[R] [R-sig-ME] different results from lme and lmer function
Ben Bolker
bbolker at gmail.com
Wed May 27 02:03:10 CEST 2015
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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