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 ________________________________________ EARL Conference, London 15-17 September 2014 - Hurry as tickets for the event are selling fast Mango are delighted to announce the inaugural EARL Conference (Effective Applications of the R Language) For further details please visit www.earl-conference.com or email questions at earl-conference.com ________________________________________
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