lme and lmer

Thank you, that explains everything!

Nick

-----Original Message-----
From: Viechtbauer Wolfgang (STAT)
[mailto:wolfgang.viechtbauer at maastrichtuniversity.nl]
Sent: 30 July 2014 09:47
To: Nicholas Burgoyne; r-sig-mixed-models at r-project.org
Subject: RE: lme and lmer

If you want to fit the same models, you should use:

lme <- lme(fixed=Conc ~ Lab, data=coop, random = ~ 1 | Bat,
subset=coop$Spc=="S1")

I am surprised that it even ran with 'random = ~ Bat' (lme in R throws an
error).

Best,
Wolfgang

--
Wolfgang Viechtbauer, Ph.D., Statistician
Department of Psychiatry and Psychology
School for Mental Health and Neuroscience
Faculty of Health, Medicine, and Life Sciences
Maastricht University, P.O. Box 616 (VIJV1)
6200 MD Maastricht, The Netherlands
+31 (43) 388-4170 |
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GAoyqk0MknUKy4q08tNzMxJzs8rKcrP0UvOz2UoNgu1NPI2MTQwsDQ2NGU
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Z

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-
models-bounces at r-project.org] On Behalf Of Nicholas Burgoyne
Sent: Wednesday, July 30, 2014 10:26
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] lme and lmer

Dear all,

I have been kindly redirected here by Ben Bolker, thank you for your
assistance so far!

I apologise for posting what is probably quite a benign query, but for
the life of me I can't find an answer.

I have been asked to explain the differences in the
variance-covariance data in an identical test in Splus (lme) and R (lmer in

lme4).

The input data is standard (from MASS), and identical (I've checked),
the test is as similar as I can make it (code below).

The same output (to a high degree of precision) is obtained for all of
the output values (not just that displaced here), expect for the vcov
data for the Intercept with itself. The effect is therefore that the
standard deviations of the fixed values are quite different!

I am using splus 6.2, R 3.0.2 and lme4 v 1.1-7 (see sessionInfo dump
later on), the contrasts in splus are set to treatment/poly.

Any advice you could give me would be very helpful.

Kind regards,

Nick Burgoyne

#########
#In Spus#
#########

options(contrasts=c("contr.treatment", "contr.poly"))
library(MASS)
coop <- coop
lme <- lme(fixed=Conc ~ Lab, data=coop, random = ~ Bat,

subset=coop$Spc=="S1")

lme

Linear mixed-effects model fit by REML
  Data: coop
  Subset: coop$Spc == "S1"
  Log-restricted-likelihood: 20.27187
  Fixed: Conc ~ Lab
 (Intercept)      LabL2 LabL3 LabL4     LabL5     LabL6
    0.319999 0.08166667  0.04  0.68 0.1233333 0.2033333

Random effects:
Formula:  ~ Bat | 1
Structure: General positive-definite
                  StdDev   Corr
(Intercept) 0.1401167895 (Intr) BatB2
      BatB2 0.0001407246  0.000
      BatB3 0.0003628541  0.000 -0.072
   Residual 0.1029156551

Number of Observations: 36
Number of Groups: 1

lme$varFix

             (Intercept)        LabL2        LabL3        LabL4
LabL5        LabL6
(Intercept)  0.021398003 -0.001765272 -0.001765272 -0.001765272 -
0.001765272 -0.001765272
      LabL2 -0.001765272  0.003530544  0.001765272  0.001765272
0.001765272  0.001765272
      LabL3 -0.001765272  0.001765272  0.003530544  0.001765272
0.001765272  0.001765272
      LabL4 -0.001765272  0.001765272  0.001765272  0.003530544
0.001765272  0.001765272
     LabL5 -0.001765272  0.001765272  0.001765272  0.001765272
0.003530544  0.001765272
      LabL6 -0.001765272  0.001765272  0.001765272  0.001765272
0.001765272  0.003530544

######
#In R#
######

library(lme4)
library(MASS)
coop <- coop
lme <- lmer(formula=Conc ~ Lab + (1|Bat),  data=coop,

subset=coop$Spc=="S1")

lme

Linear mixed model fit by REML ['lmerMod']
Formula: Conc ~ Lab + (1 | Bat)
   Data: coop
Subset: coop$Spc == "S1"
REML criterion at convergence: -40.5438 Random effects:
Groups   Name        Std.Dev.
Bat      (Intercept) 0.0000
 Residual             0.1029
Number of obs: 36, groups:  Bat, 3
Fixed Effects:
(Intercept)        LabL2        LabL3        LabL4        LabL5
LabL6
    0.32000      0.08167      0.04000      0.68000      0.12333
0.20333

vcov(lme)

6 x 6 Matrix of class "dpoMatrix"
             (Intercept)        LabL2        LabL3        LabL4
LabL5
(Intercept)  0.001765278 -0.001765278 -0.001765278 -0.001765278 -
0.001765278
LabL2       -0.001765278  0.003530556  0.001765278  0.001765278
0.001765278
LabL3       -0.001765278  0.001765278  0.003530556  0.001765278
0.001765278
LabL4       -0.001765278  0.001765278  0.001765278  0.003530556
0.001765278
LabL5       -0.001765278  0.001765278  0.001765278  0.001765278
0.003530556
LabL6       -0.001765278  0.001765278  0.001765278  0.001765278
0.001765278
                   LabL6
(Intercept) -0.001765278
LabL2        0.001765278
LabL3        0.001765278
LabL4        0.001765278
LabL5        0.001765278
LabL6        0.003530556

sessionInfo()

R version 3.0.2 (2013-09-25)
Platform: i386-w64-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=English_United Kingdom.1252 [2]

LC_CTYPE=English_United

Kingdom.1252 [3] LC_MONETARY=English_United Kingdom.1252 [4]
LC_NUMERIC=C [5] LC_TIME=English_United Kingdom.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] MASS_7.3-29  lme4_1.1-7   Rcpp_0.11.2  Matrix_1.1-4

loaded via a namespace (and not attached):
[1] grid_3.0.2      lattice_0.20-29 minqa_1.2.3     nlme_3.1-111
[5] nloptr_1.0.0    splines_3.0.2   tools_3.0.2

--
Nicholas Burgoyne
E:nburgoyne at mango-solutions.com	T:+44 (0)1249 705 450
W:www.mango-solutions.com

--

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