[R] [R-sig-ME] different results from lme and lmer function

Wed May 27 02:47:12 CEST 2015

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
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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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> 
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