different results from lme and lmer function

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

John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and

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,

+ 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,

summary(mod_lmer)

Linear mixed model fit by REML t-tests use Satterthwaite

 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