the two procedures should converge at the same
solution but as the OP does not give the versions used. Therefore I am
not sure that the fitting is done using the same procedure ("bobyqa" vs.
"Nelder_Mead"). Different optim. algorithms with different initial
values might converge to different local minima. The OP has only 39
subjects so for a model of this size over-fitting will not be unheard
off given the number of parameters.
I am curious though for your later comment: I understand that in the
case of an unstable model you might expect lme4 to be slightly better,
but wouldn't the singular fit in the correlation (-1) suggest that
lmer's fit is sub-optimal?
All best,
Pantelis
On 05/26/2015 05:50 PM, Ben Bolker wrote:
> 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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