[R-sig-ME] [R] 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
> (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.>> 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-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 
> ______________________________________________
> R-help at 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.

Confidentiality Statement:
This email message, including any attachments, is for the sole use of
the intended recipient(s) and may contain confidential and privileged
information. Any unauthorized use, disclosure or distribution is
prohibited. If you are not the intended recipient, please contact the
sender by reply email and destroy all copies of the original message. 