R help - Jun 2011 - different results from lme() and lmer()

Hello R-help,
I'm studying an example in the R book.?
The data file is available from the link
below.http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt
Could you explain Why the results from lme() and lmer() are different in the
following case? In other examples, I can get the same results using the two
functions, but not here...?
Thank you.Miya

library(lme4)library(nlme)# object dat contains the data
> summary(lme(root~fertilizer,random=~week|plant,data=dat))Linear
mixed-effects model fit by REML?Data: dat?? ? ? ?AIC ? ? ?BIC ? ?logLik?
171.0236 183.3863 -79.51181
Random effects:?Formula: ~week | plant?Structure: General positive-definite,
Log-Cholesky parametrization? ? ? ? ? ? StdDev ? ?Corr ?(Intercept) 2.8639832
(Intr)week ? ? ? ?0.9369412 -0.999Residual ? ?0.4966308 ? ? ??
Fixed effects: root ~ fertilizer?? ? ? ? ? ? ? ? ? ? ? Value Std.Error DF ?
t-value p-value(Intercept) ? ? ? ?2.799710 0.1438367 48 19.464499 ?
0e+00fertilizercontrol -1.039383 0.2034158 10 -5.109645 ? 5e-04?Correlation:?? ?
? ? ? ? ? ? ? (Intr)fertilizercontrol -0.707
Standardized Within-Group Residuals:? ? ? ?Min ? ? ? ? Q1 ? ? ? ?Med ? ? ? ? Q3
? ? ? ?Max?-1.9928118 -0.6586834 -0.1004301 ?0.6949714 ?2.0225381?
Number of Observations: 60Number of Groups: 12?
> lmer(root~fertilizer+(week|plant),data=dat)Linear mixed model fit by
REML?Formula: root ~ fertilizer + (week | plant)?? ?Data: dat?? ?AIC BIC logLik
deviance REMLdev?174.4 187 -81.21 ? ?159.7 ? 162.4Random effects:?Groups ? Name
? ? ? ?Variance ? Std.Dev. ? Corr ??plant ? ?(Intercept) 4.1416e-18 2.0351e-09 ?
? ??? ? ? ? ? week ? ? ? ?8.7452e-01 9.3516e-01 0.000??Residual ? ? ? ? ? ?
2.2457e-01 4.7389e-01 ? ? ??Number of obs: 60, groups: plant, 12
Fixed effects:? ? ? ? ? ? ? ? ? Estimate Std. Error t value(Intercept) ? ? ?
?-0.1847 ? ? 0.2024 ?-0.913fertilizercontrol ?-0.7612 ? ? 0.2862 ?-2.660
Correlation of Fixed Effects:? ? ? ? ? ? (Intr)frtlzrcntrl -0.707

Dear Miya,

Notice the very strong negative correlation between the random intercept and the
random slope in the lme() model. That is usually an indication of problems (in
this case overfitting). If you drop the random slope, then both models yield the
same parameters.

Plotting the data reviels a much better model specification.

dat <- read.table(file =
"http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt",
header = TRUE)
library(ggplot2)
ggplot(dat, aes(x = week, y = root, group = plant, colour = fertilizer)) +
geom_line()

lmer(root~fertilizer + week +(1|plant),data=dat)

Best regards,

Thierry

PS Use R-sig-mixedmodels for questions on mixed models
> -----Oorspronkelijk bericht-----
> Van: r-help-bounces at r-project.org [mailto:r-help-bounces at
r-project.org]
> Namens Taro Miyagawa
> Verzonden: woensdag 1 juni 2011 7:58
> Aan: r-help at r-project.org
> Onderwerp: [R] different results from lme() and lmer()
> 
> 
> Hello R-help,
> I'm studying an example in the R book.
> The data file is available from the link
> below.http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt
> Could you explain Why the results from lme() and lmer() are different in
the
> following case? In other examples, I can get the same results using the two
> functions, but not here...
> Thank you.Miya
> 
> library(lme4)library(nlme)# object dat contains the data
> > summary(lme(root~fertilizer,random=~week|plant,data=dat))Linear mixed-
> effects model fit by REML?Data: dat?? ? ? ?AIC ? ? ?BIC ? ?logLik? 171.0236
183.3863 -
> 79.51181
> Random effects:?Formula: ~week | plant?Structure: General
positive-definite,
> Log-Cholesky parametrization? ? ? ? ? ? StdDev ? ?Corr ?(Intercept)
2.8639832
> (Intr)week ? ? ? ?0.9369412 -0.999Residual ? ?0.4966308
> Fixed effects: root ~ fertilizer?? ? ? ? ? ? ? ? ? ? ? Value Std.Error DF ?
t-value p-
> value(Intercept) ? ? ? ?2.799710 0.1438367 48 19.464499 ?
0e+00fertilizercontrol -
> 1.039383 0.2034158 10 -5.109645 ? 5e-
> 04?Correlation:?? ? ? ? ? ? ? ? ? (Intr)fertilizercontrol -0.707
> Standardized Within-Group Residuals:? ? ? ?Min ? ? ? ? Q1 ? ? ? ?Med ? ? ?
? Q3 ? ? ? ?Max?-
> 1.9928118 -0.6586834 -0.1004301 ?0.6949714 ?2.0225381
> Number of Observations: 60Number of Groups: 12
> 
> > lmer(root~fertilizer+(week|plant),data=dat)Linear mixed model fit by
> REML?Formula: root ~ fertilizer + (week | plant)?? ?Data: dat?? ?AIC BIC
logLik
> deviance REMLdev?174.4 187 -81.21 ? ?159.7 ? 162.4Random
> effects:?Groups ? Name ? ? ? ?Variance ? Std.Dev. ? Corr ??plant ?
?(Intercept)
> 4.1416e-18 2.0351e-09 ? ? ??? ? ? ? ? week ? ? ? ?8.7452e-01 9.3516e-01
> 0.000??Residual ? ? ? ? ? ? 2.2457e-01 4.7389e-01 ? ? ??Number of obs: 60,
groups:
> plant, 12
> Fixed effects:? ? ? ? ? ? ? ? ? Estimate Std. Error t value(Intercept) ? ?
? ?-
> 0.1847 ? ? 0.2024 ?-0.913fertilizercontrol ?-0.7612 ? ? 0.2862 ?-2.660
> Correlation of Fixed Effects:? ? ? ? ? ? (Intr)frtlzrcntrl -0.707
> 
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