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

[Ben Bolker]

  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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